TY - GEN
T1 - Path-based semantic relatedness on linked data and its use to word and entity disambiguation
AU - Hulpuş, Ioana
AU - Prangnawarat, Narumol
AU - Hayes, Conor
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Semantic relatedness and disambiguation are fundamental problems for linking text documents to the Web of Data. There are many approaches dealing with both problems but most of them rely on word or concept distribution over Wikipedia. They are therefore not applicable to concepts that do not have a rich textual description. In this paper, we show that semantic relatedness can also be accurately computed by analysing only the graph structure of the knowledge base. In addition, we propose a joint approach to entity and word-sense disambiguation that makes use of graph-based relatedness. As opposed to the majority of state-of-the-art systems that target mainly named entities, we use our approach to disambiguate both entities and common nouns. In our experiments, we first validate our relatedness measure on multiple knowledge bases and ground truth datasets and show that it performs better than related state-of-the-art graph based measures. Afterwards, we evaluate the disambiguation algorithm and show that it also achieves superior disambiguation accuracy with respect to alternative state-ofthe- art graph-based algorithms.
AB - Semantic relatedness and disambiguation are fundamental problems for linking text documents to the Web of Data. There are many approaches dealing with both problems but most of them rely on word or concept distribution over Wikipedia. They are therefore not applicable to concepts that do not have a rich textual description. In this paper, we show that semantic relatedness can also be accurately computed by analysing only the graph structure of the knowledge base. In addition, we propose a joint approach to entity and word-sense disambiguation that makes use of graph-based relatedness. As opposed to the majority of state-of-the-art systems that target mainly named entities, we use our approach to disambiguate both entities and common nouns. In our experiments, we first validate our relatedness measure on multiple knowledge bases and ground truth datasets and show that it performs better than related state-of-the-art graph based measures. Afterwards, we evaluate the disambiguation algorithm and show that it also achieves superior disambiguation accuracy with respect to alternative state-ofthe- art graph-based algorithms.
UR - https://www.scopus.com/pages/publications/84952342378
U2 - 10.1007/978-3-319-25007-6_26
DO - 10.1007/978-3-319-25007-6_26
M3 - Conference Publication
SN - 9783319250069
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 442
EP - 457
BT - The Semantic Web – ISWC 2015 - 14th International Semantic Web Conference, Proceedings
A2 - Arenas, Marcelo
A2 - Corcho, Oscar
A2 - Simperl, Elena
A2 - Strohmaier, Markus
A2 - d’Aquin, Mathieu
A2 - Srinivas, Kavitha
A2 - Groth, Paul
A2 - Dumontier, Michel
A2 - Heflin, Jeff
A2 - Thirunarayan, Krishnaprasad
A2 - Staab, Steffen
PB - Springer-Verlag
T2 - 14th International Semantic Web Conference, ISWC 2015
Y2 - 11 October 2015 through 15 October 2015
ER -