4,559 research outputs found
Modeling of Social Transitions Using Intelligent Systems
In this study, we reproduce two new hybrid intelligent systems, involve three
prominent intelligent computing and approximate reasoning methods: Self
Organizing feature Map (SOM), Neruo-Fuzzy Inference System and Rough Set Theory
(RST),called SONFIS and SORST. We show how our algorithms can be construed as a
linkage of government-society interactions, where government catches various
states of behaviors: solid (absolute) or flexible. So, transition of society,
by changing of connectivity parameters (noise) from order to disorder is
inferred
Knowledge Engineering in Search Engines
With large amounts of information being exchanged on the Internet, search engines have become the most popular tools for helping users to search and filter this information. However, keyword-based search engines sometimes obtain information, which does not meet userā needs. Some of them are even irrelevant to what the user queries. When the users get query results, they have to read and organize them by themselves. It is not easy for users to handle information when a search engine returns several million results. This project uses a granular computing approach to find knowledge structures of a search engine. The project focuses on knowledge engineering components of a search engine. Based on the earlier work of Dr. Lin and his former student [1], it represents concepts in the Web by simplicial complexes. We found that to represent simplicial complexes adequately, we only need the maximal simplexes. Therefore, this project focuses on building maximal simplexes. Since it is too costly to analyze all Web pages or documents, the project uses the sampling method to get sampling documents. The project constructs simplexes of documents and uses the simplexes to find maximal simplexes. These maximal simplexes are regarded as primitive concepts that can represent Web pages or documents. The maximal simplexes can be used to build an index of a search engine in the future
Temporal Data Modeling and Reasoning for Information Systems
Temporal knowledge representation and reasoning is a major research field in Artificial
Intelligence, in Database Systems, and in Web and Semantic Web research. The ability to
model and process time and calendar data is essential for many applications like appointment
scheduling, planning, Web services, temporal and active database systems, adaptive
Web applications, and mobile computing applications. This article aims at three complementary
goals. First, to provide with a general background in temporal data modeling
and reasoning approaches. Second, to serve as an orientation guide for further specific
reading. Third, to point to new application fields and research perspectives on temporal
knowledge representation and reasoning in the Web and Semantic Web
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