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From: Patrick W. <pat...@in...> - 2018-02-15 23:30:27
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Dear DL-Learner users, we want to inform you that we moved this users discussion mailing list to FreeLists [0] and would like to ask you to send your future questions to dl-...@fr... . In case you want to update your subscription for this mailing list please use the list's info page [0]. Additional information can be found on the FreeLists FAQ page [1]. In case you have further questions regarding this mailing list, please contact me [2]. We hope for fruitful discussions and your feedback regarding the DL-Learner framework. Best regards, Patrick on behalf of the DL-Learner developer team [0] https://www.freelists.org/list/dl-learner-discussion [1] https://www.freelists.org/wiki/the_faq [2] mailto:pat...@in... -- Patrick Westphal Department of Computer Science, University of Leipzig Research Group: http://aksw.org/ Phone: +49-341-9732305 |
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From: Igor K. <igo...@st...> - 2018-02-08 16:20:53
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Dear all, I am using Dl-Learner to analyse our data with PosNegStandard learning problem, particularly I need exampleLoaderHelper to load my positive and negative examples. It works nice with the release distribution (1.3.0). However, with the latest development version (1.3.1), the ComponentInitException: 'No positive examples have been set' is thrown. It seems to me that the problem is due to the premature initialization of the ExampleLoader component. When I removed the setting of the 'initialized' flag, line 81 in ExampleLoader.java, DL-learner works with my data correctly. But I'm not sure if this simple fix is correct and has no other side effects. best Regards, Igor Kossaczky Institute of Computer Science and Mathematics Slovak University of Technology Ilkovicova 3, 812 19 Bratislava, Slovakia |
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From: mnolte <mn...@un...> - 2018-01-08 21:56:14
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Dear all, I have a question regarding ignored concepts and properties that can be set via the configuration /alg.ignoredConcepts/[1] etc: At which point in the algorithm will they be ignored? Are they simply discarded during output, or does the refinement operator no longer create any nodes containing them? In the source code I found that at least the ignored concepts are treated like unfulfillable concepts (org.dllearner.core.AbstractCELA.initClassHierarchy()). However, I am not quite clear what this implies for a theoretically point of view. I would be glad if one of you could help me with this. Kind regards, Robin Nolte Links: ------ [1] https://cdn.rawgit.com/AKSW/DL-Learner/master/interfaces/doc/configOptions.html#org.dllearner.algorithms.celoe.CELOE Universitaet Bremen |
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From: Jens L. <jen...@cs...> - 2017-12-15 13:20:07
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Dear all, The Smart Data Analytics group [1] is happy to announce SANSA 0.3 - the third release of the Scalable Semantic Analytics Stack. SANSA employs distributed computing via Apache Spark and Flink in order to allow scalable machine learning, inference and querying capabilities for large knowledge graphs. Website: http://sansa-stack.net GitHub: https://github.com/SANSA-Stack Download: http://sansa-stack.net/downloads-usage/ ChangeLog: https://github.com/SANSA-Stack/SANSA-Stack/releases You can find the FAQ and usage examples at http://sansa-stack.net/faq/. The following features are currently supported by SANSA: * Reading and writing RDF files in N-Triples, Turtle, RDF/XML, N-Quad format * Reading OWL files in various standard formats * Support for multiple data partitioning techniques * SPARQL querying via Sparqlify (with some known limitations until the next Spark 2.3.* release) * SPARQL querying via conversion to Gremlin path traversals (experimental) * RDFS, RDFS Simple, OWL-Horst (all in beta status), EL (experimental) forward chaining inference * Automatic inference plan creation (experimental) * RDF graph clustering with different algorithms * Rule mining from RDF graphs based AMIE+ * Terminological decision trees (experimental) * Anomaly detection (beta) * Distributed knowledge graph embedding approaches: TransE (beta), DistMult (beta), several further algorithms planned Deployment and getting started: * There are template projects for SBT and Maven for Apache Spark as well as for Apache Flink available [2] to get started. * The SANSA jar files are in Maven Central i.e. in most IDEs you can just search for “sansa” to include the dependencies in Maven projects. * There is example code for various tasks available [3]. * We provide interactive notebooks for running and testing code [4] via Docker. We want to thank everyone who helped to create this release, in particular the projects Big Data Europe [5], HOBBIT [6], SAKE [7], Big Data Ocean [8], SLIPO [9], QROWD [10] and BETTER. View this announcement on Twitter and the SDA blog: http://sda.cs.uni-bonn.de/sansa-0-3/ https://twitter.com/SANSA_Stack/status/941643408300441600 Kind regards, The SANSA Development Team (http://sansa-stack.net/community/#Contributors) [1] http://sda.tech [2] http://sansa-stack.net/downloads-usage/ [3] https://github.com/SANSA-Stack/SANSA-Examples [4] https://github.com/SANSA-Stack/SANSA-Notebooks [5] http://www.big-data-europe.eu [6] https://project-hobbit.eu [7] https://www.sake-projekt.de/en/start/ [8] http://www.bigdataocean.eu [9] http://slipo.eu [10] http://qrowd-project.eu -- Prof. Dr. Jens Lehmann http://jens-lehmann.org http://sda.tech Computer Science Institute Enterprise Information Systems University of Bonn Fraunhofer IAIS http://www.cs.uni-bonn.de http://www.iais.fraunhofer.de le...@un... jen...@ia... |
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From: Jens L. <le...@in...> - 2017-12-15 12:49:18
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Dear all, The Smart Data Analytics group [1] is happy to announce SANSA 0.3 - the third release of the Scalable Semantic Analytics Stack. SANSA employs distributed computing via Apache Spark and Flink in order to allow scalable machine learning, inference and querying capabilities for large knowledge graphs. Website: http://sansa-stack.net GitHub: https://github.com/SANSA-Stack Download: http://sansa-stack.net/downloads-usage/ ChangeLog: https://github.com/SANSA-Stack/SANSA-Stack/releases You can find the FAQ and usage examples at http://sansa-stack.net/faq/. The following features are currently supported by SANSA: * Reading and writing RDF files in N-Triples, Turtle, RDF/XML, N-Quad format * Reading OWL files in various standard formats * Support for multiple data partitioning techniques * SPARQL querying via Sparqlify (with some known limitations until the next Spark 2.3.* release) * SPARQL querying via conversion to Gremlin path traversals (experimental) * RDFS, RDFS Simple, OWL-Horst (all in beta status), EL (experimental) forward chaining inference * Automatic inference plan creation (experimental) * RDF graph clustering with different algorithms * Rule mining from RDF graphs based AMIE+ * Terminological decision trees (experimental) * Anomaly detection (beta) * Distributed knowledge graph embedding approaches: TransE (beta), DistMult (beta), several further algorithms planned Deployment and getting started: * There are template projects for SBT and Maven for Apache Spark as well as for Apache Flink available [2] to get started. * The SANSA jar files are in Maven Central i.e. in most IDEs you can just search for “sansa” to include the dependencies in Maven projects. * There is example code for various tasks available [3]. * We provide interactive notebooks for running and testing code [4] via Docker. We want to thank everyone who helped to create this release, in particular the projects Big Data Europe [5], HOBBIT [6], SAKE [7], Big Data Ocean [8], SLIPO [9], QROWD [10] and BETTER. View this announcement on Twitter and the SDA blog: http://sda.cs.uni-bonn.de/sansa-0-3/ https://twitter.com/SANSA_Stack/status/941643408300441600 Kind regards, The SANSA Development Team (http://sansa-stack.net/community/#Contributors) [1] http://sda.tech [2] http://sansa-stack.net/downloads-usage/ [3] https://github.com/SANSA-Stack/SANSA-Examples [4] https://github.com/SANSA-Stack/SANSA-Notebooks [5] http://www.big-data-europe.eu [6] https://project-hobbit.eu [7] https://www.sake-projekt.de/en/start/ [8] http://www.bigdataocean.eu [9] http://slipo.eu [10] http://qrowd-project.eu -- Prof. Dr. Jens Lehmann http://jens-lehmann.org http://sda.tech Computer Science Institute Enterprise Information Systems University of Bonn Fraunhofer IAIS http://www.cs.uni-bonn.de http://www.iais.fraunhofer.de le...@un... jen...@ia... -- Dr. Jens Lehmann Machine Learning and Ontology Engineering Group Lead Department of Computer Science, University of Leipzig Homepage: http://www.jens-lehmann.org Projects: http://geoknow.eu, http://www.big-data-europe.eu GPG Key: http://jens-lehmann.org/jens_lehmann.asc |
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From: Simon B. <sb...@in...> - 2017-10-05 07:19:27
