Towards text knowledge engineering
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Deriving concept hierarchies from text
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Gimme' the context: context-driven automatic semantic annotation with C-PANKOW
WWW '05 Proceedings of the 14th international conference on World Wide Web
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Ontology change: Classification and survey
The Knowledge Engineering Review
A new algebraic structure for formal concept analysis
Information Sciences: an International Journal
Extending conceptualisation modes for generalised Formal Concept Analysis
Information Sciences: an International Journal
Evaluation of IPAQ questionnaires supported by formal concept analysis
Information Sciences: an International Journal
Mining gene expression data with pattern structures in formal concept analysis
Information Sciences: an International Journal
Information Sciences: an International Journal
Unsupervised link prediction using aggregative statistics on heterogeneous social networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Concept hierarchies, such as the ACM Computing Classification Scheme and InterPro Protein Sequence Classification, are widely used in categorization and indexing applications. In the Internet and Web 2.0 era, new concepts and terms are emerging on an almost daily basis, so it is essential that such hierarchies maintain up-to-date records of concepts. This paper proposes a mechanism to identify the most suitable position to insert new terms into an existing concept hierarchy. The problem is challenging because there are hundreds or even thousands of candidate positions for insertion. Furthermore, usually there is no training instance available for an insertion; nor is it practical to assume the availability of a detailed description of the target concept, except in the hierarchy itself. To resolve the problem, we exploit the topology, content and social information, and apply a learning approach to identify the underlying construction criteria of the concept hierarchy. We utilize three metrics (namely, accuracy, taxonomic closeness, and ranking) to evaluate the proposed learning-based approach on the ACM CCS, the DOAJ and the InterPro datasets to evaluate the proposed learning-based approach. The results demonstrate that, in all three metrics, our approach outperforms similarity-based approaches, such as the Normalized Google Distance, by a significant margin. Finally, we propose a level-based recommendation scheme as a novel application of our system. The source code, dataset, and other related resources are available at http://www.csie.ntu.edu.tw/~d97944007/refinement/.