Text Based Similarity Metrics and Delta for Semantic Web Graphs Krishnamurthy Koduvayur Viswanathan...

Text Based Similarity Metrics and Delta for Semantic Web Graphs

Krishnamurthy Koduvayur ViswanathanMonday, June 28, 2010

1

Contributions

• Define text-based similarity metrics that characterize the relationship between semantic web graphs

• Evaluate the similarity metrics for three specific cases of similarity that we defined

• Generate a delta between pairs of SW graphs that may be two versions of the same graph

• Prototyped the techniques in a new system called Similis

2Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

Motivation: Near Duplicate Detection for the SW?


Goals

• Explore the different ways in which two SW graphs may be similar to each other

• In particular, evaluate the specific use case of versioning relations between SW graphs

• Additionally, develop techniques to generate a delta between versions


Comparison with near duplicate text document detection

• In a text document:– Order of the content is important– The meaning of the text is not a part of the problem, just

the textual encoding of the meaning

• For a SWD, the order is not deterministic i.e. equivalent SWDs may have different statement orderings

• Non-deterministic blank node identifiers


Semantic Web Document (SWD)

• RDF representation of a Semantic Web Graph– Document based serialization of a SW graph on

the web (ontology or data-file)– Document based serialization of the result of a

SPARQL query on a triple-store– Document based serialization of structured

metadata extracted from an HTML page using RDFa


Semantic Web Graph Similarity

• The archive or the Swoogle search engine (Ding et al. 2004) shows several examples of how ontologies and RDF documents evolve over time

• Kinds of similarity between two SW graphs:– Same classes and properties used. Differ only in literal

content– Different only in base-URIs of entities used– Different versions of the same semantic web graph


Similarity in Classes and Properties• Two semantic web graphs that differ only in the

literal content


Different in Literal Content<http://www.w3.org/People/EM/contact#me > <http://www.w3.org/1999/02/22-ref-syntax-

ns#type> <http://www.w3.org/2000/10/swap/pim/contact#Person> .<http://www.w3.org/People/EM/contact#me >

<http://www.w3.org/2000/10/swap/pim/contact#fullName> “Eric Miller” .<http://www.w3.org/People/EM/contact#me >

<http://www.w3.org/2000/10/swap/pim/contact#mailbox> “mailto:[email protected]“ .<http://www.w3.org/People/EM/contact#me >

<http://www.w3.org/2000/10/swap/pim/contact#personalTitle> “Dr” . <http://www.w3.org/People/EM/contact#me > <http://www.w3.org/1999/02/22-ref-syntax-

ns#type> <http://www.w3.org/2000/10/swap/pim/contact#Person> .<http://www.w3.org/People/EM/contact#me >

<http://www.w3.org/2000/10/swap/pim/contact#fullName> “John Doe” .<http://www.w3.org/People/EM/contact#me >

<http://www.w3.org/2000/10/swap/pim/contact#mailbox> “mailto:[email protected] “ .<http://www.w3.org/People/EM/contact#me >

<http://www.w3.org/2000/10/swap/pim/contact#personalTitle> “Mr” .


Different only in base-URI


Different only in base-URI<http://www.w3.org/2001/sw/WebOnt/guide-src/wine#ItalianRegion> ._:g103 <http://www.w3.org/2002/07/owl#onProperty>

<http://www.w3.org/2001/sw/WebOnt/guide-src/wine#locatedIn> ._:g104 <http://www.w3.org/1999/02/22-rdf-syntax-ns#first> _:g103 ._:g104 <http://www.w3.org/1999/02/22-rdf-syntax-ns#rest> <http://www.w3.org/1999/02/22-rdf-syntax-ns#nil> ._:g105 <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/2002/07/owl#Restriction> ._:g105 <http://www.w3.org/2002/07/owl#hasValue> <http://www.w3.org/2001/sw/WebOnt/guide-src/wine#Dry> ._:g105 <http://www.w3.org/2002/07/owl#onProperty>

<http://www.w3.org/2001/sw/WebOnt/guide-src/wine#hasSugar> .

<http://www.w3.org/TR/2003/PR-owl-guide-20031209/wine#ItalianRegion> ._:g103 <http://www.w3.org/2002/07/owl#onProperty>

<http://www.w3.org/TR/2003/PR-owl-guide-20031209/wine#locatedIn>._:g104 <http://www.w3.org/1999/02/22-rdf-syntax-ns#first> _:g103 ._:g104 <http://www.w3.org/1999/02/22-rdf-syntax-ns#rest> <http://www.w3.org/1999/02/22-rdf-syntax-ns#nil> ._:g105 <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://www.w3.org/2002/07/owl#Restriction> ._:g105 <http://www.w3.org/2002/07/owl#hasValue>

<http://www.w3.org/TR/2003/PR-owl-guide-20031209/wine#Dry> ._:g105 <http://www.w3.org/2002/07/owl#onProperty>

<http://www.w3.org/TR/2003/PR-owl-guide-20031209/wine#hasSugar> .