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There is no built-in option to remove duplicate results. If CELOE finds enough good results the duplicate ones should naturally disappear, otherwise you would have to post process the result list. By the way your screenshot was not attached, so I'm making a guess. Take care, On Thu, 2017-10-05 at 06:23 +0000, Sarker, Md Kamruzzaman wrote: > Hi Simon, > > > Thank you very much for your help. > > > I am trying to fix the concurrentModification error. > > > Another thing, sometimes I found duplicate result for CELOE alg. > > I am attaching screenshot and conf files here. > > [cid:3429310e-61d5-4b32-a34d-c7e7ff828b91] > > > > Here result 5, 6 and 7 the lines are same. > > I am also attaching the conf file and ontology file > > > Is there any option to remove duplicate answer? > > > With Regards, > > ------------- > > Md Kamruzzaman Sarker > > PhD Student > > Dept. of Computer Science and Engineering, Wright State University, Dayton, OH > > http://dase.cs.wright.edu/people/kamruzzaman-sarker > > ________________________________ > From: Simon Bin <sb...@in...> > Sent: Wednesday, October 4, 2017 10:59:59 AM > To: Sarker, Md Kamruzzaman; dl-...@li... > Subject: Re: [DL-Learner discussion] Stop running DL-Learner (CELOE/class expression learning algorithm) after generating n result > > Hello Sarker, > > thanks for your continued interest in DL-Learner. Currently, the > parallel approaches are still under development. If you want to > contribute to DL-Learner, feel free to send in a Pull Request to fix > the issue you mentioned. > > The CELOE algorithm is an anytime algorithm. That means, the top > results may (or may not) improve if you let it run for a longer time. > There is no easy way to tell how long you need to run it before you > will see the top results that you want to see. > > You can turn off the time limit by setting maxExecutionTimeInSeconds to > 0 and you can then decide to stop the algorithm either by limiting the > number of expression tests (maxClassExpressionTests) or by using the > noise criterion. > > You can also try different reasoner components to see if it can speed > up the process. > > Take care, > Simon > AKSW Group > Universität Leipzig > https://urldefense.proofpoint.com/v2/url?u=http-3A__aksw.org_SimonBin&d=DwIDaQ&c=3buyMx9JlH1z22L_G5pM28wz_Ru6WjhVHwo-vpeS0Gk&r=gOKJ7GGIYSN5ZikR0Wci79-a9BTFdST29ynNAKTmkas&m=74tSWWJqQwpDHivJWi0k9ek9bZaa7WGueuVs7a9L04c&s=yhWa3n2tWUPYc0XBf8Y_AtVFFqTCeDMeyfnUsjlgxMk&e= > > > On Tue, 2017-10-03 at 15:12 +0000, Sarker, Md Kamruzzaman wrote: > > Dear Concern, > > > > With due respect, I am using Dl-Learner for class expression generation from positive and negative instances with respect to background ontology. > > > > > > Particularly I am using CELOE algorithm. > > > > > > I have some questions about running time: > > > > > > The configurable parameters of CELOE algorithm (https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_SmartDataAnalytics_DL-2DLearner_blob_develop_interfaces_doc_configOptions.txt&d=DwIDaQ&c=3buyMx9JlH1z22L_G5pM28wz_Ru6WjhVHwo-vpeS0Gk&r=gOKJ7GGIYSN5ZikR0Wci79-a9BTFdST29ynNAKTmkas&m=74tSWWJqQwpDHivJWi0k9ek9bZaa7WGueuVs7a9L04c&s=YQNaODRaeOuRzSrmUGiKM0-xo-XNNZ3PsXBLne-3lKQ&e= ) shows how to stop it after running maxExecutionTimeInSeconds time. > > > > [https://urldefense.proofpoint.com/v2/url?u=https-3A__avatars3.githubusercontent.com_u_15215821-3Fv-3D4-26s-3D400&d=DwIDaQ&c=3buyMx9JlH1z22L_G5pM28wz_Ru6WjhVHwo-vpeS0Gk&r=gOKJ7GGIYSN5ZikR0Wci79-a9BTFdST29ynNAKTmkas&m=74tSWWJqQwpDHivJWi0k9ek9bZaa7WGueuVs7a9L04c&s=9y9dVNID77pY6jAiReJVEjxXM_kPr7deXEtXj9y3gVc&e= ]<https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_SmartDataAnalytics_DL-2DLearner_blob_develop_interfaces_doc_configOptions.txt&d=DwIDaQ&c=3buyMx9JlH1z22L_G5pM28wz_Ru6WjhVHwo-vpeS0Gk&r=gOKJ7GGIYSN5ZikR0Wci79-a9BTFdST29ynNAKTmkas&m=74tSWWJqQwpDHivJWi0k9ek9bZaa7WGueuVs7a9L04c&s=YQNaODRaeOuRzSrmUGiKM0-xo-XNNZ3PsXBLne-3lKQ&e= >; > > > > SmartDataAnalytics/DL-Learner<https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_SmartDataAnalytics_DL-2DLearner_blob_develop_interfaces_doc_configOptions.txt&d=DwIDaQ&c=3buyMx9JlH1z22L_G5pM28wz_Ru6WjhVHwo-vpeS0Gk&r=gOKJ7GGIYSN5ZikR0Wci79-a9BTFdST29ynNAKTmkas&m=74tSWWJqQwpDHivJWi0k9ek9bZaa7WGueuVs7a9L04c&s=YQNaODRaeOuRzSrmUGiKM0-xo-XNNZ3PsXBLne-3lKQ&e= > > > github.com > > DL-Learner - A tool for supervised Machine Learning in OWL and Description Logics > > > > > > > > But I am particularly interested to get top n results and then stop the program. > > > > > > There are another parameter maxNrOfResults but if I run the algorithm for couple of minutes it does not generate the n result and stops based on maxExecutionTimeInSeconds. > > > > > > 1. Is there any way to produce top n result and stop the algorithm disregards of maxExecutionTimeInSeconds time? > > > > 2. How to speed up the algorithm? I know reasoning algorithms are in exponential complexity but is there any option to use multiple threads or parallelization or other configurable parameters which can speed up the execution time? > > > > > > DL-Learner is an excellent tool. I appreciate the researcher's behind this tool. > > > > > > > > With Regards, > > > > ------------- > > > > Md Kamruzzaman Sarker > > > > PhD Student > > > > Dept. of Computer Science and Engineering, Wright State University, Dayton, OH > > > > http://dase.cs.wright.edu/people/kamruzzaman-sarker > > ------------------------------------------------------------------------------ > > Check out the vibrant tech community on one of the world's most > > engaging tech sites, Slashdot.org! https://urldefense.proofpoint.com/v2/url?u=http-3A__sdm.link_slashdot&d=DwIDaQ&c=3buyMx9JlH1z22L_G5pM28wz_Ru6WjhVHwo-vpeS0Gk&r=gOKJ7GGIYSN5ZikR0Wci79-a9BTFdST29ynNAKTmkas&m=74tSWWJqQwpDHivJWi0k9ek9bZaa7WGueuVs7a9L04c&s=l-FuFPftm4lfqeKFJiM1Mtfbv4EvUXSzgGNx_9xvVZk&e= > > _______________________________________________ > > dl-learner-discussion mailing list > > dl-...@li... > > https://urldefense.proofpoint.com/v2/url?u=https-3A__lists.sourceforge.net_lists_listinfo_dl-2Dlearner-2Ddiscussion&d=DwIDaQ&c=3buyMx9JlH1z22L_G5pM28wz_Ru6WjhVHwo-vpeS0Gk&r=gOKJ7GGIYSN5ZikR0Wci79-a9BTFdST29ynNAKTmkas&m=74tSWWJqQwpDHivJWi0k9ek9bZaa7WGueuVs7a9L04c&s=RIDZkryT1VPV1tB8homiZBAGJovA-1RtybaUpVj7grs&e= > > |
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From: Sarker, Md K. <sar...@wr...> - 2017-10-05 06:39:25
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Hi Simon, Thank you very much for your help. I am trying to fix the concurrentModification error. Another thing, sometimes I found duplicate result for CELOE alg. I am attaching screenshot and conf files here. [cid:3429310e-61d5-4b32-a34d-c7e7ff828b91] Here result 5, 6 and 7 the lines are same. I am also attaching the conf file and ontology file Is there any option to remove duplicate answer? With Regards, ------------- Md Kamruzzaman Sarker PhD Student Dept. of Computer Science and Engineering, Wright State University, Dayton, OH http://dase.cs.wright.edu/people/kamruzzaman-sarker ________________________________ From: Simon Bin <sb...@in...> Sent: Wednesday, October 4, 2017 10:59:59 AM To: Sarker, Md Kamruzzaman; dl-...@li... Subject: Re: [DL-Learner discussion] Stop running DL-Learner (CELOE/class expression learning algorithm) after generating n result Hello Sarker, thanks for your continued interest in DL-Learner. Currently, the parallel approaches are still under development. If you want to contribute to DL-Learner, feel free to send in a Pull Request to fix the issue you mentioned. The CELOE algorithm is an anytime algorithm. That means, the top results may (or may not) improve if you let it run for a longer time. There is no easy way to tell how long you need to run it before you will see the top results that you want to see. You can turn off the time limit by setting maxExecutionTimeInSeconds to 0 and you can then decide to stop the algorithm either by limiting the number of expression tests (maxClassExpressionTests) or by using the noise criterion. You can also try different reasoner components to see if it can speed up the process. Take care, Simon AKSW Group Universität Leipzig https://urldefense.proofpoint.com/v2/url?u=http-3A__aksw.org_SimonBin&d=DwIDaQ&c=3buyMx9JlH1z22L_G5pM28wz_Ru6WjhVHwo-vpeS0Gk&r=gOKJ7GGIYSN5ZikR0Wci79-a9BTFdST29ynNAKTmkas&m=74tSWWJqQwpDHivJWi0k9ek9bZaa7WGueuVs7a9L04c&s=yhWa3n2tWUPYc0XBf8Y_AtVFFqTCeDMeyfnUsjlgxMk&e= On Tue, 2017-10-03 at 15:12 +0000, Sarker, Md Kamruzzaman wrote: > Dear Concern, > > With due respect, I am using Dl-Learner for class expression generation from positive and negative instances with respect to background ontology. > > > Particularly I am using CELOE algorithm. > > > I have some questions about running time: > > > The configurable parameters of CELOE algorithm (https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_SmartDataAnalytics_DL-2DLearner_blob_develop_interfaces_doc_configOptions.txt&d=DwIDaQ&c=3buyMx9JlH1z22L_G5pM28wz_Ru6WjhVHwo-vpeS0Gk&r=gOKJ7GGIYSN5ZikR0Wci79-a9BTFdST29ynNAKTmkas&m=74tSWWJqQwpDHivJWi0k9ek9bZaa7WGueuVs7a9L04c&s=YQNaODRaeOuRzSrmUGiKM0-xo-XNNZ3PsXBLne-3lKQ&e= ) shows how to stop it after running maxExecutionTimeInSeconds time. > > [https://urldefense.proofpoint.com/v2/url?u=https-3A__avatars3.githubusercontent.com_u_15215821-3Fv-3D4-26s-3D400&d=DwIDaQ&c=3buyMx9JlH1z22L_G5pM28wz_Ru6WjhVHwo-vpeS0Gk&r=gOKJ7GGIYSN5ZikR0Wci79-a9BTFdST29ynNAKTmkas&m=74tSWWJqQwpDHivJWi0k9ek9bZaa7WGueuVs7a9L04c&s=9y9dVNID77pY6jAiReJVEjxXM_kPr7deXEtXj9y3gVc&e= ]<https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_SmartDataAnalytics_DL-2DLearner_blob_develop_interfaces_doc_configOptions.txt&d=DwIDaQ&c=3buyMx9JlH1z22L_G5pM28wz_Ru6WjhVHwo-vpeS0Gk&r=gOKJ7GGIYSN5ZikR0Wci79-a9BTFdST29ynNAKTmkas&m=74tSWWJqQwpDHivJWi0k9ek9bZaa7WGueuVs7a9L04c&s=YQNaODRaeOuRzSrmUGiKM0-xo-XNNZ3PsXBLne-3lKQ&e= >; > > SmartDataAnalytics/DL-Learner<https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_SmartDataAnalytics_DL-2DLearner_blob_develop_interfaces_doc_configOptions.txt&d=DwIDaQ&c=3buyMx9JlH1z22L_G5pM28wz_Ru6WjhVHwo-vpeS0Gk&r=gOKJ7GGIYSN5ZikR0Wci79-a9BTFdST29ynNAKTmkas&m=74tSWWJqQwpDHivJWi0k9ek9bZaa7WGueuVs7a9L04c&s=YQNaODRaeOuRzSrmUGiKM0-xo-XNNZ3PsXBLne-3lKQ&e= > > github.com > DL-Learner - A tool for supervised Machine Learning in OWL and Description Logics > > > > But I am particularly interested to get top n results and then stop the program. > > > There are another parameter maxNrOfResults but if I run the algorithm for couple of minutes it does not generate the n result and stops based on maxExecutionTimeInSeconds. > > > 1. Is there any way to produce top n result and stop the algorithm disregards of maxExecutionTimeInSeconds time? > > 2. How to speed up the algorithm? I know reasoning algorithms are in exponential complexity but is there any option to use multiple threads or parallelization or other configurable parameters which can speed up the execution time? > > > DL-Learner is an excellent tool. I appreciate the researcher's behind this tool. > > > > With Regards, > > ------------- > > Md Kamruzzaman Sarker > > PhD Student > > Dept. of Computer Science and Engineering, Wright State University, Dayton, OH > > http://dase.cs.wright.edu/people/kamruzzaman-sarker > ------------------------------------------------------------------------------ > Check out the vibrant tech community on one of the world's most > engaging tech sites, Slashdot.org! https://urldefense.proofpoint.com/v2/url?u=http-3A__sdm.link_slashdot&d=DwIDaQ&c=3buyMx9JlH1z22L_G5pM28wz_Ru6WjhVHwo-vpeS0Gk&r=gOKJ7GGIYSN5ZikR0Wci79-a9BTFdST29ynNAKTmkas&m=74tSWWJqQwpDHivJWi0k9ek9bZaa7WGueuVs7a9L04c&s=l-FuFPftm4lfqeKFJiM1Mtfbv4EvUXSzgGNx_9xvVZk&e= > _______________________________________________ > dl-learner-discussion mailing list > dl-...@li... > https://urldefense.proofpoint.com/v2/url?u=https-3A__lists.sourceforge.net_lists_listinfo_dl-2Dlearner-2Ddiscussion&d=DwIDaQ&c=3buyMx9JlH1z22L_G5pM28wz_Ru6WjhVHwo-vpeS0Gk&r=gOKJ7GGIYSN5ZikR0Wci79-a9BTFdST29ynNAKTmkas&m=74tSWWJqQwpDHivJWi0k9ek9bZaa7WGueuVs7a9L04c&s=RIDZkryT1VPV1tB8homiZBAGJovA-1RtybaUpVj7grs&e= |
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From: Simon B. <sb...@in...> - 2017-10-04 15:00:09
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Hello Sarker, thanks for your continued interest in DL-Learner. Currently, the parallel approaches are still under development. If you want to contribute to DL-Learner, feel free to send in a Pull Request to fix the issue you mentioned. The CELOE algorithm is an anytime algorithm. That means, the top results may (or may not) improve if you let it run for a longer time. There is no easy way to tell how long you need to run it before you will see the top results that you want to see. You can turn off the time limit by setting maxExecutionTimeInSeconds to 0 and you can then decide to stop the algorithm either by limiting the number of expression tests (maxClassExpressionTests) or by using the noise criterion. You can also try different reasoner components to see if it can speed up the process. Take care, Simon AKSW Group Universität Leipzig http://aksw.org/SimonBin On Tue, 2017-10-03 at 15:12 +0000, Sarker, Md Kamruzzaman wrote: > Dear Concern, > > With due respect, I am using Dl-Learner for class expression generation from positive and negative instances with respect to background ontology. > > > Particularly I am using CELOE algorithm. > > > I have some questions about running time: > > > The configurable parameters of CELOE algorithm (https://github.com/SmartDataAnalytics/DL-Learner/blob/develop/interfaces/doc/configOptions.txt) shows how to stop it after running maxExecutionTimeInSeconds time. > > [https://avatars3.githubusercontent.com/u/15215821?v=4&s=400]<https://github.com/SmartDataAnalytics/DL-Learner/blob/develop/interfaces/doc/configOptions.txt>; > > SmartDataAnalytics/DL-Learner<https://github.com/SmartDataAnalytics/DL-Learner/blob/develop/interfaces/doc/configOptions.txt> > github.com > DL-Learner - A tool for supervised Machine Learning in OWL and Description Logics > > > > But I am particularly interested to get top n results and then stop the program. > > > There are another parameter maxNrOfResults but if I run the algorithm for couple of minutes it does not generate the n result and stops based on maxExecutionTimeInSeconds. > > > 1. Is there any way to produce top n result and stop the algorithm disregards of maxExecutionTimeInSeconds time? > > 2. How to speed up the algorithm? I know reasoning algorithms are in exponential complexity but is there any option to use multiple threads or parallelization or other configurable parameters which can speed up the execution time? > > > DL-Learner is an excellent tool. I appreciate the researcher's behind this tool. > > > > With Regards, > > ------------- > > Md Kamruzzaman Sarker > > PhD Student > > Dept. of Computer Science and Engineering, Wright State University, Dayton, OH > > http://dase.cs.wright.edu/people/kamruzzaman-sarker > ------------------------------------------------------------------------------ > Check out the vibrant tech community on one of the world's most > engaging tech sites, Slashdot.org! http://sdm.link/slashdot > _______________________________________________ > dl-learner-discussion mailing list > dl-...@li... > https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion |
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From: Sarker, Md K. <sar...@wr...> - 2017-10-03 15:12:46
|
Dear Concern, With due respect, I am using Dl-Learner for class expression generation from positive and negative instances with respect to background ontology. Particularly I am using CELOE algorithm. I have some questions about running time: The configurable parameters of CELOE algorithm (https://github.com/SmartDataAnalytics/DL-Learner/blob/develop/interfaces/doc/configOptions.txt) shows how to stop it after running maxExecutionTimeInSeconds time. [https://avatars3.githubusercontent.com/u/15215821?v=4&s=400]<https://github.com/SmartDataAnalytics/DL-Learner/blob/develop/interfaces/doc/configOptions.txt> SmartDataAnalytics/DL-Learner<https://github.com/SmartDataAnalytics/DL-Learner/blob/develop/interfaces/doc/configOptions.txt> github.com DL-Learner - A tool for supervised Machine Learning in OWL and Description Logics But I am particularly interested to get top n results and then stop the program. There are another parameter maxNrOfResults but if I run the algorithm for couple of minutes it does not generate the n result and stops based on maxExecutionTimeInSeconds. 1. Is there any way to produce top n result and stop the algorithm disregards of maxExecutionTimeInSeconds time? 2. How to speed up the algorithm? I know reasoning algorithms are in exponential complexity but is there any option to use multiple threads or parallelization or other configurable parameters which can speed up the execution time? DL-Learner is an excellent tool. I appreciate the researcher's behind this tool. With Regards, ------------- Md Kamruzzaman Sarker PhD Student Dept. of Computer Science and Engineering, Wright State University, Dayton, OH http://dase.cs.wright.edu/people/kamruzzaman-sarker |
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From: Sarker, Md K. <sar...@wr...> - 2017-09-26 03:45:16
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Dear Concern, With due respect I am using DL-Learner. Specifically, I am trying to run PCELOE (Parallel version of CELOE) algorithm but it gives java concurrent modification error. Configuration: I have set mathreads to 8 using. ((PCELOE) la).setNrOfThreads(8); I am attaching the log here. A snippet of the log is: Caused by: java.util.ConcurrentModificationException at java.util.TreeMap$NavigableSubMap$SubMapIterator.prevEntry(TreeMap.java:1714) at java.util.TreeMap$NavigableSubMap$DescendingSubMapKeyIterator.next(TreeMap.java:1818) at org.dllearner.algorithms.celoe.PCELOE.getNextNodeToExpand(PCELOE.java:503) at org.dllearner.algorithms.celoe.PCELOE.access$200(PCELOE.java:65) at org.dllearner.algorithms.celoe.PCELOE$PCELOEWorker.run(PCELOE.java:1138) at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511) at java.util.concurrent.FutureTask.run(FutureTask.java:266) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748) Would you please help? With Regards, ------------- Md Kamruzzaman Sarker PhD Student Dept. of Computer Science and Engineering, Wright State University, Dayton, OH http://dase.cs.wright.edu/people/kamruzzaman-sarker |
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From: Jens L. <le...@in...> - 2017-06-13 15:53:07
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Dear all, The Smart Data Analytics group [1] is happy to announce SANSA 0.2 - the second release of the Scalable Semantic Analytics Stack. SANSA employs distributed computing for semantic technologies in order to allow scalable machine learning, inference and querying capabilities for large knowledge graphs. Website: http://sansa-stack.net GitHub: https://github.com/SANSA-Stack Download: http://sansa-stack.net/downloads-usage/ ChangeLog: https://github.com/SANSA-Stack/SANSA-Stack/releases You can find the FAQ and usage examples at http://sansa-stack.net/faq/. The following features are currently supported by SANSA: * Reading and writing RDF files in N-Triples format * Reading OWL files in various standard formats * Querying and partitioning based on Sparqlify * RDFS, RDFS Simple, OWL-Horst forward chaining inference * RDF graph clustering with different algorithms * Rule mining from RDF graphs Deployment and getting started: * There are template projects for SBT and Maven for Apache Spark as well as for Apache Flink available [2] to get started. * The SANSA jar files are in Maven Central i.e. in most IDEs you can just search for “sansa” to include the dependencies in Maven projects. * There is example code for various tasks available [3]. * We provide interactive notebooks for running and testing code [4] via Docker. We want to thank everyone who helped to create this release, in particular the projects Big Data Europe [5], HOBBIT [6], SAKE [7] and Big Data Ocean [8]. View this announcement on Twitter and the SDA blog: http://sda.cs.uni-bonn.de/sansa-0-2/ https://twitter.com/SANSA_Stack/status/874652172058845184 Kind regards, The SANSA Development Team (http://sansa-stack.net/community/#Contributors) [1] http://sda.tech [2] http://sansa-stack.net/downloads-usage/ [3] https://github.com/SANSA-Stack/SANSA-Examples [4] https://github.com/SANSA-Stack/SANSA-Notebooks [5] http://www.big-data-europe.eu [6] https://project-hobbit.eu [7] https://www.sake-projekt.de/en/start/ [8] http://www.bigdataocean.eu |
