Paper Info
Title
Multi-lingual Concept Extraction with Linked Data and Human-in-the-Loop
Abstract
Ontologies are dynamic artifacts that evolve both in structure and content. Keeping them up-to-date is a very expensive and critical operation for any application relying on semantic Web technologies. In this paper we focus on evolving the content of an ontology by extracting relevant instances of ontological concepts from text. We propose a novel technique which is (i) completely language independent, (ii) combines statistical methods with human-in-the-loop and (iii) exploits Linked Data as bootstrapping source. Our experiments on a publicly available medical corpus and on a Twitter dataset show that the proposed solution achieves comparable performances regardless of language, domain and style of text. Given that the method relies on a human-in-the-loop, our results can be safely fed directly back into Linked Data resources.
Year
DOI
Venue
2017
10.1145/3148011.3148021
K-CAP 2017: Knowledge Capture Conference Austin TX USA December, 2017
Field
DocType
ISBN
Data integration,Ontology (information science),Ontology,Information retrieval,Computer science,Bootstrapping,Linked data,Semantic Web,Exploit,Human-in-the-loop
Conference
978-1-4503-5553-7
Citations 
PageRank 
References 
0
0.34
23
Authors
6
Name
Order
Citations
PageRank
Alfredo Alba1779.87
Anni Coden234546.53
Anna Lisa Gentile320026.00
Daniel Gruhl42282434.45
petar ristoski525621.36
Steve Welch6104.63