A vector space model for automatic indexing
Communications of the ACM
An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval
IEEE Transactions on Knowledge and Data Engineering
Practical and effective IR-style keyword search over semantic web
Information Processing and Management: an International Journal
Knowledge-Based Linguistic Annotation of Digital Cultural Heritage Collections
IEEE Intelligent Systems
Using BM25F for semantic search
Proceedings of the 3rd International Semantic Search Workshop
Document expansion based on WordNet for robust IR
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Effective and efficient entity search in RDF data
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
Semantically enhanced Information Retrieval: An ontology-based approach
Web Semantics: Science, Services and Agents on the World Wide Web
Semantic Web search based on ontological conjunctive queries
Web Semantics: Science, Services and Agents on the World Wide Web
An ontology-based information retrieval model
ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
SMARTMUSEUM: A mobile recommender system for the Web of Data
Web Semantics: Science, Services and Agents on the World Wide Web
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The Web content increasingly consists of structured domain specific data published in the Linked Open Data (LOD) cloud. Data collections in this cloud are by definition from different domains and indexed with domain specific ontologies and schemas. Such data requires retrieval methods that are effective for domain specific collections annotated with semantic structure. Unlike previous research, we introduce a retrieval framework based on the well known vector space model of information retrieval to fully support retrieval of Semantic Web data described in the Resource Description Framework (RDF) language. We propose an indexing structure, a ranking method, and a way to incorporate reasoning and query expansion in the framework. We evaluate the approach in ad-hoc retrieval using two domain specific data collections. Compared to a baseline, where no reasoning or query expansion is used, experimental results show up to 76% improvement when an optimal combination of reasoning and query expansion is used.