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Jayaratne, Madhura; Haththotuwa, Isuru; Arachchi, Charitha Dandeniya; Perera, Shehan; Fernando, Dilina; Weerakoon, Sajith

doi:10.1145/2261605.2261637

Proceedings of the 6th Euro American Conference on Telematics and Information Systems 2012

DOI: 10.1145/2261605.2261637

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Madhura Jayaratne

Isuru Haththotuwa

Charitha Dandeniya Arachchi

et al.

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Cited by 2 publications

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Improving Semantic Queries by Utilizing UNL Ontology and a Graph Database

Kivikangas

Ishizuka

2012

2012 IEEE Sixth International Conference on Semantic Computing

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This paper describes an approach for improving semantic queries by utilizing Universal Words (UWs) and a graph database. Concept Description Language (CDL) is used for representing the semantic data, and Neo4j graph database is used as the storage back-end. Cypher graph query language is used as the basis for implementing the semantic queries. For improving the queries, query expansion is performed by utilizing semantic relationships between UWs provided by UNL Ontology.

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Improving Semantic Queries by Utilizing UNL Ontology and a Graph Database

Kivikangas

Ishizuka

2012

2012 IEEE Sixth International Conference on Semantic Computing

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Semantic Information Retrieval based on Topic Modeling and Community Interests Mining

Rajapaksha

Silva

2019

2019 Moratuwa Engineering Research Conference (MERCon)

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scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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iSeS

Cited by 2 publications

References 7 publications

Improving Semantic Queries by Utilizing UNL Ontology and a Graph Database

Improving Semantic Queries by Utilizing UNL Ontology and a Graph Database

Semantic Information Retrieval based on Topic Modeling and Community Interests Mining

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