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dl-learner-discussion
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From: alec <Cel...@lr...> - 2014-12-18 09:33:14
Attachments:
dl_learner noise.zip
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Hello, I have an ontology describing 10000 films. I have built a conf file with positive and negative examples for “American films”. I want to learn that an American film “isFromCountry some _US”. When I run DL-Learner, it works well. Now, if I add a little bit of noise (changing one positive example into negative example and vice versa), it does not work anymore. “isFromCountry some Country” is learned (acc=42.86%). I have 10000 films, so this noise represents only 0.02% of error. Is it possible to get the good definition with some noise in my examples by running DL-Learner with some parameters I don’t know about or does DL-Learner just not handle noise? Thanks in advance for your answer. I attached the files if you want to make some tests. I use dllearner-1.0-beta-3 on Windows. Best regards, Céline |
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From: Lorenz B. <spo...@st...> - 2014-12-18 17:40:40
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Hello Céline, there is a noise parameter called "noisePercentage" for the learning algorithm CELOE, so you could simply define a noise value like 20%. This will allow to return solutions in which 20% of the positive examples are not covered by the solution. Your conf file should be extended by the line: alg.noisePercentage = "20" Hope this helps. Kind regards, Lorenz > Hello, > > I have an ontology describing 10000 films. I have built a conf file > with positive and negative examples for “American films”. I want to > learn that an American film “isFromCountry some _US”. When I run > DL-Learner, it works well. Now, if I add a little bit of noise > (changing one positive example into negative example and vice versa), > it does not work anymore. “isFromCountry some Country” is learned > (acc=42.86%). I have 10000 films, so this noise represents only 0.02% > of error. > Is it possible to get the good definition with some noise in my > examples by running DL-Learner with some parameters I don’t know about > or does DL-Learner just not handle noise? > > Thanks in advance for your answer. I attached the files if you want to > make some tests. I use dllearner-1.0-beta-3 on Windows. > > Best regards, > Céline > > > ------------------------------------------------------------------------------ > Download BIRT iHub F-Type - The Free Enterprise-Grade BIRT Server > from Actuate! Instantly Supercharge Your Business Reports and Dashboards > with Interactivity, Sharing, Native Excel Exports, App Integration & more > Get technology previously reserved for billion-dollar corporations, FREE > http://pubads.g.doubleclick.net/gampad/clk?id=164703151&iu=/4140/ostg.clktrk > > > _______________________________________________ > dl-learner-discussion mailing list > dl-...@li... > https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion |
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From: alec <Cel...@lr...> - 2014-12-19 16:08:03
Attachments:
dl_learner_complicated_def.zip
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Hello Lorenz, Thanks a lot, that helps. I have another problem. I have an ontology describing holiday’s destinations. If I want to learn some simple definitions like "hasActivity some Animation" (or any "hasObjectProperty some Concept") it works well. But if my definitions are more complicated, it does not work. There is no noise in my examples. I want to learn: 1. "hasCulture min 6 Culture" 2. "(hasActivity some OldTown) or (hasEnvironment some Shopping)" 3. "(has Activity some Bathing) and (hasWeather min 2 ( (avgTemperature some double [>=23.0]) and (precipitationMm some double[<=70.0]) and (concernMonth some hasSeason some MidSummer)))" I get: 1. hasCulture min 5 ((not Archaeology) and (not Architecture)) (pred. acc.: 97,50%, F-measure: 94,74%) 2. hasActivity only (not TowedWatersport) (pred. acc.: 68,75%, F-measure: 