Publication statistics

Pub. period:2004-2011
Pub. count:4
Number of co-authors:8


Number of publications with 3 favourite co-authors:

Robert Stevens:
Xianpei Han:
Le Sun:



Productive colleagues

Jun Zhao's 3 most productive colleagues in number of publications:

Carole Goble:29
Robert Stevens:18
Guoyin Wang:6

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Jun Zhao


Publications by Jun Zhao (bibliography)

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Han, Xianpei, Sun, Le and Zhao, Jun (2011): Collective entity linking in web text: a graph-based method. In: Proceedings of the 34th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2011. pp. 765-774.

Entity Linking (EL) is the task of linking name mentions in Web text with their referent entities in a knowledge base. Traditional EL methods usually link name mentions in a document by assuming them to be independent. However, there is often additional interdependence between different EL decisions, i.e., the entities in the same document should be semantically related to each other. In these cases, Collective Entity Linking, in which the name mentions in the same document are linked jointly by exploiting the interdependence between them, can improve the entity linking accuracy. This paper proposes a graph-based collective EL method, which can model and exploit the global interdependence between different EL decisions. Specifically, we first propose a graph-based representation, called Referent Graph, which can model the global interdependence between different EL decisions. Then we propose a collective inference algorithm, which can jointly infer the referent entities of all name mentions by exploiting the interdependence captured in Referent Graph. The key benefit of our method comes from: 1) The global interdependence model of EL decisions; 2) The purely collective nature of the inference algorithm, in which evidence for related EL decisions can be reinforced into high-probability decisions. Experimental results show that our method can achieve significant performance improvement over the traditional EL methods.

© All rights reserved Han et al. and/or ACM Press

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Wei, Xiujian and Zhao, Jun (2005): Citizens' requirement analysis in Chinese e-government. In: Li, Qi and Liang, Ting-Peng (eds.) Proceedings of the 7th International Conference on Electronic Commerce - ICEC 2005 August 15-17, 2005, Xian, China. pp. 525-428.

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Wang, Guoyin, Zhao, Jun, An, Jiu-Jiang and Wu, Yu (2004): Theoretical Study on Attribute Reduction of Rough Set Theory: Comparison of Algebra and Information Views. In: Chan, Christine W., Kinsner, Witold, Wang, Yingxu and Miller, D. Michael (eds.) Proceedings of the 3rd IEEE International Conference on Cognitive Informatics ICCI 2004 16-17 August, 2004, Victoria, Canada. pp. 148-155.

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Zhao, Jun, Goble, Carole and Stevens, Robert (2004): Semantic web applications to e-science in silico experiments. In: Proceedings of the 2004 International Conference on the World Wide Web 2004. pp. 284-285.

This paper explains our research and implementations of manual, automatic and deep annotations of provenance logs for e-Science in silico experiments. Compared to annotating general Web documents, annotations for scientific data require more sophisticated professional knowledge to recognize concepts from documents, and more complex text extraction and mapping mechanisms. A simple automatic annotation approach based on "lexicons" and a deep annotation implemented by semantically populating, translating and annotating provenance logs are introduced in this paper. We used COHSE (Conceptual Open Hypermedia Services Environment) to annotate and browse provenance logs from my Grid project, which are conceptually linked together as a hypertext Web of provenance logs and experiment resources, based on the associated conceptual metadata and reasoning over these metadata.

© All rights reserved Zhao et al. and/or ACM Press

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