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From: Faranak S. <f.s...@qm...> - 2017-06-06 07:27:40
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Hi, In provided CrossValidation method in DL learner, mostly Only PosNeg and PosOnly learning problems are supported. Can you please direct me to Cross-validation method in Dl learner that can support Class Learning problem? Best, Fara |
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From: Lorenz B. <spo...@st...> - 2017-05-24 07:09:31
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Without seeing the data + the used conf files for the it's almost impossible to see what's going on. At least for the ClassLearningProblem it should not return the target class itself. For others, indeed it will return the best solution regarding coverage - this might be also a simple class A if this is good enough to distinguish pos. from neg. examples. But as I said, we need data + learning setup to given you a better explanation. Cheers, Lorenz > > Hi, > > Thanks a lot for getting back to me. > > > When I'm using the Cross validation What I get as a result is the > Class A b= > y itself. > > > I did test it with both PosOnly learning problem and also PosNegative > learn= > ing problem. > > > considering the three different learning problem(lp1, lp2, lp3); > > > > * ClassLearningProblem lp1 =3D new ClassLearningProblem(rc); > * PosOnlyLP lp2 =3D new PosOnlyLP(rc, posExamples); > * PosNegLPStandard lp3 =3D new PosNegLPStandard(rc, > posExamples, negExamples); > > > Is that correct to conclude that the solution you get based on the lp1 > is more precise in comparison with lp2 and lp3? (maybe because the > number of th= > e negative instances are always more?) > > > I did work with the provided CrossValidation in DL code and there Only > PosNeg and PosOnly learning problems are supported, however all I get > as a solutionis either; A and (not B ) or A for each Target class. I > can not get anything to cover object relations. While if I ignore > cross-validation and only use lp1, I can have results including object > relations as well. > > > Best, > Faranak > > -- > > > > ------------------------------------------------------------------------------ > Check out the vibrant tech community on one of the world's most > engaging tech sites, Slashdot.org! http://sdm.link/slashdot > > > _______________________________________________ > dl-learner-discussion mailing list > dl-...@li... > https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion |
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From: Faranak S. <f.s...@qm...> - 2017-05-22 11:26:28
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Hi, Thanks a lot for getting back to me. When I'm using the Cross validation What I get as a result is the Class A b= y itself. I did test it with both PosOnly learning problem and also PosNegative learn= ing problem. considering the three different learning problem(lp1, lp2, lp3); * ClassLearningProblem lp1 =3D new ClassLearningProblem(rc); * PosOnlyLP lp2 =3D new PosOnlyLP(rc, posExamples); * PosNegLPStandard lp3 =3D new PosNegLPStandard(rc, posExamples, negExamples); Is that correct to conclude that the solution you get based on the lp1 is more precise in comparison with lp2 and lp3? (maybe because the number of th= e negative instances are always more?) I did work with the provided CrossValidation in DL code and there Only PosNeg and PosOnly learning problems are supported, however all I get as a solution is either; A and (not B ) or A for each Target class. I can not get anything to cover object relations. While if I ignore cross-validation and only use lp1, I can have results including object relations as well. Best, Faranak -- |
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From: Fabrizio R. <fab...@un...> - 2017-05-18 07:17:24
|
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
AI*IA Outgoing Mobility Grants 2017
Call for research visits
Deadline for applications: June 20th, 2017
https://sites.google.com/a/aixia.it/italiano/premi/outgoing-mobility
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
To favour mobility of young researchers the Italian Association for
Artificial
Intelligence (AI*IA) issues the AI*IA Outgoing Mobility Grants for 2017.
Applications are solicited for funding a research visit abroad of a PhD
student
enrolled at an Italian University.
AI*IA will cover the travel costs and living expenses (up to 2,000 euros)
of successful applicants.
The funding will be provided as a reimburswement for the expenses incurred
in the
visit. After the visit, the awarded person should send the Association the
receipts of her/his costs for which she/he would like to be refunded.
The central aim of these long visits is to build a research bridge between
researchers and to create a solid basis for long term collaborations.
Moreover,
the visit has to lead to a submission of an article on a joint research
topics to the Intelligenza Artificiale journal (
http://www.iospress.nl/journal/intelligenza-artificiale/).
Applications can be made by students enrolled full-time in a PhD programme
at an Italian University.
The applicant must be a member of the Association for 2017.
If she/he is not a member for 2017 she/he must register before applying.
Funding is available for 2 students.
The visit should start between the 15th of July 2017 and the 30th of June
2018.
Deadline for applications: June 20th, 2017
Notification of grants: July 4th, 2017
The information required in the application are:
1. name of the Italian PhD student who will go abroad;
2. name of the foreign researcher who will host the student;
3. name and address of the foreign Lab/Department and University;
4. a short (max 2 pages) resume/CV of the Italian student;
5. a short (max 2 pages) resume/CV of the foreign researcher;
6. a short (max 2 pages) description of the research that will be carried
out
during the visit;
7. a budget of the foreseen expenses;
8. declaration of the foreign host indicating that the hosting institution
is
willing to provide office space and access to lab facilities to conduct the
research;
9. expected visit dates.
The applications must be sent by email to out...@ai...
The applications will be examined by a committee composed by members of the
AI*IA Board of Directors.
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|
|
From: Fabrizio R. <fab...@un...> - 2017-05-18 07:11:01
|
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
AI*IA Incoming Mobility Grants 2017
Call for research visits
Deadline for applications: June 20th, 2017
https://sites.google.com/a/aixia.it/italiano/premi/incoming-mobility
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
To favour mobility of young researchers the Italian Association for
Artificial
Intelligence (AI*IA) issues the AIxIA Incoming Mobility Grants for 2017.
Applications are solicited for funding a research visit of a PhD student
enrolled at a foreign University to an Italian institution.
AI*IA will cover the travel costs and living expenses (up to 2,000 euros)
of successful applicants.
The funding will be provided as a reimbursement for the expenses incurred
in the
visit. After the visit, the awarded person should send the Association the
receipts of her/his costs for which she/he would like to be refunded.
The central aim of these long visits is to build a research bridge between
researchers and to create a solid basis for long term collaborations.
Moreover,
the visit has to lead to a submission of an article on a joint research
topics to the Intelligenza Artificiale journal (
www.iospress.nl/journal/intelligenza-artificiale/).
Eligibility for the visiting student is to be enrolled full-time in a PhD
programme at a foreign University.
Funding is available for 2 students.
The visit should start between the 15th of July 2017 and the 30th of June
2018.
Deadline for applications: June 20th, 2017
Notification of grants: July 4th, 2017
The information required in the application are:
1. name of the foreign PhD student who will be hosted;
2. name and address of the foreign Lab/Department and University;
3. name of the Italian researcher of the hosting institution. The Italian
researcher must be a member of the Association for 2016. If she/he is not
a member for 2016 she/he must register before applying.
4. a short (max 2 pages) resume/CV of the foreign student;
5. a short (max 2 pages) resume/CV of the Italian host;
6. a short (max 2 pages) description of the research that will be carried
out during the visit
7. a budget of the foreseen expenses;
8. declaration of the Italian host indicating that the hosting institution
is willing to provide office space and access to lab facilities to conduct
the research;
9. expected visit dates.
The applications must be sent by email to inc...@ai...
The applications will be examined by a committee composed by members of the
AI*IA Board of Directors.