80,00%) 3. (hasActivity some Excursion and hasEnvironment some WaterActivity) (pred. acc.: 68,75%, F-measure: 73,12%) Could you explain why I do not get the good definitions (or at least some definitions with 100% of accuracy)? Are my definitions too complicated? Did I miss something? Thank you in advance! Sorry for disturbing you again. Best regards, Céline Le 18-12-2014 18:40, Lorenz Bühmann a écrit : > Hello Céline, > > there is a noise parameter called "noisePercentage" for the learning > algorithm CELOE, so you could simply define a noise value like 20%. > This will allow to return solutions in which 20% of the positive > examples are not covered by the solution. > > Your conf file should be extended by the line: > > alg.noisePercentage = "20" > > Hope this helps. > > Kind regards, > Lorenz > >> Hello, >> >> I have an ontology describing 10000 films. I have built a conf file >> with positive and negative examples for “American films”. I want >> to learn that an American film “isFromCountry some _US”. When I >> run DL-Learner, it works well. Now, if I add a little bit of noise >> (changing one positive example into negative example and vice >> versa), it does not work anymore. “isFromCountry some Country” >> is learned (acc=42.86%). I have 10000 films, so this noise >> represents only 0.02% of error. >> Is it possible to get the good definition with some noise in my >> examples by running DL-Learner with some parameters I don’t know >> about or does DL-Learner just not handle noise? >> >> Thanks in advance for your answer. I attached the files if you want >> to make some tests. I use dllearner-1.0-beta-3 on Windows. >> >> Best regards, >> Céline >> >> > ------------------------------------------------------------------------------ >> Download BIRT iHub F-Type - The Free Enterprise-Grade BIRT Server >> from Actuate! Instantly Supercharge Your Business Reports and >> Dashboards >> with Interactivity, Sharing, Native Excel Exports, App Integration & >> more >> Get technology previously reserved for billion-dollar corporations, >> FREE >> > http://pubads.g.doubleclick.net/gampad/clk?id=164703151&iu=/4140/ostg.clktrk >> [1] >> >> _______________________________________________ >> dl-learner-discussion mailing list >> dl-...@li... >> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion >> [2] > > > > Links: > ------ > [1] > http://pubads.g.doubleclick.net/gampad/clk?id=164703151&iu=/4140/ostg.clktrk > [2] https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion > > ------------------------------------------------------------------------------ > Download BIRT iHub F-Type - The Free Enterprise-Grade BIRT Server > from Actuate! Instantly Supercharge Your Business Reports and > Dashboards > with Interactivity, Sharing, Native Excel Exports, App Integration & > more > Get technology previously reserved for billion-dollar corporations, > FREE > http://pubads.g.doubleclick.net/gampad/clk?id=164703151&iu=/4140/ostg.clktrk > > _______________________________________________ > dl-learner-discussion mailing list > dl-...@li... > https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion |
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From: Lorenz B. <spo...@st...> - 2014-12-29 15:13:23
Attachments:
dllearner_discussion_examples.tar.gz
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Hello Céline, let me try to explain why things do not work as you would expect. First of all, please always keep in mind that DL-Learner is searching for OWL class expressions that 1. cover as many positive examples as possible 2. cover as less negative examples as possible This search space is quite large and that's why, assuming that a perfect solution exists (this is not always the case despite the trivial solution), it might be the case that DL-Learner needs much more time than the default 10 seconds. According to your examples: 1. has6culture.conf There is a default limit of n=5 for cardinalities, that's why your solution is never tested. We are working on a more intelligent solution. For now, you can increase the limit by op.type = "rho" op.cardinalityLimit = 10 Based on the latest source code, I get solutions: 1: hasCulture min 6 Thing (pred. acc.: 100.00%, F-measure: 100.00%) 2: hasCulture min 6 (not WaterBody) (pred. acc.: 100.00%, F-measure: 100.00%) 3: hasCulture min 6 (not Volcano) (pred. acc.: 100.00%, F-measure: 100.00%) 4: hasCulture min 6 (not View) (pred. acc.: 100.00%, F-measure: 100.00%) 5: hasCulture min 6 (not Urban) (pred. acc.: 100.00%, F-measure: 100.00%) 6: hasCulture min 6 (not TriumphalArch) (pred. acc.: 100.00%, F-measure: 100.00%) 7: hasCulture min 6 (not Tower) (pred. acc.: 100.00%, F-measure: 100.00%) 8: hasCulture min 6 (not Stadium) (pred. acc.: 100.00%, F-measure: 100.00%) 9: hasCulture min 6 (not QualityEnvironment) (pred. acc.: 100.00%, F-measure: 100.00%) 10: hasCulture min 6 (not Amphitheatre) (pred. acc.: 100.00%, F-measure: 100.00%) 2. hasOldTownOrShopping.conf You have to increase the runtime. alg.maxExecutionTimeInSeconds = 600 Moreover, it could help to disable some OWL constructs like negation and universal restrictions. op.type = "rho" op.useNegation = false //disable negation (not) op.useAllConstructor = false //disable universal restrictions (only) The output is solutions: 1: (hasActivity some OldTown or hasEnvironment some Shopping) (pred. acc.: 100.00%, F-measure: 100.00%) 2: (hasActivity some OldTown or hasEnvironment some Urban) (pred. acc.: 97.50%, F-measure: 98.04%) 3: (hasActivity some Promenade or hasEnvironment some Urban) (pred. acc.: 92.50%, F-measure: 94.34%) 4: (hasActivity some Promenade or hasEnvironment some Shopping) (pred. acc.: 92.50%, F-measure: 94.34%) 5: hasActivity min 2 (Excursion or Lazing) (pred. acc.: 70.00%, F-measure: 80.65%) 6: hasActivity min 2 (Excursion or WaterActivity) (pred. acc.: 68.75%, F-measure: 80.00%) 7: hasActivity min 2 (Excursion or Relaxation) (pred. acc.: 68.75%, F-measure: 80.00%) 8: hasActivity min 2 (Excursion or Nightlife) (pred. acc.: 68.75%, F-measure: 80.00%) 9: hasActivity min 2 (Excursion or NaturalEnvironment) (pred. acc.: 68.75%, F-measure: 80.00%) 10: hasActivity min 2 (Environment or Excursion) (pred. acc.: 68.75%, F-measure: 80.00%) 3. hasBathingMidSummer.conf The same holds for your 3rd example - this is really complex and far from easy to learn. Increase the runtime and disable some OWL constructs. Additionally, if you assume to get longer descriptions, you can set a parameter for the search heuristic like h.type ="celoe_heuristic" h.expansionPenaltyFactor = 0.02 The algorithm is still running and the output so far is (hasEnvironment some Bathing and hasWeather some (avgTemperatureC some double[>= 21.8563] and precipitationMm some double[<= 33.65835] and concernMonth some hasSeason some MidSummer)) which was found after 1min 43s 32ms. I attached all config files. Kind regards, Lorenz > Hello Lorenz, > > Thanks a lot, that helps. > > I have another problem. I have an ontology describing holiday’s > destinations. If I want to learn some simple definitions like > "hasActivity some Animation" (or any "hasObjectProperty some Concept") > it works well. But if my definitions are more complicated, it does not > work. There is no noise in my examples. > > I want to learn: > 1. "hasCulture min 6 Culture" > 2. "(hasActivity some OldTown) or (hasEnvironment some Shopping)" > 3. "(has Activity some Bathing) and (hasWeather min 2 ( > (avgTemperature some double [>=23.0]) and (precipitationMm some > double[<=70.0]) and (concernMonth some hasSeason some MidSummer)))" > > I get: > > 1. hasCulture min 5 ((not Archaeology) and (not Architecture)) (pred. > acc.: 97,50%, F-measure: 94,74%) > 2. hasActivity only (not TowedWatersport) (pred. acc.: 68,75%, > F-measure: 80,00%) > 3. (hasActivity some Excursion and hasEnvironment some WaterActivity) > (pred. acc.: 68,75%, F-measure: 73,12%) > > Could you explain why I do not get the good definitions (or at least > some definitions with 100% of accuracy)? Are my definitions too > complicated? Did I miss something? > Thank you in advance! Sorry for disturbing you again. > > Best regards, > Céline > > > > > Le 18-12-2014 18:40, Lorenz Bühmann a