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MyMemex server consists of a web server, a data collection agent, a file handler, and a database. The data collection agent makes connections to the company web services and stores the collected "web data" (phone logs, credit card usage logs, e- mails, and so on) to the memex database. The web server enables the users to view the collected data and get the results for the queries. The users...
Ontology mapping is a crucial task for achieving large scale semantic inter-operation of heterogeneous information sources. Conventional mapping solutions mainly focus on the mapping of a pair of ontologies. While such approaches are effective in creating one-to-one ontology mappings, they are less efficient when dealing with the many-to-many ontology mapping scenarios. To cope with the complexity...
Extension of ontology instance is the important part of ontology maintenance. In this paper, a novel and effective method is proposed to extending ontology instances from Chinese free text, which is achieved with classification using support vector machine (SVM). Firstly, classification features are extracted in terms of syntax and semantics from the training texts and the new texts based on the existed...
Question classification is very important in question answering system. This paper presents our research about question classification in a real-world on-line interactive question answering system in computer service & support domain. In the domain, questions are divided into 15 cursory categories and 220 sub-categories. The difference of this system is that standard question sentences represent...
Ontology learning has become a major area of research whose goal is to facilitate the construction of ontologies by decreasing the amount of effort required to produce an ontology for a new domain. However, there are few studies that attempt to automate the entire ontology learning process from the collection of domain-specific literature, to text mining to build new ontologies or enrich existing...
In this paper we compare the effectiveness of using morphological and ontological information for text categorization. We induce morphological information using stemmed features. Ontological information, on the other hand, has been induced in the form of WordNet hypernyms. We form text representations based on stemming and hypernyms. Those representations are evaluated using four different machine...
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