Combining a Co-occurrence-Based and a Semantic Measure for Entity Linking. Pereira Nunes, B., Dietze, S., Casanova, M. A., Kawase, R., Fetahu, B., & Nejdl, W. In Hutchison, D., Kanade, T., Kittler, J., Kleinberg, J. M., Mattern, F., Mitchell, J. C., Naor, M., Nierstrasz, O., Pandu Rangan, C., Steffen, B., Sudan, M., Terzopoulos, D., Tygar, D., Vardi, M. Y., Weikum, G., Cimiano, P., Corcho, O., Presutti, V., Hollink, L., & Rudolph, S., editors, The Semantic Web: Semantics and Big Data, volume 7882, pages 548–562. Springer Berlin Heidelberg, Berlin, Heidelberg, 2013. ![Combining a Co-occurrence-Based and a Semantic Measure for Entity Linking [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex One key feature of the Semantic Web lies in the ability to link related Web resources. However, while relations within particular datasets are often well-defined, links between disparate datasets and corpora of Web resources are rare. The increasingly widespread use of cross-domain reference datasets, such as Freebase and DBpedia for annotating and enriching datasets as well as documents, opens up opportunities to exploit their inherent semantic relationships to align disparate Web resources. In this paper, we present a combined approach to uncover relationships between disparate entities which exploits (a) graph analysis of reference datasets together with (b) entity co-occurrence on the Web with the help of search engines. In (a), we introduce a novel approach adopted and applied from social network theory to measure the connectivity between given entities in reference datasets. The connectivity measures are used to identify connected Web resources. Finally, we present a thorough evaluation of our approach using a publicly available dataset and introduce a comparison with established measures in the field.
@incollection{hutchison_combining_2013,
address = {Berlin, Heidelberg},
title = {Combining a {Co}-occurrence-{Based} and a {Semantic} {Measure} for {Entity} {Linking}},
volume = {7882},
isbn = {978-3-642-38287-1 978-3-642-38288-8},
url = {http://link.springer.com/10.1007/978-3-642-38288-8_37},
abstract = {One key feature of the Semantic Web lies in the ability to link related Web resources. However, while relations within particular datasets are often well-defined, links between disparate datasets and corpora of Web resources are rare. The increasingly widespread use of cross-domain reference datasets, such as Freebase and DBpedia for annotating and enriching datasets as well as documents, opens up opportunities to exploit their inherent semantic relationships to align disparate Web resources. In this paper, we present a combined approach to uncover relationships between disparate entities which exploits (a) graph analysis of reference datasets together with (b) entity co-occurrence on the Web with the help of search engines. In (a), we introduce a novel approach adopted and applied from social network theory to measure the connectivity between given entities in reference datasets. The connectivity measures are used to identify connected Web resources. Finally, we present a thorough evaluation of our approach using a publicly available dataset and introduce a comparison with established measures in the field.},
language = {en},
urldate = {2019-01-15},
booktitle = {The {Semantic} {Web}: {Semantics} and {Big} {Data}},
publisher = {Springer Berlin Heidelberg},
author = {Pereira Nunes, Bernardo and Dietze, Stefan and Casanova, Marco Antonio and Kawase, Ricardo and Fetahu, Besnik and Nejdl, Wolfgang},
editor = {Hutchison, David and Kanade, Takeo and Kittler, Josef and Kleinberg, Jon M. and Mattern, Friedemann and Mitchell, John C. and Naor, Moni and Nierstrasz, Oscar and Pandu Rangan, C. and Steffen, Bernhard and Sudan, Madhu and Terzopoulos, Demetri and Tygar, Doug and Vardi, Moshe Y. and Weikum, Gerhard and Cimiano, Philipp and Corcho, Oscar and Presutti, Valentina and Hollink, Laura and Rudolph, Sebastian},
year = {2013},
doi = {10.1007/978-3-642-38288-8_37},
pages = {548--562},
}
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