écrit : >> Hello Céline, >> >> there is a noise parameter called "noisePercentage" for the learning >> algorithm CELOE, so you could simply define a noise value like 20%. >> This will allow to return solutions in which 20% of the positive >> examples are not covered by the solution. >> >> Your conf file should be extended by the line: >> >> alg.noisePercentage = "20" >> >> Hope this helps. >> >> Kind regards, >> Lorenz >> >>> Hello, >>> >>> I have an ontology describing 10000 films. I have built a conf file >>> with positive and negative examples for “American films”. I want >>> to learn that an American film “isFromCountry some _US”. When I >>> run DL-Learner, it works well. Now, if I add a little bit of noise >>> (changing one positive example into negative example and vice >>> versa), it does not work anymore. “isFromCountry some Country” >>> is learned (acc=42.86%). I have 10000 films, so this noise >>> represents only 0.02% of error. >>> Is it possible to get the good definition with some noise in my >>> examples by running DL-Learner with some parameters I don’t know >>> about or does DL-Learner just not handle noise? >>> >>> Thanks in advance for your answer. I attached the files if you want >>> to make some tests. I use dllearner-1.0-beta-3 on Windows. >>> >>> Best regards, >>> Céline >>> >>> >> ------------------------------------------------------------------------------ >> >>> Download BIRT iHub F-Type - The Free Enterprise-Grade BIRT Server >>> from Actuate! Instantly Supercharge Your Business Reports and >>> Dashboards >>> with Interactivity, Sharing, Native Excel Exports, App Integration & >>> more >>> Get technology previously reserved for billion-dollar corporations, >>> FREE >>> >> http://pubads.g.doubleclick.net/gampad/clk?id=164703151&iu=/4140/ostg.clktrk >> >>> [1] >>> >>> _______________________________________________ >>> dl-learner-discussion mailing list >>> dl-...@li... >>> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion >>> [2] >> >> >> >> Links: >> ------ >> [1] >> http://pubads.g.doubleclick.net/gampad/clk?id=164703151&iu=/4140/ostg.clktrk >> >> [2] https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion >> >> ------------------------------------------------------------------------------ >> >> Download BIRT iHub F-Type - The Free Enterprise-Grade BIRT Server >> from Actuate! Instantly Supercharge Your Business Reports and Dashboards >> with Interactivity, Sharing, Native Excel Exports, App Integration & >> more >> Get technology previously reserved for billion-dollar corporations, FREE >> http://pubads.g.doubleclick.net/gampad/clk?id=164703151&iu=/4140/ostg.clktrk >> >> >> _______________________________________________ >> dl-learner-discussion mailing list >> dl-...@li... >> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion > > > ------------------------------------------------------------------------------ > Download BIRT iHub F-Type - The Free Enterprise-Grade BIRT Server > from Actuate! Instantly Supercharge Your Business Reports and Dashboards > with Interactivity, Sharing, Native Excel Exports, App Integration & more > Get technology previously reserved for billion-dollar corporations, FREE > http://pubads.g.doubleclick.net/gampad/clk?id=164703151&iu=/4140/ostg.clktrk > > > _______________________________________________ > dl-learner-discussion mailing list > dl-...@li... > https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion |
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From: alec <Cel...@lr...> - 2014-12-30 13:36:26
