natural language questions for the web of data 1 mohamed yahya, klaus berberich, gerhard weikum max...

Natural Language Questions for the Web of Data

1 Mohamed Yahya, Klaus Berberich, Gerhard WeikumMax Planck Institute for Informatics, Germany

2 Shady ElbassuoniQatar Computing Research Institute

3 Maya RamanathDept. of CSE, IIT-Delhi, India

4 Volker Tresp4Siemens AG, Corporate Technology, Munich, Germany

EMNLP 2012

QNL Translation to

QNL : Natural Language Questions“Which female actor played in Casablanca and is married to a writer who was born in Rome?”.

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 resultsDifficult for the user

Translation

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Yago2

YAGO2s is a huge semantic knowledge base, derived from Wikipedia, WordNet and GeoNames.

RelationClass Entities

Architecture of DEANNA.

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Phrase detection

A detected phrase p is a pair < Toks, l >Toks : phrasel : label (l {concept, relation})∈

Phrase detectionQNL Phrase

Pr : {<*, relation >}Pc : {<*, 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

concept phrase detection :

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?”

Phrase Mapping

to map concept phrases:also Search instances of the means relation in Yago2

to map relation phrases: rely on a corpus of textual patterns to relation mappings

e.q. “Which female actor played in Casablanca and is married to a writer who was born in Rome?”

textual patterns relation

Phrase MappingPhrase Mapping

Q-Unit Generation

Q-Unit GenerationMapping Candidategraph

Dependency parsing :

q-unit is a triple of sets of phrases

Q-Unit GenerationDependency parsing :

identifies triples of tokens:<trel, targ1, targ2>, where trel, targ1, targ2 q∈ NL

who was born in Rome?

nsubjpass(born-3, who-1)auxpass(born-3, was-2)root(ROOT-0, born-3)prep_in(born-3, Rome-5)

e.q.

born

who Rome

trel

targ1targ2

root

nsubjpass in

<born, who, Rome>,

Q-Unit Generation

q-unit is a triple of sets of phrases<{prel P∈ r}, {parg1 P∈ c}, {parg2 P∈ c}> ,trel p∈ rel , targ1 p∈ arg1 , and targ2 p∈ arg2 .

<born, writer, Rome>

triples of tokens phrase

<born, relation ><was born, relation ><Rome, concept ><a writer, concept >

Joint Disambiguation

Joint Disambiguation

1.each phrase is assigned to at most one semantic item2.resolves the phrase boundary ambiguity

(only nonoverlapping phrases are mapped)

Rule

Joint Disambiguation

Disambiguation Graph• Joint disambiguation takes place over a disambiguation

graph DG = (V, E), – V = Vs V∪ p V∪ q

– E = Esim E∪ coh E∪ q

V = Vs V∪ p V∪ q

Vq : a set of placeholder nodes for q–units

Joint Disambiguation

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

Disambiguation Graph

Disambiguation Graph

Eq V⊆ q × Vp × d, d {rel, arg1, arg2}∈Called q-edge

E = Esim E∪ coh E∪ q

Esim V⊆ p × Vs

a set of weighted similarity edges

Ecoh V⊆ s × Vs

a set of weighted coherence edges

Disambiguation Graph

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)

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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…}

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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)∪

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InLinks(r)

• InLinks(r) = ∪(e1, e2) r ∈ (InLinks(e1) ∩ InLinks(e2))

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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

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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.

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Definitions (part1)

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Definitions (part2)

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objective function

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Constraints(1~3)

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Constraints(4~7)

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Constraints(8~9)

This is not invoked for existential questions

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resulting subgraph for the disambiguation graph of Figure 3

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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

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5 Evaluation

• Datasets• Evaluation Metrics• Results & Discussion

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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

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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.

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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.

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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.

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query-answering stage

• the judges were asked to identify if the result sets for the generated queries are satisfactory.

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•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.

•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).

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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.