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Natural Language Questions for the Web of Data

Natural Language Questions for the Web of Data. 1 Mohamed Yahya , Klaus Berberich , Gerhard Weikum Max Planck Institute for Informatics, Germany 2 Shady Elbassuoni Qatar Computing Research Institute 3 Maya Ramanath Dept. of CSE, IIT-Delhi, India 4 Volker Tresp

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Natural Language Questions for the Web of Data

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  1. Natural Language Questions for the Web of Data 1 Mohamed Yahya, Klaus Berberich, Gerhard Weikum Max Planck Institute for Informatics, Germany 2 Shady Elbassuoni Qatar Computing Research Institute 3Maya Ramanath Dept. of CSE, IIT-Delhi, India 4Volker Tresp 4Siemens AG, Corporate Technology, Munich, Germany EMNLP 2012

  2. QNL Translation to QNL : Natural Language Questions “Which female actor played in Casablanca and is married to a writer who was born in Rome?”. Translation • QFL: SPARQL 1.0 ?x hasGender female ?x marriedTo ?w ?x isa actor ?w isa writer ?x actedIn Casablanca_(film) ?w bornIn Rome • Characteristics of SPARQL : • Complex query • good results • Difficult for the user

  3. Yago2 YAGO2s is a huge semantic knowledge base, derived from Wikipedia, WordNet and GeoNames. Relation Class Entities Natural Language Questions for the Web of Data

  4. Architecture of DEANNA.

  5. Phrase detection A detected phrase p is a pair < Toks, l > Toks: phrase l : label (l ∈ {concept, relation}) QNL Phrase detection Phrase Pr : {<*, relation >} Pc : {<*, concept >} Natural Language Questions for the Web of Data

  6. Phrase detection concept phrase detection : e.q. “Which female actor played in Casablanca and is married to a writer who was born in Rome?” Search instances of the means relation in Yago2

  7. Phrase detection relation phrase detection : rely on a relation detector based on ReVerb (Fader et al., 2011) with additional POS tag patterns e.q. “Which female actor played in Casablanca and is married to a writer who was born in Rome?”

  8. Phrase Mapping to map concept phrases: also Search instances of the means relation in Yago2 Phrase Phrase Mapping Mapping • to map relation phrases: • rely on a corpus of textual patterns to relation mappings textual patterns relation e.q. “Which female actor played in Casablanca and is married to a writer who was born in Rome?”

  9. Q-Unit Generation Candidategraph Mapping Q-Unit Generation Dependency parsing : q-unit is a triple of sets of phrases

  10. Q-Unit Generation Dependency parsing : identifies triples of tokens: <trel, targ1, targ2>, where trel, targ1, targ2∈qNL e.q. root who was born in Rome? trel born nsubjpass in targ2 targ1 who Rome nsubjpass(born-3, who-1) auxpass(born-3, was-2) root(ROOT-0, born-3) prep_in(born-3, Rome-5) <born, who, Rome>,

  11. Q-Unit Generation q-unit is a triple of sets of phrases <{prel∈Pr}, {parg1∈ Pc}, {parg2∈ Pc}> ,trel∈prel , targ1∈ parg1 , and targ2∈ parg2 . <born, relation > <was born, relation > <Rome, concept > <a writer, concept > <born, writer, Rome> triples of tokens phrase

  12. Joint Disambiguation Joint Disambiguation Rule 1.each phrase is assigned to at most one semantic item 2.resolves the phrase boundary ambiguity (only nonoverlapping phrases are mapped)

  13. Joint Disambiguation Disambiguation Graph • Joint disambiguation takes place over a disambiguation graph DG = (V, E), • V = Vs∪Vp∪Vq • E = Esim∪Ecoh∪Eq

  14. Joint Disambiguation Disambiguation Graph • V = Vs∪Vp∪Vq Vs: the set of s-node (s-node is semantic items) Vp: the set of p-node p-node is phrases Vrp: the set of relation phrases Vrc: the set of concept phrases Vq : a set of placeholder nodes for q–units

  15. Disambiguation Graph Disambiguation Graph E = Esim∪Ecoh∪Eq Esim⊆Vp × Vs a set of weighted similarity edges Ecoh⊆ Vs × Vs a set of weighted coherence edges Eq⊆ Vq× Vp× d, d ∈ {rel, arg1, arg2} Called q-edge

  16. Disambiguation Graph Edge Weights • Cohsem(Semantic Coherence) • between two semantic items s1 and s2 as the Jaccard coefficient of their sets of inlinks. • Three kinds of inlink • InLinks(e) • InLinks(c) • InLinks(r)

