Diese Datenbank enthält über 40.000 Dokumente zu Themen aus den Bereichen Formalerschließung – Inhaltserschließung – Information Retrieval.
© 2015 W. Gödert, TH Köln, Institut für Informationswissenschaft / Powered by litecat, BIS Oldenburg (Stand: 28. April 2022)
1Liu, D.-R. ; Chen, Y.-H. ; Shen, M. ; Lu, P.-J.: Complementary QA network analysis for QA retrieval in social question-answering websites.
In: Journal of the Association for Information Science and Technology. 66(2015) no.1, S.99-116.
Abstract: With the ubiquity of the Internet and the rapid development of Web 2.0 technology, social question and answering (SQA) websites have become popular knowledge-sharing platforms. As the number of posted questions and answers (QAs) continues to increase rapidly, the massive amount of question-answer knowledge is causing information overload. The problem is compounded by the growing number of redundant QAs. SQA websites such as Yahoo! Answers are open platforms where users can freely ask or answer questions. Users also may wish to learn more about the information provided in an answer so they can use related keywords in the answer to search for extended, complementary information. In this article, we propose a novel approach to identify complementary QAs (CQAs) of a target QA. We define two types of complementarity: partial complementarity and extended complementarity. First, we utilize a classification-based approach to predict complementary relationships between QAs based on three measures: question similarity, answer novelty, and answer correlation. Then we construct a CQA network based on the derived complementary relationships. In addition, we introduce a CQA network analysis technique that searches the QA network to find direct and indirect CQAs of the target QA. The results of experiments conducted on the data collected from Yahoo! Answers Taiwan show that the proposed approach can more effectively identify CQAs than can the conventional similarity-based method. Case and user study results also validate the helpfulness and the effectiveness of our approach.
Inhalt: Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23155/abstract.
2Shen, M. ; Liu, D.-R. ; Huang, Y.-S.: Extracting semantic relations to enrich domain ontologies.
In: Journal of Intelligent Information Systems.
Abstract: Domain ontologies facilitate the organization, sharing and reuse of domain knowledge, and enable various vertical domain applications to operate successfully. Most methods for automatically constructing ontologies focus on taxonomic relations, such as is-kind-of and is- part-of relations. However, much of the domain-specific semantics is ignored. This work proposes a semi-unsupervised approach for extracting semantic relations from domain-specific text documents. The approach effectively utilizes text mining and existing taxonomic relations in domain ontologies to discover candidate keywords that can represent semantic relations. A preliminary experiment on the natural science domain (Taiwan K9 education) indicates that the proposed method yields valuable recommendations. This work enriches domain ontologies by adding distilled semantics.
Inhalt: Vgl.: http://www.springerlink.com/content/f493xxq201163354/.
Themenfeld: Computerlinguistik ; Wissensrepräsentation