[DL-Learner SVN] SF.net SVN: dl-learner: [94] trunk

[<jen...@us...>]

Revision: 94
          http://dl-learner.svn.sourceforge.net/dl-learner/?rev=94&view=rev
Author:   jenslehmann
Date:     2007-08-28 07:54:28 -0700 (Tue, 28 Aug 2007)

Log Message:
-----------
created README and INSTALL release files

Modified Paths:
--------------
    trunk/INSTALL
    trunk/README

Modified: trunk/INSTALL
===================================================================
--- trunk/INSTALL	2007-08-28 14:38:05 UTC (rev 93)
+++ trunk/INSTALL	2007-08-28 14:54:28 UTC (rev 94)
@@ -1 +1,37 @@
- 
+Requirements
+============
+Java 6 and a reasoner is required.
+
+Installation
+============
+No further installation is necessary. Just download and extract DL-Learner.
+
+Running Instructions
+====================
+The instructions assume that you have a usable reasoner running (usually a
+DIG capable reasoner running on http://localhost:8081).
+
+Linux (and other systems)
+
+Make sure the file "dllearner" is executable and run it on the command line 
+followed by the name of the file to process, e.g. 
+"./dllearner examples/father.conf".
+
+Windows:
+
+Run "dllearner.bat" followed by the name of the file to process, e.g. 
+"dllearner.bat examples/father.conf".
+
+All:
+
+You can use the "java" command to run the program directly. Have a look at 
+"dllearner" and "dllearner.bat" to see what the command looks like.
+
+Web-Service
+===========
+
+If you want to run DL-Learner as a Web Service, you can run the scripts "ws" or
+"ws.bat". They create an own local webserver running DL-Learner. An example
+PHP client is also provided by DL-Learner. It can be downloaded at the 
+sourceforge.net project website:
+http://sourceforge.net/project/showfiles.php?group_id=203619

Modified: trunk/README
===================================================================
--- trunk/README	2007-08-28 14:38:05 UTC (rev 93)
+++ trunk/README	2007-08-28 14:54:28 UTC (rev 94)
@@ -1,319 +1,14 @@
-DL-Learner ist a program to learn Description Logic concepts from positive and 
-negative examples.
-
-Requirements
-============
-
-Java 6 is required. No installation is necessary. Just download and extract
-DL-Learner.
-
-Instructions
-============
-
-Linux (and other systems)
-
-Make sure the file "dllearner" is executable and run it on the command line 
-followed by the name of the file to process, e.g. 
-"./dllearner examples/father.conf".
-
-Windows:
-
-Run "dllearner.bat" followed by the name of the file to process, e.g. 
-"dllearner.bat examples/father.conf".
-
-All:
-
-You can use the "java" command to run the program directly. Have a look at 
-"dllearner" and "dllearner.bat" to see what the command looks like.
-
-Syntax
+README
 ======
 
-The background knowledge can either be given as OWL DL file (using the import
-function in the configuration files) or by specifying it directly in the 
-configuration file (which we refer to as the internal knowledge base).
+DL-Learner ist a tool to learn Description Logic concepts from positive and 
+negative examples. Equivalently, it can learn OWL classes given objects as
+positive and negative instances.
 
-Some examples of the syntax of the background knowledge in the internal 
-knowledge base:
+Homepage: http://dl-learner.org
+Sourceforge.net Project Page: http://sourceforge.net/projects/dl-learner/
+Bugs & Feature Requests: http://sourceforge.net/tracker/?group_id=203619
+Mailing Lists: http://sourceforge.net/mail/?group_id=203619
+Latest Release: http://sourceforge.net/project/showfiles.php?group_id=203619
 