Versioning Relationship

• Two semantic web documents have a versioning relationship, if they are variants of the same semantic web graph.

• Variants are created due to the dynamic nature of the web, i.e. content keeps getting modified– Minor changes: spelling corrections, punctuations etc– Major changes: Affect the semantic content


Problem Definition

• Problem 1: Given a collection of semantic web graphs in the form of RDF documents, characterize the similarity between pairs into one or more of the three cases:– Same classes and properties used, but differ only in the

literal content– Differ only in the base-URI used– Are different versions of the same graph i.e. have a

versioning relationship


Problem Definition

• Problem 2: Generate a delta between pairs that have been identified as having a versioning relationship between them.


ApproachInput: Corpus of SWDs

Convert to n-triples format

Convert to canonical form

Generate Reduced Forms

Compute Text-Based Similarity Metrics

Characterize similarity between pairs

Identify versions

Generate delta between versions

Build feature-vectors for each pair


Convert to n-triples


Convert to Canonical Form• Comparison methods may be affected by blank node

identifiers and statement ordering

• Canonicalization assigns consistent IDs to blank nodes and orders the statements lexicographically.

• Transforms two semantically equivalent graphs into the same canonical representation

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Based on: Carroll, J. J. 2003. Signing RDF graphs. In In 2nd ISWC, volume 2870 of LNCS, 5–15. Springer.

Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

Convert to Canonical Form

<person:John> <a:livesIn> _:x ._:x <a:IsPartOf> ”USA” .<person:John> <a:likes> ”cheese” ._:x <a:hasCapital> :y .

“~” <a:hasCapital> “~” . # _:x _:y“~” <a:IsPartOf> ”USA” . # _:x<person:John> <a:likes> ”cheese” .<person:John> <a:livesIn> “~” . #_:x

Old Blank Node Identifier

New Blank Node Identifier

_:y _:g1

_:x _:g2

_:g2 <a:hasCapital> _:g1 . _:g2 <a:IsPartOf> ”USA” . <person:John> <a:likes> ”cheese” .<person:John> <a:livesIn> _:g2 .

BNode Table


Limitation of the Algorithm: Non-Distinctive Triples

• The algorithm can only deal with graphs that do not have non-distinctive triples

• Non Distinctive Triples: The triples in the graph that cannot be uniquely identified when all the blank nodes are treated as equal


Graphs with Non-Distinctive Triples

• For a group of n non-distinct triples, there are n! ways of renaming the blank nodes

• For graphs with non-distinctive triples, a single unique canonical form does not exist

• To compare two graphs, compare each of the possible canonical forms for both graphs

• Number of comparisons: O(m!n!)• Similis throws an exception when it finds a graph

with multiple forms


Graphs with Non-Distinctive Triples• Only a small percentage of SW graphs (13%) did not

have a unique canonical form (1200 randomly collected SW documents)


Generating Reduced Forms• The canonical form of each SW graph is broken down

into a number of reduced forms• These reduced forms are used to characterize the

relationship between pairs of SW graphs• The following is the anatomy of a triple:

Entity URI <http://www.w3.org/2001/sw/guide-src/wine#hasSugar>

Base URI <http://www.w3.org/2001/sw/guide-src/wine>

Local Name <hasSugar>


Only-Literals Reduced Form• Contains only the literals from the original n-triples

file.• Lets us compare only the textual content within a

graph, separated from the rest of the graph


No-Literals Reduced Form• All the literals from the canonical form are replaced

by an empty string• Lets us compare only the classes and properties

used, regardless of literal content


Local-Name Reduced Form• The base-URI of every node in the canonical form is

replaced by an empty string• Lets us compare only the local names of the classes

and properties used


Local-Name-No-Literal Reduced Form• All the literals, and the base-URI of every node is

replaced by an empty string• Lets us compare the non-literal content of two SW

graphs


Similarity/Distance Metrics Used

• Cosine Similarity between SWD vectors• Jaccard and Containment Metrics• Hamming Distance between Simhash fingerprints


Computation of Pairwise Metrics

• Compute cosine similarity between the canonical, and local forms of each pair in the collection– If cosine similarity < 0.7, remove pair from further

consideration– Else, compute all other metrics for all the forms (5 forms *

3 metrics = 15 specific metrics)

• Total of 17 metrics computed


Cosine Similarity Between Term Vectors

• Each SWD containing terms Tj = {t1, t2…tn} is treated as a vector Vj = (γ1t1,γ2t2,… γntn) where each γi is the weight associated with term ti

• Non-blank, non-literal nods are used as features, and Term Frequency (TF) is used as weight

