The Open UniversitySkip to content

Extracting relevant questions to an RDF dataset using formal concept analysis

d'Aquin, Mathieu and Motta, Enrico (2011). Extracting relevant questions to an RDF dataset using formal concept analysis. In: Proceedings of the sixth international conference on Knowledge capture - K-CAP '11, p. 121.

Full text available as:
Full text not publicly available (Version of Record)
Due to publisher licensing restrictions, this file is not available for public download
Click here to request a copy from the OU Author.
DOI (Digital Object Identifier) Link:
Google Scholar: Look up in Google Scholar


With the rise of linked data, more and more semantically described information is being published online according to the principles and technologies of the Semantic Web (especially, RDF and SPARQL). The use of such standard technologies means that this data should be exploitable, integrable and reusable straight away. However, once 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. In this paper, we propose a method and a tool to automatically discover questions that can be answered by an RDF dataset. We use formal concept analysis to build a hierarchy of meaningful sets of entities from a dataset. These sets of entities represent answers, which common characteristics represent the clauses of the corresponding questions. This hierarchy can then be used as a querying interface, proposing questions of varying levels of granularity and specificity to the user. A major issue is however that thousands of questions can be included in this hierarchy. Based on an empirical analysis and using metrics inspired both from formal concept analysis and from ontology summarization, we devise an approach for identifying relevant questions to act as a starting point to the navigation in the question hierarchy.

Item Type: Conference or Workshop Item
Copyright Holders: 2011 ACM
Extra Information: Conference held in cooperation with the AAAI
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Item ID: 29300
Depositing User: Kay Dave
Date Deposited: 25 Aug 2011 16:00
Last Modified: 08 May 2019 14:27
Share this page:


Altmetrics from Altmetric

Citations from Dimensions

Actions (login may be required)

Policies | Disclaimer

© The Open University   contact the OU