-person = (male OR female).
-mother = (female AND EXISTS hasChild.TOP).
-motherManyDaughters = (female AND >= 4 hasChild.female).
-(mother AND father) SUBCLASSOF person.
-
-Also see the example files.
-
-This is the EBNF description of the input language [slightly outdated]:
-
-Number = ["1"-"9"] (["0"-"9"])*
-Id = ["a"-"z"] (["_","a"-"z","A"-"Z","0"-"9"])*
-String: "\"" (~["\"","\\","\n","\r"])* "\""
-Instruction =   ConfOption 
-              | FunctionCall 
-              | PosExample 
-              | NegExample 
-              | ABoxConcept 
-              | ABoxRole 
-              | Transitive 
-              | Functional 
-              | Symmetric 
-              | Inverse 
-              | Subrole 
-              | TBoxEquiv 
-              | TBoxSub
-ConfOption = Id [ "." Id ] "="  ( Id | Number ) ";"
-FunctionCall = Id "(" String ")" ";"
-PosExample = "+" Id "(" Id ")" "."
-NegExample = "-" Id "(" Id ")" "."
-ABoxConcept = Concept "(" Id ")" "."
-ABoxRole = Id "(" Id "," Id ")" "."
-Transitive = "Transitive" "(" Id ")" "."
-Functional = "Functional" "(" Id ")" "."
-Symmetric = "Symmetric" "(" Id ")" "."
-Inverse = "Inverse" "(" Id "," Id ")" "."
-Subrole = "Subrole" "(" Id "," Id ")" "."
-TBoxEquiv = Concept "=" Concept "."
-TBoxSub = Concept ("SUBCLASSOF" | "SUB" ) Concept "."
-Concept =   "TOP"
-          | "BOTTOM"
-          | Id
-          | "(" Concept "AND" Concept ")"
-          | "(" Concept "OR" Concept ")"
-          | "EXISTS" Id "." Concept
-          | "ALL" Id "." Concept
-          | "NOT" Concept
-          | ">=" Number Id "." Concept
-          | "<=" Number Id "." Concept
-
-Configuration Options
-=====================
-
-General
--------
-
-Option: algorithm
-Possible Values: bruteForce, gp, random, refinement, hybridGP
-Default: refinement
-Effect: Specifies the algorithm to use for solving the learning problem. Note,
-        that hybridGP is not an algorithm itself, but starts the GP algorithm
-        with a sensible set of default values for the hybrid algorithm combining
-        GP with refinement operators. In particular the probability of all
-        operators except refinement is set to 0.
-
-Option: reasoner
-Possible Values: dig, kaon2, fastRetrieval
-Default: dig
-Effect: Specifies the reasoner to be used. DIG communicates with a reasoner
-        using the DIG Interface. KAON2 means to use the KAON2 Java API directly.
-        FastRetrieval is an internal algorithm, which can only be used for 
-        retrieval (not for subsumption). Currently the DIG reasoner cannot read
-        OWL files.
-
-Option: digReasonerURL
-Possible Values: a valid URL
-Default: http://localhost:8081
-Effect: Specifies the URL to be used to look for a DIG capable reasoner.
-
-Option: writeDIGProtocol
-Possible Values: true, false
-Default: false
-Effect: Specifies whether to store all DIG communication.
-
-Option: digProtocolFile
-Possible Values: strings
-Default: digProtocol.txt
-Effect: The file to store all DIG communication if writeDIGProtocol is true.
-
-Option: useRetrievalForClassification
-Possible Values: true, false
-Default: false
-Effect: To measure which concepts are covered, one can either use one retrieval
-        or several instance checks (at most one for each example). This option
-        controls which of both options should be used.
-
-Option: percentPerLengthUnit
-Possible Values: 0-1
-Default: 0.05
-Effect: How much percent (wrt classification accuracy) can a concept be worse to
-        justify an increase in length of 1. This variable is used for GP and in
-        refinement when the flexible heuristic is used. For GP, you should use a
-        value smaller than the default.
-
-> general options below are ignored <
-> by the refinement operator algorithm <
-
-Option: accuracyPenalty
-Possible Values: 1-1000
-Default: 1
-Effect: Sets the penalty for "small misclassifications".
-
-Option: errorPenalty
-Possible Values: 1-1000
-Default: 3
-Effect: Sets the penalty for classification errors.
-
-Option: maxLength
-Possible Values: 1-20
-Default: 7
-Effect: For the brute force learner this specifies the depth limit for the
-        search. The GP learner currently ignores it.
-
-Option: scoreMethod
-Possible Values: full, positive
-Default: positive
-Effect: The positive score method ignores if a negative examples cannot be
-        classified. This is often usefull, because of the limited expressiveness
-        of SHIQ wrt. negated role assertions. The full method penalizes this.
-		
-Option: showCorrectClassifications
-Possible Values: true, false
-Default: false
-Effect: Controls if correct classifications are printed (does not effect the
-        algorithm).
-		
-Option: penalizeNeutralExamples
-Possible Values: true, false
-Default: false
-Effect: If true there is a penalty if a neutral (neither positive nor negative)
-        individual is classified as either positive or negative. This should
-        usually be set to false.
-
-Refinement Operator Algorithm Specific
---------------------------------------
-
-Option: refinement.horizontalExpansionFactor
-Possible Values: 0-1
-Default: 0.6
