Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Ontology summarization based on rdf sentence graph
Proceedings of the 16th international conference on World Wide Web
Reducing the Representation Complexity of Lattice-Based Taxonomies
ICCS '07 Proceedings of the 15th international conference on Conceptual Structures: Knowledge Architectures for Smart Applications
ASWC '08 Proceedings of the 3rd Asian Semantic Web Conference on The Semantic Web
An intelligent user interface for browsing and searching MPEG-7 images using concept lattices
CLA'06 Proceedings of the 4th international conference on Concept lattices and their applications
Conceptual navigation in RDF graphs with SPARQL-Like queries
ICFCA'10 Proceedings of the 8th international conference on Formal Concept Analysis
Query-based multicontexts for knowledge base browsing: an evaluation
ICCS'06 Proceedings of the 14th international conference on Conceptual Structures: inspiration and Application
Formal concept discovery in semantic web data
ICFCA'12 Proceedings of the 10th international conference on Formal Concept Analysis
Proceedings of the Third International Conference on Learning Analytics and Knowledge
Query generation for semantic datasets
Proceedings of the seventh international conference on Knowledge capture
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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.