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
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From: Lorenz B. <spo...@st...> - 2017-05-17 06:15:09
|
Hello Faranak, although there exists trivial solutions like A or the enumeration class containing all positive examples (here all individuals of A) that wouldn't be meaningful and doesn't contribute to the ontology schema. Do you have any particular example or use-case where this happens? Cheers, Lorenz > Hi, > > > In-Class Learning, you are given an existing class A, within an > ontology O and you want to describe A. > > Where do you see the problem when A, itself is the solution? > > > Best, > > Faranak > > > > > ------------------------------------------------------------------------------ > Check out the vibrant tech community on one of the world's most > engaging tech sites, Slashdot.org! http://sdm.link/slashdot > > > _______________________________________________ > dl-learner-discussion mailing list > dl-...@li... > https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion |
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From: Faranak S. <f.s...@qm...> - 2017-05-16 12:38:55
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Hi, In-Class Learning, you are given an existing class A, within an ontology O and you want to describe A. Where do you see the problem when A, itself is the solution? Best, Faranak |
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From: Faranak S. <f.s...@qm...> - 2017-05-08 15:58:38
|
Dear Professor;
I am using the DL-learner and I did face with few problems when I use
cross-validation which you might possibly help me over that;
The following is what I did so far;
We want to evaluate 7 classes based on Cross validation
Considering the 7 following Target classes with the number of instances
for each of them in our ontology;
Target C = {C_1: A (57), C_2: B (11), C_3: C (8),C_4: D(63), C_5: E
(30), C_6: F (83)C_7: G (21)}
A = {all the instances of the Target Classes = 273}, {p_1…, p_273}
a) i:=1
posExamplesClass ={Instance of C_2}
negExamples = Sets.difference(lp.getReasoner().getIndividuals(),
posExamplesClass); - all the indivisuals -{posExamplesClass}
b) consider p_i and learn set of L for all classes from O \cup (A
\setminus \{ p_i\}) %learn by taking out p_i From A
c) now compute precision, recall, F1 wrt O \cup A \cup L %test by
taking now also p_i into account wrt the whole ontology O, A, and
learned GCIs L.
d) i:= i+1
e) repeat a)- d) until i 273
Eventually, compute micro, macro precision, recall, F1 of all n trials.
I did use the provided CrossValidation sample code in DL-Learner
package and my main problem is;
As an example, I do have 11 instances for the Target C_2 as
posExamplesClass and 262 (273-11} instance for negative class, I won't
be able to get the proper learning solution and all I get is so general
concepts. (Obviously, the number of positive is much less than negExamples).
Also as I can have *n* different solutions, in order to select the best
the one I use;
List<OWLClassExpression list = la.getCurrentlyBestDescriptions();
OWLClassExpression BestDescription = list.get(*n*);
A couple of things;
A) Does it make any difference in the solution if the LP considered only positive? I did define the LP as following;
PosNegLPStandard lp = new PosNegLPStandard(rc, posExamples , negExamples);
Can you please let me know how I can define LP with only positive classes?
B) Obviously, the *n* (number of the best solution ) is changing for
each Target class but another problem is this*n*keepsp changing in
every Fold as well.
Imagine for the Target class C_1 we have the soulotuin
Fold 1, solutions *2:* Father and has kids, n=2
Fold 2, solutions *5:* Father and has kids, n=5
Then I have to change *n* in the following for each fold?
List<OWLClassExpression list = la.getCurrentlyBestDescriptions();
OWLClassExpression BestDescription = list.get(*n*);
How do you recommend me in this case and also how do you define the best
solution generally?
--
Faranak Sobhani
PhD Electronic Engineering Student
MMV (Multimedia and Vision) Lab
School of Electronic Engineering and Computer Science
Queen Mary University of London
Mile End Road, London E1 4NS, UK
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From: Sarker, Md K. <sar...@wr...> - 2017-05-03 17:01:03
|
Hi Lorenz, Thank you very much. With Regards, ------------- Md Kamruzzaman Sarker PhD Student Dept. of Computer Science and Engineering, Wright State University, Dayton, OH http://dase.cs.wright.edu/people/kamruzzaman-sarker ________________________________ From: Lorenz B. <spo...@st...> Sent: Wednesday, May 3, 2017 2:53 AM To: Sarker, Md Kamruzzaman; dl-...@li... Subject: Re: [DL-Learner discussion] DL-Learner solution not including object properties Hi Kamruzzaman, Hi Lorenz, Many thanks for your reply. I am sorry that I attached wrong ontology file here. Here I am attaching the corrected ontology file, corresponding configuration file and the corresponding solution file. I have did the same experiments and got similar result (solution not including object properties for father_3.conf problem, which is using father_3.owl ). no worries, thank you for the updated ontologies. Is it the case that object properties need to be connected to all individuals/classes if we want a solution which includes that object property? Well, there are two points you have to keep in mind for the current example: 1. DL-Learner tries to solve the learning problem such that a solution covers all positive examples while not covering any negative example. Especially the coverage of the positive examples is important, and DL-Learner is very strict here in. Indeed, this assumes a perfect world in which we always have perfect solutions - we know, that's usually not the case, therefore, we have a configuration parameter called "noisePercentage" which allows to lower the restrictions on the coverage of the positive examples. For instance, if you add the following line to the *.conf file alg.noisePercentage = 30 DL-Learner will also return Note, that in your example the concept "male" is already a perfect solution, thus, it will probably not find any concept with a score lower than 100% . In any case, you should always set some value for the noise as long as you don't know that the data is perfect and there must be a perfect solution. Hope this helps, but feel free to ask if you have any further questions. Cheers, Lorenz With Regards, ------------- Md Kamruzzaman Sarker PhD Student Dept. of Computer Science and Engineering, Wright State University, Dayton, OH http://dase.cs.wright.edu/people/kamruzzaman-sarker ________________________________ From: Lorenz B. <spo...@st...><mailto:spo...@st...> Sent: Tuesday, May 2, 2017 5:38 AM To: dl-...@li...<mailto:dl-...@li...> Subject: Re: [DL-Learner discussion] DL-Learner solution not including object properties Hi, unfortunately your ontologies do not contain what you say, i.e. * father_2 is the same as father_0 and does not contain an additional class "transgender" * father_3 contains a class "transgender" but not the classes "human" and "animal" and furthermore not the class assertions human(zaman), animal(cow) This is especially problematic since your conf file states that there is a positive example "zaman". Note, "male" is already a trivial solution for your configuration of the learning problem file, thus, it's not that clear which other solution you expect. Cheers, Lorenz Dear Concern, With due respect, I am exploring DL-Learner and running it for Learning Problem (Class learning). >From the experiments I am doing, I am not getting the solution which includes objectProperties. I am doing the experiments using the default father.conf (which using father.owl file). When using the default father.owl file the result includes hasChild object properties. But if I include a couple of extra classes in ontology and run with the same configuration file then the result does not include object properties. Does it mean, If we need a solution including object properties, is it required for every owlClass have to be related to the object property? The experiments result I have attached here, I have attached here the configuration and ontology file here for reference. ##Original ontology given in example folder of DL-Learner. I renamed it with father_0.owl solutions: 1: male (pred. acc.: 100.00%, F-measure: 100.00%) 2: male (pred. acc.: 100.00%, F-measure: 100.00%) 3: not (female) (pred. acc.: 100.00%, F-measure: 100.00%) 4: male or (hasChild some female) (pred. acc.: 100.00%, F-measure: 100.00%) 5: (not (female)) or (hasChild some female) (pred. acc.: 100.00%, F-measure: 100.00%) 6: male or (hasChild some (not (male))) (pred. acc.: 100.00%, F-measure: 100.00%) 7: (not (female)) or (hasChild some (not (male))) (pred. acc.: 100.00%, F-measure: 100.00%) 8: male or (hasChild some (hasChild some Thing)) (pred. acc.: 100.00%, F-measure: 100.00%) 9: male or (hasChild some (hasChild some male)) (pred. acc.: 100.00%, F-measure: 100.00%) 10: male or (hasChild some (hasChild some female)) (pred. acc.: 100.00%, F-measure: 100.00%) ##father_2.owl ##original ontology with transgender class and individual boby solutions: 1: male (pred. acc.: 100.00%, F-measure: 100.00%) 2: not (female) (pred. acc.: 100.00%, F-measure: 100.00%) 3: male or transgender (pred. acc.: 100.00%, F-measure: 100.00%) 4: transgender or (not (female)) (pred. acc.: 100.00%, F-measure: 100.00%) 5: male and (not (transgender)) (pred. acc.: 100.00%, F-measure: 100.00%) 6: male or (hasChild some transgender) (pred. acc.: 100.00%, F-measure: 100.00%) 7: male or (hasChild some female) (pred. acc.: 100.00%, F-measure: 100.00%) 8: (not (female)) and (not (transgender)) (pred. acc.: 100.00%, F-measure: 100.00%) 9: (not (female)) or (hasChild some female) (pred. acc.: 100.00%, F-measure: 100.00%) 10: male or (hasChild some (not (male))) (pred. acc.: 100.00%, F-measure: 100.00%) ##father_3.owl ##original ontology with transgender, human and animal class ## individual human(zaman) , individual animal(cow) solutions: 1: male (pred. acc.: 100.00%, F-measure: 100.00%) 2: not (female) (pred. acc.: 100.00%, F-measure: 100.00%) 3: male or transgender (pred. acc.: 100.00%, F-measure: 100.00%) 4: human or male (pred. acc.: 100.00%, F-measure: 100.00%) 5: animal or male (pred. acc.: 100.00%, F-measure: 100.00%) 6: transgender or (not (female)) (pred. acc.: 100.00%, F-measure: 100.00%) 7: human or (not (female)) (pred. acc.: 100.00%, F-measure: 100.00%) 8: animal or (not (female)) (pred. acc.: 100.00%, F-measure: 100.00%) 9: male and (not (transgender)) (pred. acc.: 100.00%, F-measure: 100.00%) 10: male and (not (human)) (pred. acc.: 100.00%, F-measure: 100.00%) ------------- Md Kamruzzaman Sarker PhD Student Dept. of Computer Science and Engineering, Wright State University, Dayton, OH http://dase.cs.wright.edu/people/kamruzzaman-sarker ------------------------------------------------------------------------------ Check out the vibrant tech community on one of the world's most engaging tech sites, Slashdot.org! http://sdm.link/slashdot<https://urldefense.proofpoint.com/v2/url?u=http-3A__sdm.link_slashdot&d=DwMF-g&c=3buyMx9JlH1z22L_G5pM28wz_Ru6WjhVHwo-vpeS0Gk&r=gOKJ7GGIYSN5ZikR0Wci79-a9BTFdST29ynNAKTmkas&m=Rn6HNVx7QPR-sQg2zKEIrFVRvipjJvnfUpNHQHN7mjg&s=XXVJ4EP4pm7Aks2TiPUVeJ2l_AGeR3crohUski1ZqKk&e=> _______________________________________________ dl-learner-discussion mailing list dl-...