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Hello Lorenz, Thank you very much for all the time you spent on this! This is very helpful. Best regards, Céline Le 29-12-2014 16:03, Lorenz Bühmann a écrit : > Hello Céline, > > let me try to explain why things do not work as you would expect. > > First of all, please always keep in mind that DL-Learner is searching > for OWL class expressions that > > 1. cover as many positive examples as possible > 2. cover as less negative examples as possible > > This search space is quite large and that's why, assuming that a > perfect solution exists (this is not always the case despite the > trivial solution), it might be the case that DL-Learner needs much > more time than the default 10 seconds. > > According to your examples: > > 1. has6culture.conf > There is a default limit of n=5 for cardinalities, that's why your > solution is never tested. We are working on a more intelligent > solution. For now, you can increase the limit by > > op.type = "rho" > op.cardinalityLimit = 10 > > Based on the latest source code, I get > > solutions: > 1: hasCulture min 6 Thing (pred. acc.: 100.00%, F-measure: 100.00%) > 2: hasCulture min 6 (not WaterBody) (pred. acc.: 100.00%, F-measure: > 100.00%) > 3: hasCulture min 6 (not Volcano) (pred. acc.: 100.00%, F-measure: > 100.00%) > 4: hasCulture min 6 (not View) (pred. acc.: 100.00%, F-measure: > 100.00%) > 5: hasCulture min 6 (not Urban) (pred. acc.: 100.00%, F-measure: > 100.00%) > 6: hasCulture min 6 (not TriumphalArch) (pred. acc.: 100.00%, > F-measure: 100.00%) > 7: hasCulture min 6 (not Tower) (pred. acc.: 100.00%, F-measure: > 100.00%) > 8: hasCulture min 6 (not Stadium) (pred. acc.: 100.00%, F-measure: > 100.00%) > 9: hasCulture min 6 (not QualityEnvironment) (pred. acc.: 100.00%, > F-measure: 100.00%) > 10: hasCulture min 6 (not Amphitheatre) (pred. acc.: 100.00%, > F-measure: 100.00%) > > 2. hasOldTownOrShopping.conf > You have to increase the runtime. > > alg.maxExecutionTimeInSeconds = 600 > > Moreover, it could help to disable some OWL constructs like negation > and universal restrictions. > op.type = "rho" > op.useNegation = false //disable negation (not) > op.useAllConstructor = false //disable universal restrictions (only) > > The output is > > solutions: > 1: (hasActivity some OldTown or hasEnvironment some Shopping) (pred. > acc.: 100.00%, F-measure: 100.00%) > 2: (hasActivity some OldTown or hasEnvironment some Urban) (pred. > acc.: 97.50%, F-measure: 98.04%) > 3: (hasActivity some Promenade or hasEnvironment some Urban) (pred. > acc.: 92.50%, F-measure: 94.34%) > 4: (hasActivity some Promenade or hasEnvironment some Shopping) > (pred. acc.: 92.50%, F-measure: 94.34%) > 5: hasActivity min 2 (Excursion or Lazing) (pred. acc.: 70.00%, > F-measure: 80.65%) > 6: hasActivity min 2 (Excursion or WaterActivity) (pred. acc.: > 68.75%, F-measure: 80.00%) > 7: hasActivity min 2 (Excursion or Relaxation) (pred. acc.: 68.75%, > F-measure: 80.00%) > 8: hasActivity min 2 (Excursion or Nightlife) (pred. acc.: 68.75%, > F-measure: 80.00%) > 9: hasActivity min 2 (Excursion or NaturalEnvironment) (pred. acc.: > 68.75%, F-measure: 80.00%) > 10: hasActivity min 2 (Environment or Excursion) (pred. acc.: 68.75%, > F-measure: 80.00%) > > > 3. hasBathingMidSummer.conf > The same holds for your 3rd example - this is really complex and far > from easy to learn. Increase the runtime and disable some OWL > constructs. Additionally, if you assume to get longer descriptions, > you can set a parameter for the search heuristic like > > h.type ="celoe_heuristic" > h.expansionPenaltyFactor = 0.02 > > The algorithm is still running and the output so far is > > (hasEnvironment some Bathing and hasWeather some (avgTemperatureC > some double[>= 21.8563] and precipitationMm some double[<= 33.65835] > and concernMonth some hasSeason some MidSummer)) > > which was found after 1min 43s 32ms. > > I attached all config files. > > Kind regards, > Lorenz > >> Hello Lorenz, >> >> Thanks a lot, that helps. >> >> I have another problem. I have an ontology describing holiday’s >> destinations. If I want to learn some simple definitions like >> "hasActivity some Animation" (or any "hasObjectProperty some >> Concept") it works well. But if my definitions are more complicated, >> it does not work. There is no noise in my examples. >> >> I want to learn: >> 1. "hasCulture min 6 Culture" >> 2. "(hasActivity some OldTown) or (hasEnvironment some Shopping)" >> 3. "(has Activity some Bathing) and (hasWeather min 2 ( >> (avgTemperature some double [>=23.0]) and (precipitationMm some >> double[<=70.0]) and (concernMonth some hasSeason some