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Author: search.filters.author.Chittayasothorn, SuphamitSubject: search.filters.subject.Ontology

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    Some key issues in information systems, databases and big data integration
    (2019-07-01)
    Chittayasothorn, Suphamit
    Databases and big data integration are keywords which attract attentions from both government and private sectors' administrators. In general, the term big data refers to data of large volume which come in different varieties and high velocity. Typical big data sources are from sensors of various kinds and social media. Transactional data from enterprise information systems with traditional structured databases are also major data sources for management decision support systems. This paper presents some key big data and database integration issues and suggests the level of integration required.
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    Semi-automatic relational databases integration using ontology
    (2013-12-16)
    Phungtua-Eng, Thanapol
    ;
    Chittayasothorn, Suphamit
    Database integration is an organization-level problem which has become more and more serious when correct and unified databases are required to support business intelligence and management decision making. In many organizations, information systems are developed independently. The database that supports the human resource management system, for example, is developed and implemented separately from the database that supports the main production system. Different naming conventions, data types, and values in such different databases make it hard to consider if the data items from different databases refer to the same real-world objects. The problems become worse in the case that databases have different data structures; different data models. Polyglot environments may be a solution for operational systems but may turn to be problems for decision support systems. This paper presents an approach to the database integration problem. Ontology is used as a central knowledge base where data items and relationships are identified and resolved. Since the database integration process must yield perfect or close to perfect result, any mismatches or errors are not acceptable and user involvements are required. Hence, a semi-automatic approach is adopted.
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    An RDF-based distributed expert system
    (2009-09-04)
    Prapakorn, Napat
    ;
    Chittayasothorn, Suphamit
    An expert system or knowledge-based system comprises of a knowledge base and an inference engine in which their expertise knowledge is represented. The knowledge can be called upon when needed to solve a problem by the inference engine. In a large expert system, the knowledge base can be represented using frames. Hence they are called frame-based expert system or frame-based system. To avoid too many communication traffics during inferences, a distributed expert system, an expert system with an inference engine on its external knowledge base side is presented. It is an expert system which has an RDF external knowledge base for improved flexibility and mobility in knowledge sharing. This research presents a design and implementation of a Frame-based RDF expert system which has a RDF/XML database as its external knowledge base. The external knowledge base uses frame as its knowledge representation stored in RDF/XML format so that it can be placed on the WWW (World Wide Web) which is, ideally, accessible from anywhere. With this capability, an expert system will be enriched with flexibility and mobility in knowledge sharing.
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    An ontology-based case matching technique
    (2007-12-01)
    Tanawong, Tawin
    ;
    Chittayasothorn, Suphamit
    Case-based reasoning is an artificial intelligence technique that refers to past experiences (cases) to help making decisions or taking actions. An important part of case based reasoning is case matching. Previous cases which are similar to a current given case are matched and retrieved. This paper presents a case matching technique that transforms input surface structure sentences into deep-structure sentences which can be represented by fact types of a conceptual schema model. Each entity type that is involved in a fact type is further described by ontology. The matching result of the proposed technique is compared with those of other classical technique. Better similarity of the case matching is found.