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Question Answering over Linked Data. London Text Analytics Meetup , 5 August 2011. Danica Damljanović contact: danica.damljanovic@gmail.com. The goal. Automatically answer Natural Language questions by a machine as if it were a human. MARY <is a> PERSON

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question answering over linked data

Question Answering over Linked Data

London Text Analytics Meetup, 5 August 2011

DanicaDamljanović

contact: danica.damljanovic@gmail.com

the goal

The goal

Automatically answer Natural Language questions by a machine as if it were a human.

slide3

MARY <is a> PERSON

UNIVERSITY OF SHEFFIELD <is an> ORGANISATION

MARY <works for> UNIVERSITY OF SHEFFIELD

SHEFFIELD <is a> CITY

UNIVERSITY OF SHEFFIELD <is located in> SHEFFIELD

UNITED KINGDOM <is a> COUNTRY

SHEFFIELD <is located in> UNITED KINGDOM

MARY <lives in> SHEFFIELD

SELECT ?country

WHERE

{

?person <lives in> ?city

?city <located in> ?country

  • FILTER ?person = MARY

}

Mary works for University of Sheffield, which is located in Sheffield. Sheffield is located in the United Kingdom. Mary lives in Sheffield.

slide4

Motivation

In which country

does Mary live?

building question answering systems challenges
Building Question-Answering systems: Challenges

Application developers:

customise the system when porting it to work with different kind of data e.g.

Where >> Location/City

Who >> Person, Organisation

requirements
Requirements
  • Flexibility of the supported language
    • No strict adherence to syntax
  • Habitable system: the user easily makes the queries and also avoids constructions that are not supported without any problems
  • Portable with minimal customisation
freya f eedback r efinement e xtended vocabular y a ggregator
FREyA - Feedback, Refinement, Extended VocabularyAggregator
  • Feedback: showing the user the system’s interpretation of the query
  • Refinement:
    • resolving ambiguity: generating dialog whenever one term refers to more than one concept in the ontology (precision)
  • Extended Vocabulary:
    • expressiveness: generating dialog whenever an “unknown” term appears in the question (recall)
feedback in freya
Feedback in FREyA
  • http://gate.ac.uk/freya

ESWC 2010

clarification dialogs
Clarification dialogs
  • Generated by combining the syntactic parsing and ontology-based lookup
  • the system learns from the user’s selections
    • “No ranking will be perfect”
  • Customisation through the dialog with the user
    • application developers vs. end-users
learning
Learning

IF

THEN

the user controls the output
The User Controls the Output

POC

min

geo:loElevation

point

POC

geo:isLowestPointOf

geo:LoPoint

POC

max

state

geo:stateArea

area

geo:State

evaluation
evaluation
  • Question-Answering over Linked Data Challenge
    • Two datasets of different kind, MusicBrainz and DBPedia
    • 50 training questions per dataset along with the correct answers
    • 50 testing questions per dataset without the answer
freya results
FREyA: results
  • Question-Answering over Linked Data challenge (ESWC’11, Crete)
    • FREyA the only system that participated with both provided datasets demonstrating portability
    • DBPedia, f-measure 0.58 (PowerAqua 0.5)
    • MusicBrainz, f-measure 0.71 (SWIP 0.66)
  • 94.4 % precision and recall on the Mooney GeoQuery dataset (PANTO recall 88.05%, Querix precision 87.11%)
conclusion
Conclusion
  • Combining syntactic parsing with ontology-based lookup in an interactive process of feedback and query refinement can increase the precision and recall of Question-Answering over Linked Data,
  • while reducing the time for customisation by shifting some tasks from application developers to end users.
future challenges
Future Challenges
  • Output:
    • Correct answer OR
    • Identifying the flaws in the data?
  • Ranking/disambiguation algorithms to improve MRR
thank you for your attention questions
thank you for your attention! questions?

Contact: danica.damljanovic@gmail.com

demos
Demos
  • http://gate.ac.uk/sale/dd/
  • United States geography
  • MusicBrainz
  • Dbpedia