Swoogle: a search and metadata engine for the semantic web
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Ontology ranking based on the analysis of concept structures
Proceedings of the 3rd international conference on Knowledge capture
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A Multidimensional Semantic Space for Data Model Independent Queries over RDF Data
ICSC '11 Proceedings of the 2011 IEEE Fifth International Conference on Semantic Computing
Searching web data: An entity retrieval and high-performance indexing model
Web Semantics: Science, Services and Agents on the World Wide Web
Falcons Concept Search: A Practical Search Engine for Web Ontologies
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
SQORE-based ontology retrieval system
DEXA'07 Proceedings of the 18th international conference on Database and Expert Systems Applications
Proceedings of the 19th international conference on Intelligent User Interfaces
Editorial: Querying linked data graphs using semantic relatedness: A vocabulary independent approach
Data & Knowledge Engineering
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The process of searching and understanding existing vocabularies (terminological artifacts) on the Linked Data Web is an intrinsic activity to the consumption and production of Linked Data. Data consumers trying to find and understand the vocabularies behind datasets in order to query them, or data producers searching for existing resources to describe their data, face the challenge of semantically searching existing concepts in vocabularies. Traditional search mechanisms do not address the level of semantic matching necessary to match users' information needs to vocabulary elements, bringing an additional barrier to the consumption and production of Linked Data on the Web. This work describes a terminological search mechanism which uses a distributional semantic model to provide a best-effort semantic matching solution. The distributional semantic model leverages the semantic information present in large volumes of unstructured text to improve the semantic matching capabilities of the search process. A quantitative evaluation of the quality of the search results shows that the approach provides an effective semantic matching mechanism for terminological search.