@li...<mailto:dl-...@li...> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion<https://urldefense.proofpoint.com/v2/url?u=https-3A__lists.sourceforge.net_lists_listinfo_dl-2Dlearner-2Ddiscussion&d=DwMF-g&c=3buyMx9JlH1z22L_G5pM28wz_Ru6WjhVHwo-vpeS0Gk&r=gOKJ7GGIYSN5ZikR0Wci79-a9BTFdST29ynNAKTmkas&m=Rn6HNVx7QPR-sQg2zKEIrFVRvipjJvnfUpNHQHN7mjg&s=fE8SOJoa1PDfwXIf6j6_kaDDaw9RvDG_wevcTY3Rg6g&e=> |
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From: Lorenz B. <spo...@st...> - 2017-05-03 06:53:14
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Hi Kamruzzaman, > Hi Lorenz, > > Many thanks for your reply. I am sorry that I attached wrong ontology > file here. > > > *Here I am attaching the corrected ontology file, corresponding > configuration file and the corresponding solution file.* > > > *I have did the same experiments and got similar result (solution not > including object properties for father_3.conf problem, which is using > father_3.owl ).* > no worries, thank you for the updated ontologies. > > > > Is it the case that object properties need to be connected to all > individuals/classes if we want a solution which includes that object > property? > Well, there are two points you have to keep in mind for the current example: 1. DL-Learner tries to solve the learning problem such that a solution covers all positive examples while not covering any negative example. Especially the coverage of the positive examples is important, and DL-Learner is very strict here in. Indeed, this assumes a perfect world in which we always have perfect solutions - we know, that's usually not the case, therefore, we have a configuration parameter called "noisePercentage" which allows to lower the restrictions on the coverage of the positive examples. For instance, if you add the following line to the *.conf file alg.noisePercentage = 30 DL-Learner will also return Note, that in your example the concept "male" is already a perfect solution, thus, it will probably not find any concept with a score lower than 100% . In any case, you should always set some value for the noise as long as you don't know that the data is perfect and there must be a perfect solution. Hope this helps, but feel free to ask if you have any further questions. Cheers, Lorenz > > > With Regards, > > ------------- > > Md Kamruzzaman Sarker > > PhD Student > > Dept. of Computer Science and Engineering, Wright State University, > Dayton, OH > > http://dase.cs.wright.edu/people/kamruzzaman-sarker > > > > ------------------------------------------------------------------------ > *From:* Lorenz B. <spo...@st...> > *Sent:* Tuesday, May 2, 2017 5:38 AM > *To:* dl-...@li... > *Subject:* Re: [DL-Learner discussion] DL-Learner solution not > including object properties > > Hi, > > unfortunately your ontologies do not contain what you say, i.e. > > * father_2 is the same as father_0 and does not contain an additional > class "transgender" > * father_3 contains a class "transgender" but not the classes "human" > and "animal" and furthermore not the class assertions human(zaman), > animal(cow) > > This is especially problematic since your conf file states that there > is a positive example "zaman". > Note, "male" is already a trivial solution for your configuration of > the learning problem file, thus, it's not that clear which other > solution you expect. > > > Cheers, > Lorenz > >> Dear Concern, >> >> With due respect, I am exploring DL-Learner and running it for >> Learning Problem (Class learning). >> >> >> From the experiments I am doing, *I am not getting the solution which >> includes objectProperties*. >> >> I am doing the experiments using the default father.conf (which using >> father.owl file). >> >> When using the default father.owl file the result includes *hasChild* >> object properties. >> >> But if I include a couple of extra classes in ontology and run with >> the same configuration file then the result does not include object >> properties. >> >> >> *Does it mean,* >> >> *If we need a solution including object properties, is it required >> for every owlClass have to be related to the object property? * >> >> * >> * >> >> The experiments result I have attached here, I have attached here the >> configuration and ontology file here for reference. >> >> >> ##Original ontology given in example folder of DL-Learner. I renamed >> it with father_0.owl >> >> solutions: >> 1: male (pred. acc.: 100.00%, F-measure: 100.00%) >> 2: male (pred. acc.: 100.00%, F-measure: 100.00%) >> 3: not (female) (pred. acc.: 100.00%, F-measure: 100.00%) >> 4: male or (hasChild some female) (pred. acc.: 100.00%, F-measure: >> 100.00%) >> 5: (not (female)) or (hasChild some female) (pred. acc.: 100.00%, >> F-measure: 100.00%) >> 6: male or (hasChild some (not (male))) (pred. acc.: 100.00%, >> F-measure: 100.00%) >> 7: (not (female)) or (hasChild some (not (male))) (pred. acc.: >> 100.00%, F-measure: 100.00%) >> 8: male or (hasChild some (hasChild some Thing)) (pred. acc.: >> 100.00%, F-measure: 100.00%) >> 9: male or (hasChild some (hasChild some male)) (pred. acc.: 100.00%, >> F-measure: 100.00%) >> 10: male or (hasChild some (hasChild some female)) (pred. acc.: >> 100.00%, F-measure: 100.00%) >> >> >> ##father_2.owl >> ##original ontology with transgender class and individual boby >> >> solutions: >> 1: male (pred. acc.: 100.00%, F-measure: 100.00%) >> 2: not (female) (pred. acc.: 100.00%, F-measure: 100.00%) >> 3: male or transgender (pred. acc.: 100.00%, F-measure: 100.00%) >> 4: transgender or (not (female)) (pred. acc.: 100.00%, F-measure: >> 100.00%) >> 5: male and (not (transgender)) (pred. acc.: 100.00%, F-measure: 100.00%) >> 6: male or (hasChild some transgender) (pred. acc.: 100.00%, >> F-measure: 100.00%) >> 7: male or (hasChild some female) (pred. acc.: 100.00%, F-measure: >> 100.00%) >> 8: (not (female)) and (not (transgender)) (pred. acc.: 100.00%, >> F-measure: 100.00%) >> 9: (not (female)) or (hasChild some female) (pred. acc.: 100.00%, >> F-measure: 100.00%) >> 10: male or (hasChild some (not (male))) (pred. acc.: 100.00%, >> F-measure: 100.00%) >> >> >> ##father_3.owl >> ##original ontology with transgender, human and animal class >> ## individual human(zaman) , individual animal(cow) >> >> solutions: >> 1: male (pred. acc.: 100.00%, F-measure: 100.00%) >> 2: not (female) (pred. acc.: 100.00%, F-measure: 100.00%) >> 3: male or transgender (pred. acc.: 100.00%, F-measure: 100.00%) >> 4: human or male (pred. acc.: 100.00%, F-measure: 100.00%) >> 5: animal or male (pred. acc.: 100.00%, F-measure: 100.00%) >> 6: transgender or (not (female)) (pred. acc.: 100.00%, F-measure: >> 100.00%) >> 7: human or (not (female)) (pred. acc.: 100.00%, F-measure: 100.00%) >> 8: animal or (not (female)) (pred. acc.: 100.00%, F-measure: 100.00%) >> 9: male and (not (transgender)) (pred. acc.: 100.00%, F-measure: 100.00%) >> 10: male and (not (human)) (pred. acc.: 100.00%, F-measure: 100.00%) >> >> >> >> >> ------------- >> >> Md Kamruzzaman Sarker >> >> PhD Student >> >> Dept. of Computer Science and Engineering, Wright State University, >> Dayton, OH >> >> http://dase.cs.wright.edu/people/kamruzzaman-sarker >> >> >> >> ------------------------------------------------------------------------------ >> Check out the vibrant tech community on one of the world's most >> engaging tech sites, Slashdot.org! http://sdm.link/slashdot >> >> >> _______________________________________________ >> dl-learner-discussion mailing list >> dl-...@li... >> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion > |
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From: Giuseppe <gi...@gm...> - 2017-05-02 16:42:28