MidSummer)))" >> >> I get: >> >> 1. hasCulture min 5 ((not Archaeology) and (not Architecture)) >> (pred. acc.: 97,50%, F-measure: 94,74%) >> 2. hasActivity only (not TowedWatersport) (pred. acc.: 68,75%, >> F-measure: 80,00%) >> 3. (hasActivity some Excursion and hasEnvironment some >> WaterActivity) (pred. acc.: 68,75%, F-measure: 73,12%) >> >> Could you explain why I do not get the good definitions (or at >> least some definitions with 100% of accuracy)? Are my definitions >> too complicated? Did I miss something? >> Thank you in advance! Sorry for disturbing you again. >> >> Best regards, >> Céline >> >> Le 18-12-2014 18:40, Lorenz Bühmann a écrit : >> Hello Céline, >> >> there is a noise parameter called "noisePercentage" for the >> learning >> algorithm CELOE, so you could simply define a noise value like 20%. >> >> This will allow to return solutions in which 20% of the positive >> examples are not covered by the solution. >> >> Your conf file should be extended by the line: >> >> alg.noisePercentage = "20" >> >> Hope this helps. >> >> Kind regards, >> Lorenz >> >> Hello, >> >> I have an ontology describing 10000 films. I have built a conf file >> >> with positive and negative examples for “American films”. I >> want >> to learn that an American film “isFromCountry some _US”. When I >> >> run DL-Learner, it works well. Now, if I add a little bit of noise >> (changing one positive example into negative example and vice >> versa), it does not work anymore. “isFromCountry some Country” >> is learned (acc=42.86%). I have 10000 films, so this noise >> represents only 0.02% of error. >> Is it possible to get the good definition with some noise in my >> examples by running DL-Learner with some parameters I don’t know >> about or does DL-Learner just not handle noise? >> >> Thanks in advance for your answer. I attached the files if you want >> >> to make some tests. I use dllearner-1.0-beta-3 on Windows. >> >> Best regards, >> Céline >> >> > ------------------------------------------------------------------------------ >> >> Download BIRT iHub F-Type - The Free Enterprise-Grade BIRT Server >> from Actuate! Instantly Supercharge Your Business Reports and >> Dashboards >> with Interactivity, Sharing, Native Excel Exports, App Integration >> & >> more >> Get technology previously reserved for billion-dollar corporations, >> >> FREE >> >> > http://pubads.g.doubleclick.net/gampad/clk?id=164703151&iu=/4140/ostg.clktrk >> [1] >> [1] >> >> _______________________________________________ >> dl-learner-discussion mailing list >> dl-...@li... >> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion >> [2] >> [2] >> >> Links: >> ------ >> [1] >> > http://pubads.g.doubleclick.net/gampad/clk?id=164703151&iu=/4140/ostg.clktrk >> [3] >> [2] >> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion >> [2] >> >> > ------------------------------------------------------------------------------ >> >> Download BIRT iHub F-Type - The Free Enterprise-Grade BIRT Server >> from Actuate! Instantly Supercharge Your Business Reports and >> Dashboards >> with Interactivity, Sharing, Native Excel Exports, App Integration >> & more >> Get technology previously reserved for billion-dollar corporations, >> FREE >> > http://pubads.g.doubleclick.net/gampad/clk?id=164703151&iu=/4140/ostg.clktrk >> [1] >> >> _______________________________________________ >> dl-learner-discussion mailing list >> dl-...@li... >> https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion >> [2] > > ------------------------------------------------------------------------------ > Download BIRT iHub F-Type - The Free Enterprise-Grade BIRT Server > from Actuate! Instantly Supercharge Your Business Reports and > Dashboards > with Interactivity, Sharing, Native Excel Exports, App Integration & > more > Get technology previously reserved for billion-dollar corporations, > FREE > http://pubads.g.doubleclick.net/gampad/clk?id=164703151&iu=/4140/ostg.clktrk > [1] > > _______________________________________________ > dl-learner-discussion mailing list > dl-...