Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

TechMiner: extracting technologies from academic publications

Osborne, Francesco, De Ribaupierre, Helene and Motta, Enrico 2016. TechMiner: extracting technologies from academic publications. Presented at: WKAW 2016: European Knowledge Acquisition Workshop, Bologna, Italy, 19-23 November 2016. Published in: Blomqvist, Eva, Ciancarini, Paolo, Poggi, Francesco and Vitali, Fabio eds. Knowledge Engineering and Knowledge Management: 20th International Conference, EKAW 2016, Bologna, Italy, November 19-23, 2016, Proceedings. Lecture Notes in Computer Science Cham: Springer, pp. 463-479. 10.1007/978-3-319-49004-5_30

Full text not available from this repository.

Abstract

In recent years we have seen the emergence of a variety of scholarly datasets. Typically these capture ‘standard’ scholarly entities and their connections, such as authors, affiliations, venues, publications, citations, and others. However, as the repositories grow and the technology improves, researchers are adding new entities to these repositories to develop a richer model of the scholarly domain. In this paper, we introduce TechMiner, a new approach, which combines NLP, machine learning and semantic technologies, for mining technologies from research publications and generating an OWL ontology describing their relationships with other research entities. The resulting knowledge base can support a number of tasks, such as: richer semantic search, which can exploit the technology dimension to support better retrieval of publications; richer expert search; monitoring the emergence and impact of new technologies, both within and across scientific fields; studying the scholarly dynamics associated with the emergence of new technologies; and others. TechMiner was evaluated on a manually annotated gold standard and the results indicate that it significantly outperforms alternative NLP approaches and that its semantic features improve performance significantly with respect to both recall and precision.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Publisher: Springer
ISBN: 9783319490038
ISSN: 0302-9743
Last Modified: 08 Jul 2019 08:44
URI: http://orca-mwe.cf.ac.uk/id/eprint/101015

Citation Data

Cited 5 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item