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Dear Md Kamruzzaman Sarker, I think you should remove "ex:heinz" from the set of positive examples. Because, according to the ontology, Heinz is a male and nowhere in the ontology it is written that he has a child. You have as positive individuals all the individuals that are male and as negative ones the individuals that belongs to the female class. Therefore as best class expression DL-Learner will find "male". (If you remove Heinz you will notice the "hasChild" property in the list of candidate class expressions) Best Regards, Giuseppe Cota On 02/05/2017 17:33, Sarker, Md Kamruzzaman wrote: > > Hi Lorenz, > > Many thanks for your reply. I am sorry that I attached wrong ontology > file here. > > > *Here I am attaching the corrected ontology file, corresponding > configuration file and the corresponding solution file.* > > > *I have did the same experiments and got similar result (solution not > including object properties for father_3.conf problem, which is using > father_3.owl ).* > > Is it the case that object properties need to be connected to all > individuals/classes if we want a solution which includes that object > property? > > > With Regards, > > ------------- > > Md Kamruzzaman Sarker > > PhD Student > > Dept. of Computer Science and Engineering, Wright State University, > Dayton, OH > > http://dase.cs.wright.edu/people/kamruzzaman-sarker > > > > ------------------------------------------------------------------------ > *From:* Lorenz B. <spo...@st...> > *Sent:* Tuesday, May 2, 2017 5:38 AM > *To:* dl-...@li... > *Subject:* Re: [DL-Learner discussion] DL-Learner solution not > including object properties > Hi, > > unfortunately your ontologies do not contain what you say, i.e. > > * father_2 is the same as father_0 and does not contain an additional > class "transgender" > * father_3 contains a class "transgender" but not the classes "human" > and "animal" and furthermore not the class assertions human(zaman), > animal(cow) > > This is especially problematic since your conf file states that there > is a positive example "zaman". > Note, "male" is already a trivial solution for your configuration of > the learning problem file, thus, it's not that clear which other > solution you expect. > > > Cheers, > Lorenz > >> Dear Concern, >> >> With due respect, I am exploring DL-Learner and running it for >> Learning Problem (Class learning). >> >> >> From the experiments I am doing, *I am not getting the solution which >> includes objectProperties*. >> >> I am doing the experiments using the default father.conf (which using >> father.owl file). >> >> When using the default father.owl file the result includes *hasChild* >> object properties. >> >> But if I include a couple of extra classes in ontology and run with >> the same configuration file then the result does not include object >> properties. >> >> >> *Does it mean,* >> >> *If we need a solution including object properties, is it required >> for every owlClass have to be related to the object property? * >> >> * >> * >> >> The experiments result I have attached here, I have attached here the >> configuration and ontology file here for reference. >> >> >> ##Original ontology given in example folder of DL-Learner. I renamed >> it with father_0.owl >> >> solutions: >> 1: male (pred. acc.: 100.00%, F-measure: 100.00%) >> 2: male (pred. acc.: 100.00%, F-measure: 100.00%) >> 3: not (female) (pred. acc.: 100.00%, F-measure: 100.00%) >> 4: male or (hasChild some female) (pred. acc.: 100.00%, F-measure: >> 100.00%) >> 5: (not (female)) or (hasChild some female) (pred. acc.: 100.00%, >> F-measure: 100.00%) >> 6: male or (hasChild some (not (male))) (pred. acc.: 100.00%, >> F-measure: 100.00%) >> 7: (not (female)) or (hasChild some (not (male))) (pred. acc.: >> 100.00%, F-measure: 100.00%) >> 8: male or (hasChild some (hasChild some Thing)) (pred. acc.: >> 100.00%, F-measure: 100.00%) >> 9: male or (hasChild some (hasChild some male)) (pred. acc.: 100.00%, >> F-measure: 100.00%) >> 10: male or (hasChild some (hasChild some female)) (pred. acc.: >> 100.00%, F-measure: 100.00%) >> >> >> ##father_2.owl >> ##original ontology with transgender class and individual boby >> >> solutions: >> 1: male (pred. acc.: 100.00%, F-measure: 100.00%) >> 2: not (female) (pred. acc.: 100.00%, F-measure: 100.00%) >> 3: male or transgender (pred. acc.: 100.00%, F-measure: 100.00%) >> 4: transgender or (not (female)) (pred. acc.: 100.00%, F-measure: >> 100.00%) >> 5: male and (not (transgender)) (pred. acc.: 100.00%, F-measure: 100.00%) >> 6: male or (hasChild some transgender) (pred. acc.: 100.00%, >> F-measure: 100.00%) >> 7: male or (hasChild some female) (pred. acc.: 100.00%, F-measure: >> 100.00%) >> 8: (not (female)) and (not (transgender)) (pred. acc.: 100.00%, >> F-measure: 100.00%) >> 9: (not (female)) or (hasChild some female) (pred. acc.: 100.00%, >> F-measure: 100.00%) >> 10: male or (hasChild some (not (male))) (pred. acc.: 100.00%, >> F-measure: 100.00%) >> >> >> ##father_3.owl >> ##original ontology with transgender, human and animal class >> ## individual human(zaman) , individual animal(cow) >> >> solutions: >> 1: male (pred. acc.: 100.00%, F-measure: 100.00%) >> 2: not (female) (pred. acc.: 100.00%, F-measure: 100.00%) >> 3: male or transgender (pred. acc.: 100.00%, F-measure: 100.00%) >> 4: human or male (pred. acc.: 100.00%, F-measure: 100.00%) >> 5: animal or male (pred. acc.: 100.00%, F-measure: 100.00%) >> 6: transgender or (not (female)) (pred. acc.: 100.00%, F-measure: >> 100.00%) >> 7: human or (not (female)) (pred. acc.: 100.00%, F-measure: 100.00%) >> 8: animal or (not (female)) (pred. acc.: 100.00%, F-measure: 100.00%) >> 9: male and (not (transgender)) (pred. acc.: 100.00%, F-measure: 100.00%) >> 10: male and (not (human)) (pred. acc.: 100.00%, F-measure: 100.00%) >> >> >> >> ------------- >> >> Md Kamruzzaman Sarker >> >> PhD Student >> >> Dept. of Computer Science and Engineering, Wright State University, >> Dayton, OH >> >> http://dase.cs.wright.edu/people/kamruzzaman-sarker >> >> >> >> ------------------------------------------------------------------------------ >> Check out the vibrant tech community on one of the world's most >> engaging tech sites, Slashdot.org!http://sdm.link/slashdot >> >> >> _______________________________________________ >> dl-learner-discussion mailing list >> dl-...@li... >> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion > > > > This body part will be downloaded on demand. > > > This body part will be downloaded on demand. |
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From: Sarker, Md K. <sar...@wr...> - 2017-05-02 15:33:17
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Hi Lorenz, Many thanks for your reply. I am sorry that I attached wrong ontology file here. Here I am attaching the corrected ontology file, corresponding configuration file and the corresponding solution file. I have did the same experiments and got similar result (solution not including object properties for father_3.conf problem, which is using father_3.owl ). Is it the case that object properties need to be connected to all individuals/classes if we want a solution which includes that object property? With Regards, ------------- Md Kamruzzaman Sarker PhD Student Dept. of Computer Science and Engineering, Wright State University, Dayton, OH http://dase.cs.wright.edu/people/kamruzzaman-sarker ________________________________ From: Lorenz B. <spo...@st...> Sent: Tuesday, May 2, 2017 5:38 AM To: dl-...@li... Subject: Re: [DL-Learner discussion] DL-Learner solution not including object properties Hi, unfortunately your ontologies do not contain what you say, i.e. * father_2 is the same as father_0 and does not contain an additional class "transgender" * father_3 contains a class "transgender" but not the classes "human" and "animal" and furthermore not the class assertions human(zaman), animal(cow) This is especially problematic since your conf file states that there is a positive example "zaman". Note, "male" is already a trivial solution for your configuration of the learning problem file, thus, it's not that clear which other solution you expect. Cheers, Lorenz Dear Concern, With due respect, I am exploring DL-Learner and running it for Learning Problem (Class learning). >From the experiments I am doing, I am not getting the solution which includes objectProperties. I am doing the experiments using the default father.conf (which using father.owl file). When using the default father.owl file the result includes hasChild object properties. But if I include a couple of extra classes in ontology and run with the same configuration file then the result does not include object properties. Does it mean, If we need a solution including object properties, is it required for every owlClass have to be related to the object property? The experiments result I have attached here, I have attached here the configuration and ontology file here for reference. ##Original ontology given in example folder of DL-Learner. I renamed it with father_0.owl solutions: 1: male (pred. acc.: 100.00%, F-measure: 100.00%) 2: male (pred. acc.: 100.00%, F-measure: 100.00%) 3: not (female) (pred. acc.: 100.00%, F-measure: 100.00%) 4: male or (hasChild some female) (pred. acc.: 100.00%, F-measure: 100.00%) 5: (not (female)) or (hasChild some female) (pred. acc.: 100.00%, F-measure: 100.00%) 6: male or (hasChild some (not (male))) (pred. acc.: 100.00%, F-measure: 100.00%) 7: (not (female)) or (hasChild some (not (male))) (pred. acc.: 100.00%, F-measure: 100.00%) 8: male or (hasChild some (hasChild some Thing)) (pred. acc.: 100.00%, F-measure: 100.00%) 9: male or (hasChild some (hasChild some male)) (pred. acc.: 100.00%, F-measure: 100.00%) 10: male or (hasChild some (hasChild some female)) (pred. acc.: 100.00%, F-measure: 100.00%) ##father_2.owl ##original ontology with transgender class and individual boby solutions: 1: male (pred. acc.: 100.00%, F-measure: 100.00%) 2: not (female) (pred. acc.: 100.00%, F-measure: 100.00%) 3: male or transgender (pred. acc.: 100.00%, F-measure: 100.00%) 4: transgender or (not (female)) (pred. acc.: 100.00%, F-measure: 100.00%) 5: male and (not (transgender)) (pred. acc.: 100.00%, F-measure: 100.00%) 6: male or (hasChild some transgender) (pred. acc.: 100.00%, F-measure: 100.00%) 7: male or (hasChild some female) (pred. acc.: 100.00%, F-measure: 100.00%) 8: (not (female)) and (not (transgender)) (pred. acc.: 100.00%, F-measure: 100.00%) 9: (not (female)) or (hasChild some female) (pred. acc.: 100.00%, F-measure: 100.00%) 10: male or (hasChild some (not (male))) (pred. acc.: 100.00%, F-measure: 100.00%) ##father_3.owl ##original ontology with transgender, human and animal class ## individual human(zaman) , individual animal(cow) solutions: 1: male (pred. acc.: 100.00%, F-measure: 100.00%) 2: not (female) (pred. acc.: 100.00%, F-measure: 100.00%) 3: male or transgender (pred. acc.: 100.00%, F-measure: 100.00%) 4: human or male (pred. acc.: 100.00%, F-measure: 100.00%) 5: animal or male (pred. acc.: 100.00%, F-measure: 100.00%) 6: transgender or (not (female)) (pred. acc.: 100.00%, F-measure: 100.00%) 7: human or (not (female)) (pred. acc.: 100.00%, F-measure: 100.00%) 8: animal or (not (female)) (pred. acc.: 100.00%, F-measure: 100.00%) 9: male and (not (transgender)) (pred. acc.: 100.00%, F-measure: 100.00%) 10: male and (not (human)) (pred. acc.: 100.00%, F-measure: 100.00%) ------------- Md Kamruzzaman Sarker PhD Student Dept. of Computer Science and Engineering, Wright State University, Dayton, OH http://dase.cs.wright.edu/people/kamruzzaman-sarker ------------------------------------------------------------------------------ Check out the vibrant tech community on one of the world's most engaging tech sites, Slashdot.org! http://sdm.link/slashdot<https://urldefense.proofpoint.com/v2/url?u=http-3A__sdm.link_slashdot&d=DwMF-g&c=3buyMx9JlH1z22L_G5pM28wz_Ru6WjhVHwo-vpeS0Gk&r=gOKJ7GGIYSN5ZikR0Wci79-a9BTFdST29ynNAKTmkas&m=Rn6HNVx7QPR-sQg2zKEIrFVRvipjJvnfUpNHQHN7mjg&s=XXVJ4EP4pm7Aks2TiPUVeJ2l_AGeR3crohUski1ZqKk&e=> _______________________________________________ dl-learner-discussion mailing list dl-...