1. Text Based Similarity Metrics and Delta for Semantic Web Graphs Krishnamurthy Koduvayur Viswanathan Monday, June 28, 2010 1

2. 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 2 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

3. Motivation: Near Duplicate Detection for the SW? 3 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

4. 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 4 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

5. 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 5 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

6. 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 6 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

7. 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 7 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

8. Similarity in Classes and Properties Two semantic web graphs that differ only in the literal content 8 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

9. Different in Literal Content. “Eric Miller”. “mailto:em@w3.org“. “Dr”.. “John Doe”. “mailto:jd@w3.org “. “Mr”. 9 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

10. Different only in base-URI 10 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

11. Different only in base-URI. _:g103. _:g104 _:g103. _:g104. _:g105.. _:g103. _:g104 _:g103. _:g104. _:g105. 11 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

12. 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 12 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

13. 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 13 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

14. Problem Definition Problem 2: Generate a delta between pairs that have been identified as having a versioning relationship between them. 14 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

15. Approach Input: 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 15 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

16. Convert to n-triples 16 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

17. 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 17 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

18. Convert to Canonical Form _:x. _:x ”USA”. ”cheese”. _:x :y. “~” “~”. # _:x _:y “~” ”USA”. # _:x ”cheese”. “~”. #_:x Old Blank Node Identifier New Blank Node Identifier _:y_:g1 _:x_:g2 _:g2 _:g1. _:g2 ”USA”. ”cheese”. _:g2. BNode Table 18 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

19. 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 19 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

20. 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 20 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

21. 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) 21 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

22. 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 Base URI Local Name 22 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

23. 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 23 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

24. 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 24 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

25. 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 25 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

26. 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 26 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

27. Similarity/Distance Metrics Used Cosine Similarity between SWD vectors Jaccard and Containment Metrics Hamming Distance between Simhash fingerprints 27 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

28. 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 28 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

29. Cosine Similarity Between Term Vectors Each SWD containing terms T j = {t 1, t 2 …t n } is treated as a vector V j = (γ 1 t 1,γ 2 t 2,… γ n t n ) where each γ i is the weight associated with term t i 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 29 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

30. SW Document Vectors TermFreq 2 1 1 1 30 TermFreq 2 2 1 1 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

31. 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 31 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

32. 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) 32 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

33. Classification 33 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 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion Example feature vector used for determining versioning relationship

34. Computing Delta Between Two Versions 34 Version1 Except Version2 Subtractive Delta Version2 Except Version1 Additive Delta Version1 Version2 Delta SVM Classifier: Versioning Relationship Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

35. Raw Delta Statement-by-statement comparison between canonical forms of the two SWDs Only local names of entities are compared 35 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

36. Delta After Deductive Closure 36 SWGv1 SWGv2 Compute deductive closure Canonicalize Generate Raw Delta Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

37. Delta After Deductive Closure If O is a set of propositions, p ԑ O and p ╞ q, then q ԑ O 37 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

38. Delta at Concept Level 38 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

39. 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 39 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

40. Concept Level Delta: example 40 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

41. Detecting Class Renaming 41 Sauterne Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

42. Detecting Class Renaming 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 42 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

43. Detecting Class Renaming 43 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

44. 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 44 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

45. 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 45 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

46. Evaluation: Pairs that Differ in Literal Content Results of 10-fold stratified cross validation using a Naïve Bayes classifier: 46 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

47. Evaluation: Pairs that Differ in Literal Content Results of using a SVM with all of the features, instead of manually selecting features: Attribute relevance ranking: 47 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

48. 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 48 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

49. Evaluation: Pairs that Differ in Base-URI 10-fold cross validation using Naïve Bayes: 10-fold cross validation (SVM linear-kernel) 49 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

50. Evaluation: Pairs with a Versioning Relationship 124 training instances from Swoogle data-set Filtered highly dynamic pairs from consideration 50 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

51. Evaluation: Pairs with a Versioning Relationship Test dataset: 160 instances (50% +ve 50% -ve) Classification results using SVM (linear kernel) 51 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

52. Correctness of Delta Computation For any two versions of a SW graph, it holds that Δ x (K → K’)K ≡ K’ We check this condition programmatically for each delta generated 52 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

53. 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 53 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion

54. 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 54 Introduction ᵒ Approach ᵒ Evaluation ᵒ Conclusion