@li... > https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion [2] > > > > Links: > ------ > [1] > http://pubads.g.doubleclick.net/gampad/clk?id=164703151&iu=/4140/ostg.clktrk > [2] https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion > [3] > http://pubads.g.doubleclick.net/gampad/clk?id=164703151&amp;iu=/4140/ostg.clktrk > > ------------------------------------------------------------------------------ > Dive into the World of Parallel Programming! The Go Parallel Website, > sponsored by Intel and developed in partnership with Slashdot Media, is > your > hub for all things parallel software development, from weekly thought > leadership blogs to news, videos, case studies, tutorials and more. > Take a > look and join the conversation now. http://goparallel.sourceforge.net > > _______________________________________________ > dl-learner-discussion mailing list > dl-...@li... > https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion |
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From: alec <Cel...@lr...> - 2015-01-06 10:50:37
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Hello Lorenz, Thank you again for your answer. I wish you a happy new year. I am sorry to disturb you again with some new questions… 1. Is there a parameter to display more than the 10 first best solutions? For example, to display all the solutions with the same accuracy and f-measure as the first solution. 2. What is the criteria for the order of the answers (besides accuracy and f-measure)? If there are several solutions with the same accuracy/f-measure, how do you choose what solution to put in the first position? I think it is related to the readability, but could you explain a bit more please? I am asking this because I am confused with 2 things: - If I take the “hasCulture min 6 Thing” with the parameters you have added: the second solution is “hasCulture min 6 (not WaterBody)” and the next ones are all like “hasCulture min 6 (not Concept_Name)”. I don’t understand why “hasCulture min 6 Culture” is not at least in the second position. I mean, “Culture” is more readable than “(not Concept_name)”, isn’t it? - Moreover, in this context, “hasCulture min 6 Culture” and “hasCulture min 6 Thing” are equivalent because Culture is the range of hasCulture. But in my opinion, it would be more adapted to get “hasCulture min 6 Culture” before “hasCulture min 6 Thing”. I mean, I would rather say to an expert that a cultural destination is a destination which hasCulture at least “6 instances of Culture” rather than “6 instances of Thing”. Of course, this is only a matter of readability for an expert, since saying “Thing” or “Culture” does not change anything if the ontology is consistent. 3. Is there a way to avoid to get solutions with the same accuracy/f-measure as one solution but which are much more complex for no reason? For example, if I disable the “not” and “only” with the “hasCulture min 6 Thing” example, I get: 1: hasCulture min 6 Thing 2: Destination and hasCulture min 6 Thing 3: hasCulture min 6 Thing and (Destination or Weather) 4: hasCulture min 6 Thing and (Destination or Season) Etc All of these definitions are 100% correct. But what is the point to get the “or Weather” / “or Season” part? If I say “hasCulture min 6 Thing and (Destination or ConceptA or ConceptB or …)”, of course I can add anything with “or”, it would not change the accuracy but I don’t see the point. Thanks a lot, Céline |
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From: Lorenz B. <spo...@st...> - 2015-01-16 12:03:16