@li...<mailto:dl-...@li...> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion<https://urldefense.proofpoint.com/v2/url?u=https-3A__lists.sourceforge.net_lists_listinfo_dl-2Dlearner-2Ddiscussion&d=DwMF-g&c=3buyMx9JlH1z22L_G5pM28wz_Ru6WjhVHwo-vpeS0Gk&r=gOKJ7GGIYSN5ZikR0Wci79-a9BTFdST29ynNAKTmkas&m=Rn6HNVx7QPR-sQg2zKEIrFVRvipjJvnfUpNHQHN7mjg&s=fE8SOJoa1PDfwXIf6j6_kaDDaw9RvDG_wevcTY3Rg6g&e=> |
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From: Lorenz B. <spo...@st...> - 2017-05-02 09:39:00
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Hi, unfortunately your ontologies do not contain what you say, i.e. * father_2 is the same as father_0 and does not contain an additional class "transgender" * father_3 contains a class "transgender" but not the classes "human" and "animal" and furthermore not the class assertions human(zaman), animal(cow) This is especially problematic since your conf file states that there is a positive example "zaman". Note, "male" is already a trivial solution for your configuration of the learning problem file, thus, it's not that clear which other solution you expect. Cheers, Lorenz > Dear Concern, > > With due respect, I am exploring DL-Learner and running it for > Learning Problem (Class learning). > > > From the experiments I am doing, *I am not getting the solution which > includes objectProperties*. > > I am doing the experiments using the default father.conf (which using > father.owl file). > > When using the default father.owl file the result includes *hasChild* > object properties. > > But if I include a couple of extra classes in ontology and run with > the same configuration file then the result does not include object > properties. > > > *Does it mean,* > > *If we need a solution including object properties, is it required for > every owlClass have to be related to the object property? * > > * > * > > The experiments result I have attached here, I have attached here the > configuration and ontology file here for reference. > > > ##Original ontology given in example folder of DL-Learner. I renamed > it with father_0.owl > > solutions: > 1: male (pred. acc.: 100.00%, F-measure: 100.00%) > 2: male (pred. acc.: 100.00%, F-measure: 100.00%) > 3: not (female) (pred. acc.: 100.00%, F-measure: 100.00%) > 4: male or (hasChild some female) (pred. acc.: 100.00%, F-measure: > 100.00%) > 5: (not (female)) or (hasChild some female) (pred. acc.: 100.00%, > F-measure: 100.00%) > 6: male or (hasChild some (not (male))) (pred. acc.: 100.00%, > F-measure: 100.00%) > 7: (not (female)) or (hasChild some (not (male))) (pred. acc.: > 100.00%, F-measure: 100.00%) > 8: male or (hasChild some (hasChild some Thing)) (pred. acc.: 100.00%, > F-measure: 100.00%) > 9: male or (hasChild some (hasChild some male)) (pred. acc.: 100.00%, > F-measure: 100.00%) > 10: male or (hasChild some (hasChild some female)) (pred. acc.: > 100.00%, F-measure: 100.00%) > > > ##father_2.owl > ##original ontology with transgender class and individual boby > > solutions: > 1: male (pred. acc.: 100.00%, F-measure: 100.00%) > 2: not (female) (pred. acc.: 100.00%, F-measure: 100.00%) > 3: male or transgender (pred. acc.: 100.00%, F-measure: 100.00%) > 4: transgender or (not (female)) (pred. acc.: 100.00%, F-measure: 100.00%) > 5: male and (not (transgender)) (pred. acc.: 100.00%, F-measure: 100.00%) > 6: male or (hasChild some transgender) (pred. acc.: 100.00%, > F-measure: 100.00%) > 7: male or (hasChild some female) (pred. acc.: 100.00%, F-measure: > 100.00%) > 8: (not (female)) and (not (transgender)) (pred. acc.: 100.00%, > F-measure: 100.00%) > 9: (not (female)) or (hasChild some female) (pred. acc.: 100.00%, > F-measure: 100.00%) > 10: male or (hasChild some (not (male))) (pred. acc.: 100.00%, > F-measure: 100.00%) > > > ##father_3.owl > ##original ontology with transgender, human and animal class > ## individual human(zaman) , individual animal(cow) > > solutions: > 1: male (pred. acc.: 100.00%, F-measure: 100.00%) > 2: not (female) (pred. acc.: 100.00%, F-measure: 100.00%) > 3: male or transgender (pred. acc.: 100.00%, F-measure: 100.00%) > 4: human or male (pred. acc.: 100.00%, F-measure: 100.00%) > 5: animal or male (pred. acc.: 100.00%, F-measure: 100.00%) > 6: transgender or (not (female)) (pred. acc.: 100.00%, F-measure: 100.00%) > 7: human or (not (female)) (pred. acc.: 100.00%, F-measure: 100.00%) > 8: animal or (not (female)) (pred. acc.: 100.00%, F-measure: 100.00%) > 9: male and (not (transgender)) (pred. acc.: 100.00%, F-measure: 100.00%) > 10: male and (not (human)) (pred. acc.: 100.00%, F-measure: 100.00%) > > > > > ------------- > > Md Kamruzzaman Sarker > > PhD Student > > Dept. of Computer Science and Engineering, Wright State University, > Dayton, OH > > http://dase.cs.wright.edu/people/kamruzzaman-sarker > > > > ------------------------------------------------------------------------------ > Check out the vibrant tech community on one of the world's most > engaging tech sites, Slashdot.org! http://sdm.link/slashdot > > > _______________________________________________ > dl-learner-discussion mailing list > dl-...@li... > https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion |
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From: Sarker, Md K. <sar...@wr...> - 2017-05-02 07:56:46
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Dear Concern, With due respect, I am exploring DL-Learner and running it for Learning Problem (Class learning). >From the experiments I am doing, I am not getting the solution which includes objectProperties. I am doing the experiments using the default father.conf (which using father.owl file). When using the default father.owl file the result includes hasChild object properties. But if I include a couple of extra classes in ontology and run with the same configuration file then the result does not include object properties. Does it mean, If we need a solution including object properties, is it required for every owlClass have to be related to the object property? The experiments result I have attached here, I have attached here the configuration and ontology file here for reference. ##Original ontology given in example folder of DL-Learner. I renamed it with father_0.owl solutions: 1: male (pred. acc.: 100.00%, F-measure: 100.00%) 2: male (pred. acc.: 100.00%, F-measure: 100.00%) 3: not (female) (pred. acc.: 100.00%, F-measure: 100.00%) 4: male or (hasChild some female) (pred. acc.: 100.00%, F-measure: 100.00%) 5: (not (female)) or (hasChild some female) (pred. acc.: 100.00%, F-measure: 100.00%) 6: male or (hasChild some (not (male))) (pred. acc.: 100.00%, F-measure: 100.00%) 7: (not (female)) or (hasChild some (not (male))) (pred. acc.: 100.00%, F-measure: 100.00%) 8: male or (hasChild some (hasChild some Thing)) (pred. acc.: 100.00%, F-measure: 100.00%) 9: male or (hasChild some (hasChild some male)) (pred. acc.: 100.00%, F-measure: 100.00%) 10: male or (hasChild some (hasChild some female)) (pred. acc.: 100.00%, F-measure: 100.00%) ##father_2.owl ##original ontology with transgender class and individual boby solutions: 1: male (pred. acc.: 100.00%, F-measure: 100.00%) 2: not (female) (pred. acc.: 100.00%, F-measure: 100.00%) 3: male or transgender (pred. acc.: 100.00%, F-measure: 100.00%) 4: transgender or (not (female)) (pred. acc.: 100.00%, F-measure: 100.00%) 5: male and (not (transgender)) (pred. acc.: 100.00%, F-measure: 100.00%) 6: male or (hasChild some transgender) (pred. acc.: 100.00%, F-measure: 100.00%) 7: male or (hasChild some female) (pred. acc.: 100.00%, F-measure: 100.00%) 8: (not (female)) and (not (transgender)) (pred. acc.: 100.00%, F-measure: 100.00%) 9: (not (female)) or (hasChild some female) (pred. acc.: 100.00%, F-measure: 100.00%) 10: male or (hasChild some (not (male))) (pred. acc.: 100.00%, F-measure: 100.00%) ##father_3.owl ##original ontology with transgender, human and animal class ## individual human(zaman) , individual animal(cow) solutions: 1: male (pred. acc.: 100.00%, F-measure: 100.00%) 2: not (female) (pred. acc.: 100.00%, F-measure: 100.00%) 3: male or transgender (pred. acc.: 100.00%, F-measure: 100.00%) 4: human or male (pred. acc.: 100.00%, F-measure: 100.00%) 5: animal or male (pred. acc.: 100.00%, F-measure: 100.00%) 6: transgender or (not (female)) (pred. acc.: 100.00%, F-measure: 100.00%) 7: human or (not (female)) (pred. acc.: 100.00%, F-measure: 100.00%) 8: animal or (not (female)) (pred. acc.: 100.00%, F-measure: 100.00%) 9: male and (not (transgender)) (pred. acc.: 100.00%, F-measure: 100.00%) 10: male and (not (human)) (pred. acc.: 100.00%, F-measure: 100.00%) ------------- Md Kamruzzaman Sarker PhD Student Dept. of Computer Science and Engineering, Wright State University, Dayton, OH http://dase.cs.wright.edu/people/kamruzzaman-sarker |
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