Repairing Hidden Links in Linked Data
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The document presents a study on enhancing the quality of RDF knowledge graphs by repairing hidden links between entities represented as string literals and their corresponding IRIs. The authors propose a method utilizing ontological axioms and data profiling to identify and convert these string literals, resulting in improved graph connectivity with notable increases in metrics. The approach shows high recall and precision in detecting entity relations, suggesting substantial potential for future improvements in knowledge graph integrity.
Repairing Hidden Links in Linked Data
- 1. REPAIRING HIDDEN LINKS IN LINKED DATA ENHANCING THE QUALITY OF RDF KNOWLEDGE GRAPHS Nandana Mihindukulasooriya, Mariano Rico, Idafen Santana-Pérez, Raúl García-Castro and Asunción Gómez-Pérez December 5th, 2017 The Ninth International Conference on Knowledge Capture K-CAP 2018, Austin, Texas, United States
- 3. Things as strings! 3 dbr:Tim_Cook dbr:Apple_Pie “Apple” dbo:employer dbo:ingredient • Introduce model inconsistencies • Increases ambiguity • Reduces connectivity
- 4. … after correctly linked 4 dbr:Tim_Cook dbr:Apple_Pie dbr:Apple_Inc dbr:Apple dbo:Company dbr:Consumer _electronics dbr:IPhone yago:Fruit 2.4 dbr:Rosacese rdf:type rdf:typedbo:industry dbo:family dbp:fiber dbo:product dbo:employer dbo:ingredient
- 5. Is this a common problem? • Web Data Commons (JSON-LD embedded data) • http://webdatacommons.org/structureddata/ 5 Property # of objects # of string literals Literal % IRI % schema:creator 16,573,426 15,1782,874 95.23 4.77 schema:brand 4,694,411 2,420,908 51.57 48.43 schema:author 26,193,682 1,500,898 5.73 94.37 schema: hiringOrganization 1,252,870 1,231,840 98.84 1.16 schema:publisher 31,317,151 560,577 1.79 98.21
- 6. Research Questions • How to identify string literals that denote entities in a KG? • How to transform those string literals into IRIs that correspond to entities they denote? • How to measure the improvement in quality because of the transformation? 6
- 7. Entity relations • Entity relations link two entities. • All objects of entity relations should be entities. • Not all relations are entity relations. 7 an entity relation Person Company Company “Company Description” a non-entity relation
- 8. String to Entity IRI Transformation Such string literals can be transformed to their corresponding entity IRIs with high precision using both ontological axioms and data profiling information. 8 “Apple” dbr:Apple dbr:Apple_Inc dbr:Apple _Bank dbr:The_Apple_Film
- 9. Connectivity Connectivity of a knowledge graph can be improved by transforming literal nodes in entity relations into their corresponding entity IRIs. 9
- 10. Related Problems - I • Named entity disambiguation from text 10 Kill Bill was directed by Quentin Tarantino and stars Uma Thurman. dbr:Kill_Bill:_Volume_1 dbr:Quentin_Tarantino dbr:Uma_Thurman
- 11. Related Problems - II • Web Table Matching 11 company ind. loc. IBM technology United States United airlines USA … … … dbr:United_States dbr:Technology dbr:IBM dbr:United_Airlines dbr:Airline dbo:country
- 12. Approach 12 • Using ontological axioms • Using data profiling information Identification of entity relations • Context generation • Type identification (entity relation range) • Entity IRI identification String literal to IRI conversion
- 13. Identification of entity relations 13 Identification of entity relations RDF Graph Ontology Definitions Entity relations Other relations P1 P2 P3 Pn PX PY
- 14. Identification of entity relations 14 Identification of entity relations OWL Object Properties Property range Entity Relation Classifier Features IRI %, Lit%, DistinctIRI%, DistinctLit%, String%, Num%, Date% Training Data Known entity relations Manually annotated rdfs props Ontology- driven Data- driven see the paper for the algorithm
- 15. Approach 15 • Using ontological axioms • Using data profiling information Identification of entity relations •Type identification (entity relation range) •Context generation •Entity disambiguation String literal to IRI conversion
- 16. Type identification and entity linking 16 employer dbr:Google dbr:Microsoft dbr:Yahoo! dbr: Amazon_(company) “Apple” “I.B.M” “Johnson & Johnson” “Mars” “Oracle” “Sun” range information (ontology) Type Prediction (range) Type restrictions Type frequency analysis Entity linking Entity disambiguation
- 17. Context generation 17 Tim Cook employer Apple. Timothy Donald "Tim" Cook is an American business executive, industrial engineer and developer. Cook is the current and seventh Chief Executive Officer of Apple Inc., previously serving as the company's Chief Operating Officer, under its .... Apple Pie ingredient Apple. An apple pie is a fruit pie, in which the principal filling ingredient is apple. It is, on occasion, served with whipped cream or ice cream on top, or alongside cheddar cheese. The pastry is generally used top-and-bottom ...
- 18. Evaluation 18
- 19. Evaluation - I • Can the approach correctly identity entity relations? • manually-annotated gold standard • 3 annotators with high inter-annotator agreement 19 Class Entity Relations Detected relations Prec. Recall Correct Incorrect dbo:Athlete 183 178 53 77.06% 97.27% dbo:SportsTeam 157 156 48 76.47% 99.36% dbo:SportsEvent 116 115 41 73.71% 99.14% Total 456 449 142 75.97% 98.47% High Recall
- 20. Evaluation - II • Can the approach correctly disambiguate and link the identified strings? • Manual verification of a random sample of 300 20 Class Sample Size Disambiguated Prec. Recall Correct Incorrect dbo:Athlete 100 51 50 98.04% 50% dbo:SportsTeam 100 44 44 100% 44% dbo:SportsEvent 100 58 55 94.83% 56% Total 300 153 149 97.38% 49.67% High Precision
- 21. Graph metrics 21 # of edges # of components Isolated components largest component size 8 6 3 5 • connected components
- 22. Graph metrics 22 # of edges # of components Isolated components largest component size 8 6 3 5 12 (+4) 2 (-4) 0 (-3) 12 (+7) • connected components
- 23. Evaluation - III • Does this transformation increase the connectivity of the graph? 23 Graph edges components isolated Largest component size % of nodes Original 828,310 119,623 112,331 168,128 54.28 Repaired 1,035,912 99,137 93,507 192,805 64.16 +207,602 -20,486 -18,824 +24,677 +9.88 +25.06% -17.12% -16.76% +14.68% Improved Connectivity
- 24. Conclusions and future work • A large number of entities are represented as strings in Linked Data . • The proposed approach can detect such strings with high recall (98%) and covert them to their corresponding entity IRIs with high precision (97%). • We could add 25% more links and improved the connectivity of a subgraph of DBpedia by 17% • In future, improve the algorithm by using more context information from the graph for the task. 24
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