1. Question Answering & Linked Data
Wang Yong

2. Content Overview of QA System Template-based Question Answering
Open Question Answering Over Multiple Knowledge Bases
Structured data and inference in DeepQA
Conclusion

3. General Structure of QA System[1]
Question Analysis
Matching with Data
Query Construction
Answer retrieval
Scoring
Natural Language Question
Answer(s)
Linguistic Tools and Resources
KDs
corpora
Data Sources
Ontology
Index

4. Main challenges Variability of Natural Language
How can you tell if you have the flu?
What are signs of the flu?
Complexity of Natural Language
Of current U.N. member countries with 4-letter names, the one that is first alphabetically.
Who produced the most films?

5. Main challenges Gap between Natural Language and Data Sources
String Differences
wife of, husband of ---- dbo:spouse
Structure Differences
Who are the great-grandchildren of Bruce Lee?
dbo:child
Quality and Heterogeneity of Data Sources
Completeness and accuracy
Open Information Extraction
Different Schemas
dbo:location dbo:headquarter dbo:locationCity

6. Template-based Question Answering[2]

7. Motivation
Traditional methods map a natural language question to a triple-based representation
Who wrote The Neverending Story?
<person; wrote; Neverending Story>
Some question can be represented this way
Which cities have more than three universities?
<cities; more than; three universities>
SELECT ?y WHERE {
?x rdf:type onto:University .
?x onto:city ?y . }
HAVING (COUNT(?x) > 3)

8. Solution SPARQL template
syntactic structure of natural language question
domain-independent expressions
Which y p more than N x?
SELECT ?y WHERE {
?x rdf:type ?c .
?x ?p ?y . }
HAVING (COUNT(?x) > N)

9. Implementation Lexicalized Tree Adjoining Grammar (LTAG)
discourse representation Structure (DRS)
Based on manual compiled grammars and rules
parser
Natural language input
grammar
LTAG derivation Tree
syntactic construction
DRS
semantic construction
formal query
Scope resolution
Tree-adjoining grammar (TAG) is a grammar formalism defined by Aravind Joshi. Tree-adjoining grammars are somewhat similar to context-free grammars, but the elementary unit of rewriting is the tree rather than the symbol.
initial trees ('α') and auxiliary trees ('β')
substitution or adjunction
DRT uses discourse representation structures (DRS) to represent a hearer's mental representation of a discourse as it unfolds over time. There are two critical components to a DRS:
1, A set of discourse referents representing entities which are under discussion.
2, A set of DRS conditions representing information that has been given about discourse referents.
Consider Sentence (1) below:
(1) A farmer owns a donkey. The DRS of (1) can be notated as (2) below:
(2) [x,y: farmer(x), donkey(y), owns(x,y)]

10. Experiment 50 questions from the QALD benchmark
11 questions are not in the analysis scope
5 questions cannot be parsed
unknown syntactic constructions
uncovered domain-independent expressions
Who has been the 5th president of the United States of America?
19 have correct answer, 2 are almost correct
13 are wrong or under the threshold
Main problem
entity identification (Give me all movies with Tom Cruise?)
query selection

11. Open Question Answering Over Multiple Knowledge Bases[3]

12. Motivation One knowledge base can not answer all questions
Open Question Answering need information from different knowledge bases
Natural language has high variability
Different knowledge bases use different knowledge expression

13. Solution Scope: simple factoid questions
Paraphrase to overcome natural language variability
Rewrite to match KB schema
Express question as triples to utilize all KBs
What fruits are a source of vitamin C?
?x : (?x, is-a, fruit) (?x, source of, vitamin c)
SELECT t0.arg1 FROM triples AS t0, triples AS t1
WHERE
keyword-match(t0.rel, "is-a") AND
keyword-match(t0.arg2, "fruit") AND
keyword-match(t1.rel, "source of") AND
keyword-match(t1.arg2, "vitamin c") AND
string-similarity(t0.arg1, t1.arg1) > 0.9

14. Implementation Question Paraphrase Parse Query Rewrite Execute Answer
How can you tell if you have the flu?
What are signs of the flu?
Query
?x: (?x, sign of, the flu)
?x: (the flu, symptoms, ?x)
Answer
(the flu, symptoms include, chills)
Paraphrase
5 million mined Operators
From wikiAnswers
Parse
10 high-precision templates
Manual created
Rewrite
74 million mined operators
Mined from corpora
Execute
1 billion assertions

15. Experiment KBs: Training over Question and Answer Pairs
Freebase, Open IE, Probase and NELL
Training over Question and Answer Pairs
Linear scoring function
latent-variable structured perceptron algorithm
Question and Answer pairs
WebQuestions, TREC, WikiAnswers

16. Experiment

17. Structured data and inference in DeepQA[4]

18. Motivation Unstructured data structured data Broad coverage
Low-precision
structured data
incomplete
high-precision
Has formal semantics
logical reasoning (common sense reasoning/implicit evidence)

19. Temporal and geospatial reasoning
Detect time relations:
TLink, birthDate, deathDate
Compute temporally compatible
birthdate < TLink < deathDate
Detect spatial relations
relative direction, border, containment, near, far
Convert to geo-coordinates from Dbpedia to compute distance or other geospatial relations
the symmetry of the borders relation
transitivity of the containment relation
Evaluation
1% to 2% improvement in accuracy

20. Taxonomic reasoning check candidate answer’s type
Data Source: Dbpedia, YAGO
candidate answer – an entity resource
question lexical answer type(LAT) - a class in the type system
WordNet, domain-specific type-mapping file, statistical relatedness
Soring
Equivalent/subclass, Disjoint, Sibling, Superclass…
Evaluation
3%–4% improvement in accuracy

21. Conclusion Analysis of complex problem is a nontrivial problem
manual compiled grammars and rules
Mapping between natural language and KBs has significant impact on the accuracy
Semantics light expression(in), structure differences(gf)
Structured Data is incomplete, need help from unstructured data

22. Reference
[1] Unger, C., Freitas, A., Cimiano P.: An Introduction to Question Answering over Linked Data. Reasoning Web. Reasoning on the Web in the Big Data Era, LNCS, pp (2014)
[2] Unger, C., Bühmann, L., Lehmann, et al.: Template-based question answering over RDF data. In: Proceedings of the 21st International Conference on World Wide Web, pp. 639–648. ACM (2012)
[3] Fader, A., Zettlemoyer, L., Etzioni, O.: Open question answering over curated and extracted knowledge bases. In: Proceedings of the International Conference on Knowledge Discovery and Data Mining, KDD (2014)
[4] Kalyanpur, A., et al.: Structured data and inference in DeepQA. IBM Journal of Research & Development 56(3/4) (2012)