Association Link Network: An Incremental Web Resources Link Model for Learning Resources Management.
ABSTRACT Association Link Network (ALN) is proposed to establish association relations among Web resources, aiming at extending the hyperlink-based Web to an association-rich network and effectively supporting Web intelligence activities, such as Web-based learning. However, it is difficult to build the ALN one-off by direct computing since the huge number and quickly increasing learning resources on the Web. Thus, how to rapidly and accurately acquire the association relations between the new coming and existing learning resources has become a challenge in the incrementally building process of ALN. In this paper, a new algorithm is developed for incrementally updating ALN to cater for the dynamic management of learning resources increasing with time.
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ABSTRACT: SUMMARY A semantic link network (SLN) consists of nodes (entities, features, concepts, schemas or communities) and semantic links between nodes. This paper proposes an autonomous SLN formalism to support intelligent applications on large-scale networks. The formalism integrates the SLN logical reasoning with the SLN analogical reasoning and the SLN inductive reasoning, as well as existing techniques to form an autonomous semantic overly. The SLN logical reasoning mechanism derives implicit semantic relations by a semantic matrix and relevant addition and multiplication operations based on semantic link rules. The SLN analogical reasoning mechanism proposes conjectures on semantic relations based on structural mapping between nodes. The SLN inductive reasoning mechanism derives general semantics from special semantics. The cooperation of diverse reasoning mechanisms enhances the reasoning ability of each, therefore providing a powerful semantic ability for the semantic overlay. The self-organizing diverse scales of the SLN support the intelligent applications of the Knowledge Grid. Copyright cConcurrency and Computation: Practice and Experience. 01/2007; 19:1065-1085.
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ABSTRACT: Association link network (ALN) is used to establish associated relations among various resources, aiming at extending the hyperlink network World Wide Web to an association-rich network, for effectively supporting Web intelligence activities. Unfortunately, with the increase number of Web resources, the challenge of incremental building of ALN is on how to perform the association weight of the new coming Web resources efficiently and exactly. A naive way is to compare every pair of data in the existing ALN, thus bearing a O(n2) time complexity. Given the scale of the Web, it is unrealistic to compute the association weight between the new coming Web data and each data in the existing ALN, respectively. In this paper, a new method based on All-Pairs algorithm for incremental building of ALN is proposed. The experiments and evaluations show that our incremental building method performs a high accuracy. Moreover, the scale-independent property of our method make it more appropriate to be used on the Web.IEEE 15th International Conference on Parallel and Distributed Systems, ICPADS 2009, 8-11 December 2009, Shenzhen, China; 01/2009
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ABSTRACT: Discovering the significant relations embedded in documents would be very useful not only for information retrieval but also for question answering and summarization. Prior methods for relation discovery, however, needed large annotated corpora which cost a great deal of time and effort. We propose an unsupervised method for relation discovery from large corpora. The key idea is clustering pairs of named entities according to the similarity of context words intervening between the named entities. Our experiments using one year of newspapers reveals not only that the relations among named entities could be detected with high recall and precision, but also that appropriate labels could be automatically provided for the relations.07/2004;