Extracting Relevant Questions to an RDF Dataset Using Formal ...

Extracting Relevant Questions to an RDF Dataset Using Formal ...

Extracting Relevant Questions to an RDF Dataset Using Formal Concept Analysis 2016.05.09 Outline Introduction Formal Concept Analysis Generating questions building formal context creating concept lattice

query interface find interesting questions Validation Inspiration Introduction a potentially interesting dataset has been discovered significant efforts are currently required in order to understand its schema, its content, the way to query it and what it can answer automatically discover questions that can be answered by an

RDF dataset sets of entities => answers a hierarchy of meaningful sets of entities from a dataset => query interface identifying relevant questions to act as a starting point to the navigation in the question hierarchy empirical analysis metrics (formal concept analysis & ontology summarisation) Introduction

questions: "Who knows Tom?" query: "" "What are the people that Tom knows?" "What are the things that Tom knows?" "What are the people that Tom met?" Formal Concept Analysis formal context

set of objects set of attributes binary relation concept extent intent objects

attributes concept lattice set of all concepts derived from a formal context subconcept relation () => and Building Formal Context consider 3 types of attributes applied to instance

example & Creating Concept Lattice Figure: Concept lattice generated from http://kmi.open.ac.uk/people/tom/rdf

Query Interface pseudo-natural language "What are the that and that ?" ,, example "What are the that ?"

a complete hierarchy of questions Query Interface Find Interesting Questions generate a reasonable entry point into a large question hierarchy. Ontology Summarization coverage level density

union of not too high not too low not too high FCA support not a large number intensional stability extensional stability Find Interesting Questions

Experiment ask 12 users to inspect a reasonably large dataset and express up to 5 questions they believe to be interesting. 27(61%) out of 44 questions match the format of questions produced by the method. the use of disjunctive clauses Which Greek plays have been performed in Kenneth McLeishs translations or versions? numerical manipulations/tests on values What are the restaurants that have ratings higher than 2.5

Find Interesting Questions generate a reasonable entry point into a large question hierarchy. Ontology Summarization level density : d(concept level, average level) : diff(density(concetp), 2) FCA support

depends on dataset intensional stability vary low (0.0008) to high (0.82) extensional stability always high (0.75~1.0) linear combination of the 3 metrics Validation Inspiration use FCA to analyze baidu baike. Class and property

automatically extract questions from baidu baike and extend questions. generate question-answer pairs

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