-Effect: Specifies horizontal expansion factor.
-
-Option: refinement.writeSearchTree
-Possible Values: true, false
-Default: false
-Effect: Specifies whether to write the search tree to a file.
-
-Option: refinement.searchTreeFile
-Possible Values: strings
-Default: "searchTree.txt"
-Effect: Specifies a file to save the current search tree after each loop of
-        the refinement algorithm.
-
-Option: refinement.heuristic
-Possible Values: flexible, lexicographic
-Default: lexicographic
-Effect: The refinement operator together with a heuristic yields a learning
-        algorithm. The lexicographic heuristic uses a lexicographic order of
-        covered negative examples and horizontal expansion of a node (i.e.
-        the covered examples are the first criterion, the horizontal expansion
-        the second criterion). The flexible heuristic computes a combined node
-        score of both criteria. Note, that the lexicographic needs a horizontal
-        expansion factor greater than 0 to ensure correctness of the learning
-        algorithm.
-
-Option: refinement.quiet
-Possible Values: true, false
-Default: false
-Effect: If set to true, no messages will be shown during the run of the 
-        algorithm (but there will still be startup and summary messages).
-
-Option: refinement.applyAllFilter
-Possible Values: true, false
-Default: true
-Effect: Specifies wether all equivalences should be used.
-
-Option: refinement.applyExistsFilter
-Possible Values: true, false
-Default: true
-Effect: Specifies wether exists equivalences should be used.
-
-Option: refinement.useTooWeakList
-Possible Values: true, false
-Default: true
-Effect: Specifies wether a too weak list should be used to reduce reasoner
-        requests.
-
-Option: refinement.useOverlyGeneralList
-Possible Values: true, false
-Default: true
-Effect: Specifies wether an overly general list should be used to reduce 
-        reasoner requests.
-
-Option: refinement.useShortConceptConstruction
-Possible Values: true, false
-Default: true
-Effect: Specifies wether the algorithm should try to reduce a concept to a
-        known more general concept to reduce the number of necessary 
-        subsumption checks for the reasoner.
-
-Option: refinement.useDIGMultiInstanceChecks
-Possible Values: never, twoChecks, oneCheck
-Default: twoChecks
-Effect: The DIG protocol allows to send several queries to a DIG reasoner at
-        once. [This is automatically done for subsumption tests.] However,
-        for instance checks this has the disadvantage that it may not be 
-        necessary to send all instance to the DIG reasoner if one of the 
-        positive examples is not covered (meaning that the concept is 
-        classified as too weak). 
-        If the option is set to never, then each instance check is send 
-        separately.
-        If the option is set to twoChecks, then first all positive examples will
-        be send in one query. If all of them are covered, i.e. the concept is
-        not classified as too weak, then all the negative examples are send in
-        one query.
-        If the option is set to oneCheck, then all examples will be send in one
-        query.
-
-Genetic Programming Specific
-----------------------------
-
-Option: gp.algorithmType
-Possible Values: steadyState, generational
-Default: steadyState
-Effect: Uses either a steady state (population partly replaced) or generational
-        (population completely replaced) algorithm.
-		
-Option: gp.elitism
-Possible Values: true, false
-Default: true
-Effect: If true an the GP algorithm uses elitism, i.e. the best individual is
-        guarenteed to survive.
-		
-Option: gp.numberOfIndividuals
-Possible Values: 1-1000000
-Default: 1000
-Effect: Sets the number of individuals in the population. A higher value
-        improves classification, but is computationally more expensive.
-		
-Option: gp.numberOfSelectedIndividuals
-Possible Values: 1-1000000
-Default: 960
-Effect: Sets the number of individuals, which are selected for replacement in a
-        steady state GP algorithm.
-		
-Option: gp.crossoverPercent
-Possible Values: 0-100
-Default: 95
-Effect: The probability that offspring is produced using crossover (in contrast
-        to simply being copied over to the next generation).
-		
-Option: gp.mutationPercent
-Possible Values: 0-100
-Default: 3
-Effect: The probability that offspring is mutated after reproduction.
-
-Option: gp.hillClimbingPercent
-Possible Values: 0-100
-Default: 0
-Effect: The probability that offspring is produced using the hill climbing
-        operator.
-
-Option: gp.refinementPercent
-Possible Values: 0-100
-Default: 0
-Effect: The probability that offspring is produced using the genetic refinement
-        operator.
-
-Option: gp.postConvergenceGenerations
-Possible Values: 10-1000
-Default: 50
-Effect: If the algorithm does not find a better solution for this number of
-        generations it stops.
\ No newline at end of file
+DL-Learner is Open Source and licenced unter the GNU General Public License.


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