Natural Language Questions for the Web of Data

1. Natural Language Questionsfor the Web of Data Mohamed Yahya, Klaus Berberich, Gerhard Weikum Max Planck Institute for Informatics, Germany Shady Elbassuoni Qatar Computing Research Institute Maya Ramanath Dept. of CSE, IIT-Delhi, India Volker Tresp Siemens AG, Corporate Technology, Munich, Germany EMNLP 2012

2. Introduction • Natural language question qNL • “Which female actor played in Casablanca and is • married to a writer who was born in Rome?”. • SPARQL • ?x hasGender female ?x isa actor • ?x actedIn Casablanca (film) ?x marriedTo ?w • ?w isa writer ?w bornIn Rome Problem : SPARQL is way too difficlut. Target : Convert SPARQL to qNL.

3. Knowledge Base: Yago2 • Yago2is a huge semantic knowledge base, derived from Wikipedia, WordNet and GeoNames.

4. Framework • DEANNA (DEep Answers for maNy Naturally Asked questions)

5. Framework Phrase Detection Phrase Mapping Q-Unit Generation Disambiguation of Phrase Mappings Query Generation

6. Phrase Detection • A detected phrase p is a pair < Toks, l > where Toks is a phrase and l is a label, l∈ {concept, relation}. • Such as • qNL : “Which female actor played inCasablanca and is • married toa writer who was born in Rome?”. • Concept phrase : < a writer, concept> • < Casablanca, concept> • Relation phrase : < played in, relation > • < is married to, relation > Framework

7. Phrase Detection Conceptdetection • Using the Yago2 knowledge base. Framework

8. Phrase Detection Relation detection • Using the ReVerb (Fader et al., 2011) which is a relation detector. qNL : “Which female actor played in Casablanca and is married to a writer who was born in Rome?”. Framework

9. Phrase Detection Framework

10. Phrase Mapping • The mapping of concept phrases also relies on the phrase-concept dictionary. : Using Yago2 knowledge base. • The mapping of relation phrases relies on a corpus of • textual patterns to relation mappings of the form. Framework

11. Phrase Mapping Framework

12. Dependency Parsing & Q-Unit Generation Dependency Parsing • Dependency parsing identifies triples of tokens, • or triploids, <trel, targ1, targ2>, where • trel, targ1, targ2 ∈ qNL are seeds for phrases. Framework

13. Dependency Parsing & Q-Unit Generation • qNL : “Which female actorplayed inCasablanca and is • married to a writer who was born in Rome?”. played / played in actor Casablanca • Triploid : < played in, actor, Casablanca > < played, actor, Casablanca > Framework

14. Dependency Parsing & Q-Unit Generation Q-Unit Generation • A q-unit is a triple of sets of phrases, • <{prel ∈ Pr}, {parg1 ∈ Pc}, {parg2 ∈ Pc}>, where • trel ∈ prel and similarly for arg1 and arg2. Framework

15. Dependency Parsing & Q-Unit Generation Framework

16. Dependency Parsing & Q-Unit Generation Framework

17. Disambiguation of Phrase Mappings • Disambiguation Graph • Esim⊆ Vp × Vs • Ecoh⊆ Vs × Vs • Eq⊆ Vq×Vp×d, where d ∈ {rel, arg1, arg2} is a q-edge. Framework

18. Disambiguation of Phrase Mappings Disambiguation Graph(Cohsem) • For Yago2, the characterize an entity e by its inlinks InLinks(e): the set of Yago2 entities whose corresponding Wikipedia pages link to the entity. InLinks(Taipei_zoo): Framework

19. Disambiguation of Phrase Mappings Disambiguation Graph(Cohsem) • For classc with entities e, its inlinks are defined as follows: InLinks(Taiwan): Framework

20. Disambiguation of Phrase Mappings Disambiguation Graph(Cohsem) • For classr with entities e, its inlinks are defined as follows: Framework

21. Disambiguation of Phrase Mappings Disambiguation Graph(Simsem) • For entities • How often a phrase refers to a certain entity in Wikipedia. • For classes • Normalized prior the reflects the Number of members in a class • For relations • The maximumn-gram similarity between the phrase and any of the • relation’s surface forms Framework

22. Disambiguation of Phrase Mappings • Objective function is : Framework

23. Disambiguation of Phrase Mappings Definitions: Framework

24. Disambiguation of Phrase Mappings Definitions: Framework

25. Disambiguation of Phrase Mappings Constraints: Framework

26. Disambiguation of Phrase Mappings Constraints: Framework

27. Disambiguation of Phrase Mappings Constraints: Framework

28. Query Generation Framework

29. Evaluation • Experiments are based on two collections of questions: • QALD-1 (27 questions out of 50) • NAGA (44 questions out of 87) • Using 19 questions from the QALD-1 Test set for tuning hyperparameters (α, β, γ) in the ILP objective function

30. Evaluation • Evaluating the output of DEANNA at three stages in the • processing pipeline: • a) Disambiguation • b) Query Generation • c) Question Answering • At each of the three stages, the output was shown to two human assessors. If the two were in disagreement, then a third person resolved the judgment.

31. Evaluation • Define coverage and precision as follows:

32. Evaluation a) Disambiguation

33. Evaluation b) Query Generation

34. Evaluation c) Question Answering

35. Evaluation

36. Conclusions A method for translating natural-language questions into structured queries.