2015 IncorporatingWorldKnowledgetoDo

• (Wang et al., 2015) ⇒ Chenguang Wang, Yangqiu Song, Ahmed El-Kishky, Dan Roth, Ming Zhang, and Jiawei Han. (2015). “Incorporating World Knowledge to Document Clustering via Heterogeneous Information Networks.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783374

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Cited By

• http://scholar.google.com/scholar?q=%222015%22+Incorporating+World+Knowledge+to+Document+Clustering+via+Heterogeneous+Information+Networks
• http://dl.acm.org/citation.cfm?id=2783258.2783374&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; document clustering; heterogeneous information network; knowledge base; knowledge graph; world knowledge

Abstract

One of the key obstacles in making learning protocols realistic in applications is the need to supervise them, a costly process that often requires hiring domain experts. We consider the framework to use the world knowledge as indirect supervision. World knowledge is general-purpose knowledge, which is not designed for any specific domain. Then the key challenges are how to adapt the world knowledge to domains and how to represent it for learning. In this paper, we provide an example of using world knowledge for domain dependent document clustering. We provide three ways to specify the world knowledge to domains by resolving the ambiguity of the entities and their types, and represent the data with world knowledge as a heterogeneous information network. Then we propose a clustering algorithm that can cluster multiple types and incorporate the sub-type information as constraints. In the experiments, we use two existing knowledge bases as our sources of world knowledge. One is Freebase, which is collaboratively collected knowledge about entities and their organizations. The other is YAGO2, a knowledge base automatically extracted from Wikipedia and maps knowledge to the linguistic knowledge base, WordNet. Experimental results on two text benchmark datasets (20newsgroups and RCV1) show that incorporating world knowledge as indirect supervision can significantly outperform the state-of-the-art clustering algorithms as well as clustering algorithms enhanced with world knowledge features.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 IncorporatingWorldKnowledgetoDo	Yangqiu Song Ming Zhang Ahmed El-Kishky Chenguang Wang Dan Roth Jiawei Han			Incorporating World Knowledge to Document Clustering via Heterogeneous Information Networks				10.1145/2783258.2783374		2015