A large volume of research documents are available online for us to access and analysis. It is very important to detect and mine the dynamics of the research topics from these large corpora. In this paper, we propose an improved method by introducing WordNet to LDA. This approach is to find latent topics of large corpora, and then we propose many methods to analyze the dynamics of those topics. We apply the methodology to two large document collections: 1940 papers from NIPS 00-13 (1987-2000) and 2074 papers from NIPS 14-23 (2001-2010). Six experiments are conducted on the two corpora and the experimental results show that our method is better than LDA in finding research topics and is feasible in discovering the dynamics of research topics. INDEX TERMS Research topics mining, dynamics of research topics, latent Dirichlet allocation, WordNet, large corpora.

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In an emergency response system (ERS), resource matchmaking schema (RMS) consists of emergency impact assessment, resource utility classification and mapping between impact and utility. These tasks serve the resource allocation and must be done before launching rescue activities. However, related concepts and relations have little explication. Consequently, an adequate knowledge structure is necessary. In this paper, a domain ontology for RMS (RMS_Ontology) is formally defined, on which impact-utility mapping and rule-based reasoning process are implemented. Based on the ontology defined, the authors illustrate how RMS is realized and the way reasoning process works in the ERS application of highway network under severe weather conditions. Finally, a prototype system has been developed to facilitate knowledge management and to improve the performance of emergency disposal procedures.

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