• Two vectors for each SWD: one uses full entity URIs as features, other uses local-name of terms

• Indicates similarity in classes and properties


SW Document Vectors

Term Freq

<http://purl.org/dc/elements/1.1/title> 2

<http://purl.org/dc/elements/1.1/creator> 1

<http://purl.org/dc/elements/1.1/contributor> 1

<http://put-off.org> 1

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Term Freq

<title> 2

<creator> 2

<contributor> 1

<put-off.org> 1


Jaccard and Containment

• Computed for all forms (five) for a candidate pair of SW graphs (5 * 2 = 10 metrics)

• Construct sets of character 4-grams for each document

• 4-grams are computed by running a four character-wide window over the text representation


Hamming Distance between Simhash Fingerprints

• Simhash fingerprints of similar documents differ in a small number of bit positions

• Tokenize documents into character 3-grams• Compute simhash fingerprint for each document in

pair (we implemented 128 bit fingerprints)• Find Hamming Distance between the fingerprints• Computed for all forms (five) for a candidate pair of

SW graphs (5 * 1 = 5 metrics)


Classification

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Naïve Bayes Classifier:

Similarity in classes and properties

Similarity metrics

computed for each

candidate pair

Naïve Bayes/SVM classifier:

Difference only in Base-URI

SVM Classifier: Versioning

Relationship

Feature Vector

FV

Feature Vector


Example feature vector used for determining versioning relationship

Computing Delta Between Two Versions

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Version1

Except Version2

Subtractive Delta

Version2Except Version1

Additive Delta

Version1

Version2

Delta

SVM Classifier: Versioning

Relationship


Raw Delta• Statement-by-statement comparison between

canonical forms of the two SWDs• Only local names of entities are compared


Delta After Deductive Closure

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SWGv1

SWGv2

Compute deductive closure

Compute deductive closure

Canonicalize

Canonicalize

Generate Raw Delta


Delta After Deductive Closure• If O is a set of propositions, p ԑ O and p q╞ , then q ԑ

O


Delta at Concept Level


Delta at Concept Level

• Works only for ontologies• Groups of class/property definitions are serialized

into individual graphs• Corresponding graphs in the two versions are

compared to each other


Concept Level Delta: example


Detecting Class Renaming

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Sauterne

Sauterne

Sauterne

Sauterne

Sauterne

Sauterne



Input: Local names of entites in both diffs

Generate 3-gram sets for each entity

Compute 3-gram overlap between sets in additive and subtractive deltas

If overlap > 0.7, add (oldname, newname) to candidate set

Replace oldname in subtractive delta by newname

Check for presence of all modified statements in additive delta


Data-set: Using Swoogle’s SW Wayback machine

• Swoogle caches multiple snapshots for each indexed semantic web document

• Labeling for versions: We extract such snapshots from Swoogle’s cache and label these pairs as versions


Evaluation: Pairs that Differ in Literal Content

• Features used for classification:– LocalNameCosineSim– CosineSim– LocalNameNoLiteralJaccard– LocalNameNoLiteralSimhash

• Training set from Swoogle archive included 806 positive pairs, and 806 negative pairs



• Results of 10-fold stratified cross validation using a Naïve Bayes classifier:



• Results of using a SVM with all of the features, instead of manually selecting features:

• Attribute relevance ranking:


Evaluation: Pairs that Differ in Base-URI

• Features for classification:– CosineSim– LocalNameCosineSim– LocalNameNoLiteralJaccard– LocalNameNoLiteralContainment– OnlyLiteralContainment– OnlyLiteralJaccard

• Training set contained 100 positive examples, and 100 negative examples


Evaluation: Pairs that Differ in Base-URI

• 10-fold cross validation using Naïve Bayes:

• 10-fold cross validation (SVM linear-kernel)


Evaluation: Pairs with a Versioning Relationship

• 124 training instances from Swoogle data-set

• Filtered highly dynamic pairs from consideration


Evaluation: Pairs with a Versioning Relationship

• Test dataset: 160 instances (50% +ve 50% -ve)• Classification results using SVM (linear kernel)


Correctness of Delta Computation

• For any two versions of a SW graph, it holds that Δx(K → K’)K ≡ K’

• We check this condition programmatically for each delta generated


Conclusion

• Define text-based similarity metrics that characterize the relationship between semantic web graphs

• Evaluate the similarity metrics for three specific cases of similarity that we defined

• Generate deltas between pairs of SW graphs that may be two versions of the same graph

• Prototyped the techniques in a new system called Similis


Future Directions

• Scalability• Content of Delta Generated• Standard Ontologies to:– Describe delta– Describe the relationship between a pair of SW

graphs

• Detecting direction of change between two versions


Text Based Similarity Metrics and Delta for Semantic Web Graphs Krishnamurthy Koduvayur Viswanathan...

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