A content-search information retrieval process based on conceptual graphs
Knowledge and Information Systems
Extending SPARQL with regular expression patterns (for querying RDF)
Web Semantics: Science, Services and Agents on the World Wide Web
Top-k Exploration of Query Candidates for Efficient Keyword Search on Graph-Shaped (RDF) Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
SPARK: adapting keyword query to semantic search
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Q2Semantic: a lightweight keyword interface to semantic search
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
An easy way of expressing conceptual graph queries from keywords and query patterns
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
Expressing conceptual graph queries from patterns: how to take into account the relations
ICCS'11 Proceedings of the 19th international conference on Conceptual structures for discovering knowledge
SemSearch: a search engine for the semantic web
EKAW'06 Proceedings of the 15th international conference on Managing Knowledge in a World of Networks
Allowing end users to query graph-based knowledge bases
EKAW'12 Proceedings of the 18th international conference on Knowledge Engineering and Knowledge Management
| Hi-index | 0.00 |
Our purpose is to hide the complexity of formulating a query expressed in a graph query language such as SPARQL. We propose a mechanism allowing queries to be expressed in a very simple pivot language, mainly composed of keywords and relations between keywords. Our system associates the keywords with the corresponding elements of the ontology (classes, relations, instances). Then it selects pre-written query patterns, and instanciates them with regard to the keywords of the initial query. Several possible queries are generated, ranked and then shown to the user. These queries are presented by means of natural language sentences. The user then selects the query he/she is interested in and the SPARQL query is built.