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Knowledge fusion framework based on Web page
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| Sikang HU1,Yuanda CAO2, |
| 1.School of Computer
Science and Technology, Beijing Institute of Technology, Beijing 100081,
China; 2.School of Software,
Beijing Institute of Technology, Beijing 100081, China; |
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Abstract With the proliferation ofWeb page texts, it is important to fuse these texts to useful documents that users need. However, there is still no complete and unified theoretical model for studying the research issues including redundancy, localization, and fuzziness existing in the process of fusing Web page texts. This paper proposes a fusion framework calledWeb Pages Knowledge Fusion Framework (WPKFF) to synthesize the knowledge of Web page texts. First, sentences in Web page texts are extracted and transformed into triple semantic net as knowledge representation. Then a semantic description of attribute fusion rules, description information fusion rules and attribute-value and description information fusion rules are defined in WPKFF. These rules are used to fuse the attributes of same domain concepts in triple semantic net. The features of attributes include description (string) and value data (number). The results of the experiments indicate that the fusion framework is a feasible model in terms of precision and recall.
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| Keywords
fusion framework
fusion rules
Web text formal semanteme
knowledge acquisition
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Issue Date: 05 December 2009
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Feigenbaun E. Somechallenges and grand challenges for computational intelligence. Journal of the ACM, 2003, 50(1): 32―40
doi: 10.1145/602382.602400
|
|
Lin C Y, Hovy E. From single to multi-documentsummarization: a prototype system and its evaluation. In: Proceedings of the 40th AnnualMeeting of the Association for ComputationalLinguistics (ACL). Philadelphia, 2002, 457―464
|
|
Radev D R, Jing H Y, Budzikowska M. Centroid-based summarization of multiple documents: sentenceextraction, utility-based evaluation, and user studies.In: Proceedings of ANLP/NAACL 2000 Workshop. Seattle, 2000, 21―29
|
|
Fung P, Ngai G. Combining optimal clusteringand hidden markov model for extractive. In: Proceedingsof the ACL 2003 workshop on multilingual summarization and questionanswering. Sapporo, 2003: 21―28
|
|
Hunter A, Summerton R. Fusion rules for context-dependentaggregation of structured news reports. Journal of Applied Non-classical Logic, 2004, 14(3): 329―366
doi: 10.3166/jancl.14.329-366
|
|
Hunter A, Liu W. Fusion rules for merginguncertain information. Information Fusion, 2006, 7(1): 97―134
|
|
Hunter A, Summerton R. A knowledge-based approachto merging information, Knowledge-BasedSystems. 2006, 19(8): 647―674
doi: 10.1016/j.knosys.2006.05.007
|
|
Hunter A, Summerton R. Propositional fusion rules. In: Proceedings of LNCS. Springer, 2003, 2711: 502―514
|
|
Chuang W T, Yang J. Extracting sentence segmentsfor text summarization: a machine learning approach. In: Proceedings of the 23rd annual international ACM SIGIR, 2000, 152―159
|
|
Grégoire É, Sofiane A. Fusing syntax and semanticsin knowledge fusion. In: Proceedings ofEUSFLAT Conference, 2001, 414―417
|
|
Sui Y F, Gao Y, Cao C G. Ontologies, frames and logical theories in NKI. Journal of Software, 2005, 12(16): 2045―2053
doi: 10.1360/jos162045
|
|
Anokhin P, Motro A. Fusionplex: resolution ofdata inconsistencies in the integration of heterogeneous Iinformationsources. Technical Report ISE-TR-03-06,Information and Software Engineering Dept., George Mason Univ., Fairfax,Virginia, 2003
|
|
Berberich K, Vazirgiannis M, Weikum G. T-Rank: time-aware authority ranking. In: Proceedings of WAW, 2004, 131―142
|
|
Xie N F. Knowledge Fusion and Synchronization Methods Based on Semantic WebTechnologies. Ph. D. dissertation, GraduateSchool of the Chinese Academy of Sciences, 2005 (in Chinese)
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