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Output details

13 - Electrical and Electronic Engineering, Metallurgy and Materials

University of Surrey

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Output 232 of 311 in the submission

Article title

Probabilistic Topic Models for Learning Terminological Ontologies

Type

D - Journal article

DOI

10.1109/TKDE.2009.122

Title of journal

IEEE Transactions on Knowledge and Data Engineering

Article number

Volume number

Issue number

First page of article

1028

ISSN of journal

1041-4347

Year of publication

2010

URL

Number of additional authors

Additional information

This presents a significant breakthrough in automated creation of ontologies from scratch. Terminological ontologies created by our proposed method are significantly accurate and can be directly used for semantic search and reasoning purposes. We also received comments from the National Science Foundation (NSF) in the US that they found it an interesting approach and the work was recommended to researchers in this field. It also led to further development of a semantic knowledge-base creation and search engine for finding and ranking scientific papers. Another interesting aspect of the work is being domain independent that makes it applicable to different domains.

Interdisciplinary

Cross-referral requested

Research group

None

Proposed double-weighted

Double-weighted statement

Reserve for a double-weighted output

Non-English

English abstract