  17. InLinks(e) • InLinks(e): the set of Yago2 entities whose corresponding Wikipedia pages link to the entity. • e.q. • Let e = Casablanca • InLinks(Casablanca) = {Marwan_al-Shehhi , Ingrid_Bergman, …, Morocco…} Natural Language Questions for the Web of Data

  18. InLinks(c) • InLinks(c) = ∪e∈c Inlinks(e) • e.q. let c = wikicategory_Metropolitan_areas_of_Morocco • InLinks(wikicategory_Metropolitan_areas_of_Morocco) = InLinks(Casablanca) ∪InLinks(Marrakech) ∪InLinks(Fes) ∪InLinks(Agadir) ∪InLinks(Safi,_Morocco) ∪InLinks(Oujda) ∪InLinks(Tangier) ∪InLinks(Rabat) Natural Language Questions for the Web of Data

  19. InLinks(r) • InLinks(r) = ∪(e1, e2) ∈ r (InLinks(e1) ∩InLinks(e2)) Natural Language Questions for the Web of Data

  20. Similarity Weights • For entities • how often a phrase refers to a certain entity in Wikipedia. • For classes • reflects the number of members in a class • For relations • reflects the maximum n-gram similarity between the phrase and any of the relation’s surface forms Natural Language Questions for the Web of Data

  21. Disambiguation Graph Processing • The result of disambiguation is a subgraph of the disambiguation graph, yielding the most coherent mappings. • We employ an ILP to this end. Natural Language Questions for the Web of Data

  22. Definitions (part1) Natural Language Questions for the Web of Data

  23. Definitions (part2) Natural Language Questions for the Web of Data

  24. objective function Natural Language Questions for the Web of Data

  25. Constraints(1~3) Natural Language Questions for the Web of Data

  26. Constraints(4~7) Natural Language Questions for the Web of Data

  27. Constraints(8~9) This is not invoked for existential questions Natural Language Questions for the Web of Data

  28. resulting subgraph for the disambiguation graph of Figure 3 Natural Language Questions for the Web of Data

  29. Query Generation • not assign subject/object roles in triploids and q-units • Example: • “Which singer is married to a singer?” • ?x type singer , ?x marriedTo ?y , and ?y type singer Natural Language Questions for the Web of Data

  30. 5 Evaluation • Datasets • Evaluation Metrics • Results & Discussion Natural Language Questions for the Web of Data

  31. Datasets • author's experiments are based on two collections of questions: • QALD-1 • 1st Workshop on Question Answering over Linked Data (QALD-1) • the context of the NAGA project • NAGA collection • The NAGA collection is based on linking data from the Yago2 knowledge base • Training set • 23 QALD-1 questions • 43 NAGA questions • Test set • 27 QALD-1 questions • 44 NAGA questions • Get hyperparameters (α, β, γ) in the ILP objective function. • 19 QALD-1 questions in Test set Natural Language Questions for the Web of Data

  32. Evaluation Metrics • author evaluated the output of DEANNA at three stages • 1. after the disambiguation of phrases • 2. after the generation of the SPARQL query • 3. after obtaining answers from the underlying linked-data sources • Judgement • two human assessors who judged whether an output item was good or not • If the two were in disagreement , then a third person resolved the judgment. Natural Language Questions for the Web of Data

  33. disambiguation stage • The task of judges • looked at each q-node/s-node pair, in the context of the question and the underlying data schemas, • determined whether the mapping was correct or not • determined whether any expected mappings were missing. Natural Language Questions for the Web of Data

  34. query-generation stage • The task of judges • Looked at each triple pattern • determined whether the pattern was meaningful for the question or not • whether any expected triple pattern was missing. Natural Language Questions for the Web of Data

  35. query-answering stage • the judges were asked to identify if the result sets for the generated queries are satisfactory. Natural Language Questions for the Web of Data

  36. For a question q and item set s in one of the stages of evaluation • correct(q, s) : the number of correct items in s • ideal(q) : the size of the ideal item set • retrieved(q, s) : the number of retrieved items • define coverage and precision as follows: • cov(q, s) = correct(q, s) / ideal(q) • prec(q, s) = correct(q, s) / retrieved(q, s). • Micro-averaging • aggregates over all assessed items regardless of the questions to which they belong. • Macro-averaging • first aggregates the items for the same question, and then averages the quality measure over all questions. Natural Language Questions for the Web of Data

  37. Natural Language Questions for the Web of Data

  38. Conclusions • Author presented a method for translating natural language questions into structured queries. • Although author’s model, in principle, leads to high combinatorial complexity, they observed that the Gurobi solver could handle they judiciously designed ILP very efficiently. • Author’s experimental studies showed very high precision and good coverage of the query translation, and good results in the actual question answers. Natural Language Questions for the Web of Data

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