Ontology construction for information classification

What's the Problem?

Why they do this Work?

How they deal with the problem & solve the challenges?

Why this method works better & Any evidence?

Any shortcoming?

Backgrounds

Pantel, P., Lin, D.: Discovering word senses from text. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery
and data mining, ACM (2002) 613–619

provides a soft clustering algorithm called Clustering by Committee (CBC) which can assign words to differents clusters. This method only creates clusters of terms but it not create a hierarchical structure.

[21] Beil, Florian, Martin Ester, and Xiaowei Xu. "Frequent term-based text clustering." Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. 2002.
[22] Karoui, Lobna, Marie-aude Aufaure, and Nacera Bennacer. "Context-based hierarchical clustering for the ontology learning." 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings)(WI'06). IEEE, 2006.
[23] Sung, Sangsoo, Seokkyung Chung, and Dennis McLeod. "Efficient concept clustering for ontology learning using an event life cycle on the web." Proceedings of the 2008 ACM symposium on Applied computing. 2008.
[24] Yeh, Jian-hua, and Naomi Yang. "Ontology construction based on latent topic extraction in a digital library." International Conference on Asian Digital Libraries. Springer, Berlin, Heidelberg, 2008.

Text clustering algorithms have been used in ontology learning [21, 22, 23, 24] but such works do not consider the ambiguous words or their semantic relationships.

Further Readings

References