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Hello Céline, there is no need to apologize. This is the official DL-Learner discussion list and we're always happy when we can help people in using DL-Learner or answer questions. 1. Yes, at least for the learning algorithm CELOE there is a parameter called "maxNrOfResults" which limits the number of returned solutions. I'm not sure if I understand the second point, do you ask for another option that handles all solution with the same accuracy as one solution, thus, you want e.g. n distinct solutions with different score? 2. The solutions are sorted by: accuracy > length > class expression type We use the length as second criteria because of readability and simplicity, as you already assumed. Indeed there might be other sorting priorities (probably depending on the use-case and/or dataset), but the main focus is on the accuracy. If you have any other in mind, let us know and we will think about it. “hasCulture min 6 Culture” is not in the solution list, because it's follows logically from the knowledge base itself. We have an optional parameter that rewrites the class expressions exactly like in your example, but that's unfortunately not yet available in the conf files. I've open a feature request [1]. 3. You're right. This is not very efficient and makes the results more confusing. I wouldn't say that this is a bug, but definitely needs to be avoided. I opened a ticket [2]. Kind regards, Lorenz [1] https://github.com/AKSW/DL-Learner/issues/2 [2] https://github.com/AKSW/DL-Learner/issues/3 > Hello Lorenz, > > Thank you again for your answer. I wish you a happy new year. I am sorry > to disturb you again with some new questions… > > 1. Is there a parameter to display more than the 10 first best > solutions? For example, to display all the solutions with the same > accuracy and f-measure as the first solution. > > > 2. What is the criteria for the order of the answers (besides accuracy > and f-measure)? > If there are several solutions with the same accuracy/f-measure, how do > you choose what solution to put in the first position? > I think it is related to the readability, but could you explain a bit > more please? > > I am asking this because I am confused with 2 things: > > - If I take the “hasCulture min 6 Thing” with the parameters you have > added: the second solution is “hasCulture min 6 (not WaterBody)” and the > next ones are all like “hasCulture min 6 (not Concept_Name)”. I don’t > understand why “hasCulture min 6 Culture” is not at least in the second > position. I mean, “Culture” is more readable than “(not Concept_name)”, > isn’t it? > > - Moreover, in this context, “hasCulture min 6 Culture” and “hasCulture > min 6 Thing” are equivalent because Culture is the range of hasCulture. > But in my opinion, it would be more adapted to get “hasCulture min 6 > Culture” before “hasCulture min 6 Thing”. I mean, I would rather say to > an expert that a cultural destination is a destination which hasCulture > at least “6 instances of Culture” rather than “6 instances of Thing”. Of > course, this is only a matter of readability for an expert, since saying > “Thing” or “Culture” does not change anything if the ontology is > consistent. > > > 3. Is there a way to avoid to get solutions with the same > accuracy/f-measure as one solution but which are much more complex for > no reason? > > For example, if I disable the “not” and “only” with the “hasCulture min > 6 Thing” example, I get: > 1: hasCulture min 6 Thing > 2: Destination and hasCulture min 6 Thing > 3: hasCulture min 6 Thing and (Destination or Weather) > 4: hasCulture min 6 Thing and (Destination or Season) > Etc > > All of these definitions are 100% correct. > But what is the point to get the “or Weather” / “or Season” part? If I > say “hasCulture min 6 Thing and (Destination or ConceptA or ConceptB or > …)”, of course I can add anything with “or”, it would not change the > accuracy but I don’t see the point. > > > Thanks a lot, > Céline > > > ------------------------------------------------------------------------------ > Dive into the World of Parallel Programming! The Go Parallel Website, > sponsored by Intel and developed in partnership with Slashdot Media, is your > hub for all things parallel software development, from weekly thought > leadership blogs to news, videos, case studies, tutorials and more. Take a > look and join the conversation now. http://goparallel.sourceforge.net > _______________________________________________ > dl-learner-discussion mailing list > dl-...@li... > https://lists.sourceforge.net/lists/listinfo/dl-learner-discussion |
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