Joint entity and relation linking using EARL
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Joint entity and relation linking using EARL. / Banerjee, Debayan; Dubey, Mohnish; Chaudhuri, Debanjan et al.
ISWC 2018 Posters & Demonstrations, Industry and Blue Sky Ideas Tracks : Proceedings of the ISWC 2018 Posters & Demonstrations, Industry and Blue Sky Ideas Tracks, co-located with 17th International Semantic Web Conference (ISWC 2018), Monterey, USA, October 8th to 12th, 2018 . ed. / Marieke van Erp; Medha Atre; Vanessa Lopez; Kavitha Srinivas; Carolina Fortuna. Aachen : Sun Site Central Europe (RWTH Aachen University), 2018. (CEUR Workshop Proceedings; Vol. 2180).Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - Joint entity and relation linking using EARL
AU - Banerjee, Debayan
AU - Dubey, Mohnish
AU - Chaudhuri, Debanjan
AU - Lehmann, Jens
N1 - Conference code: 17
PY - 2018
Y1 - 2018
N2 - In order to answer natural language questions over knowledge graphs, most processing pipelines involve entity and relation linking. Traditionally, entity linking and relation linking have been performed either as dependent sequential tasks or independent parallel tasks. In this demo paper, we present EARL, which performs entity linking and relation linking as a joint single task. The system determines the best semantic connection between all keywords of the question by referring to the knowledge graph. This is achieved by exploiting the connection density between entity candidates and relation candidates. EARL uses Bloom filters for faster retrieval of connection density and uses an extended label vocabulary for higher recall to improve the overall accuracy.
AB - In order to answer natural language questions over knowledge graphs, most processing pipelines involve entity and relation linking. Traditionally, entity linking and relation linking have been performed either as dependent sequential tasks or independent parallel tasks. In this demo paper, we present EARL, which performs entity linking and relation linking as a joint single task. The system determines the best semantic connection between all keywords of the question by referring to the knowledge graph. This is achieved by exploiting the connection density between entity candidates and relation candidates. EARL uses Bloom filters for faster retrieval of connection density and uses an extended label vocabulary for higher recall to improve the overall accuracy.
KW - Entity Linking
KW - Question Answering
KW - Relation Linking
KW - Informatics
UR - http://www.scopus.com/inward/record.url?scp=85055354595&partnerID=8YFLogxK
M3 - Article in conference proceedings
AN - SCOPUS:85055354595
T3 - CEUR Workshop Proceedings
BT - ISWC 2018 Posters & Demonstrations, Industry and Blue Sky Ideas Tracks
A2 - van Erp, Marieke
A2 - Atre, Medha
A2 - Lopez, Vanessa
A2 - Srinivas, Kavitha
A2 - Fortuna, Carolina
PB - Sun Site Central Europe (RWTH Aachen University)
CY - Aachen
T2 - 17th International Semantic Web Conference - ISWC 2018
Y2 - 8 October 2018 through 12 October 2018
ER -