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In this paper, we describe our work on developing a model and method for extracting key entities from the online social messages regarding emergent events for enhancing ontology engineering, enabling a sensible solution for prevention of similar disasters. Our work started with the development of an event modelling system using a data-cluster slicing approach, which combines analytics of social data and event lifecycle algorithms, allowing for large-scale emerging novel events to be quickly and accurately analyzed. Subsequently, our system computes the energy of each collected event data sets, and then encapsulates ranked temporal, spatial and topical keywords into a structured node for event-entity extraction, in order to update event ontologies for fast response of emergent events. The preliminary experimental results demonstrate that our developed system is workable, allowing for prediction of possible evolution and early warning of critical incidents with a support of dynamic entity extraction.

Topic maps provide a general, powerful, and user-oriented way to navigate the information resources under consideration in any specific domain. A topic map provides a uniform framework that not only identifies important subjects from an entity of information resources and specifies the resources that are semantically related to a subject, but also explores the relations among these subjects. When a user needs to find some specific information on a pool of information resources, he or she only needs to examine the topic maps of this pool, select the topic that seems interesting, and the topic maps will display the information resources that are related to this topic, as well as its related topics. The user will also recognize the relationships among these topics and the roles they play in such relationships. With the help of the topic maps, you no longer have to browse through a set of hyperlinked documents and hope that you may eventually reach the information you need in a finite amount of time, while knowing nothing about where to start. You also don’t have to gather some words and hope that they may perfectly symbolize the idea you’re interested in, and be well-conceived by a search engine to obtain reasonable result. Topic maps provide a way to navigate and organize information, as well as create and maintain knowledge in an infoglut. To construct a topic map for a set of information resources, human intervention is unavoidable at the present time. Human effort is needed in tasks such as selecting topics, identifying their occurrences, and revealing their associations. Such a need is acceptable only when the topic maps are used merely for navigation purposes and when the volume of the information resource is considerably small. However, a topic map should not only be a topic navigation map. The volume of the information resource under consideration is generally large enough to prevent the manual construction of topic maps. To expand the applicability of topic maps, some kind of automatic process should be involved during the construction of the maps. The degree of automation in such a construction process may vary for different users with different needs. One person may need only a friendly interface to automate the topic map authoring process, while another may try to automatically identify every component of a topic map for a set of information resources from the ground up. In this article, we recognize the importance of topic maps not only as a navigation tool but also as a desirable scheme for knowledge acquisition and representation. According to such recognition, we try to develop a scheme based on a proposed text-mining approach to automatically construct topic maps for a set of information resources. Our approach is the opposite of the navigation task performed by a topic map to obtain information. We extract knowledge from a corpus of documents to construct a topic map. Although currently the proposed approach cannot fully construct the topic maps automatically, our approach still seems promising in developing a fully automatic scheme for topic map construction.

Recently, many approaches have been devised for mining various kinds of knowledge from texts. One important application of text mining is to identify themes and the semantic relations among these themes for text categorization. Traditionally, these themes were arranged in a hierarchical manner to achieve effective searching and indexing as well as easy comprehension for human beings. The determination of category themes and their hierarchical structures was mostly done by human experts. In this work, we developed an approach to automatically generate category themes and reveal the hierarchical structure among them. We also used the generated structure to categorize text documents. The document collection was trained by a self-organizing map to form two feature maps. We then analyzed these maps and obtained the category themes and their structure. Although the test corpus contains documents written in Chinese, the proposed approach can be applied to documents written in any language, and such documents can be transformed into a list of separated terms.