Authors:
Özgür Bağlıoğlu
and
Mesut Çeviker
Affiliation:
Middle East Technical University, Turkey
Keyword(s):
Ontology, Knowledge Extraction, Semantic Search, Information Extraction, Information Systems, Knowledge Discovery.
Abstract:
Public and private enterprise finance performance is reflected and affected by unorganized, unstructured data such as news, reports (IMF, OECD and other periodical reports) as well as structured statistical data extracted by Statistical Institutes and other organizations. The role of raw data in influencing performance and decision making is not negligible. In this context, this paper presents knowledge extraction methodology for precise and fast decision making in finance by using ontological tools. For this purpose, we firstly design finance ontology and collect datasets. The aim of this ontology is to support the knowledge management in the finance domain and to increase the productivity through evidence base, comprising raw finance data to be retrieved from various operational sources. We then propose to populate the ontology by using past project properties and project progress reports. After population of data, we plan to develop and use a semantic search engine to gather meani
ngful data i.e. knowledge. The semantic search engine will assist decision makers to make better decisions. The output of this work will be also used as an input for decision making and scenario based future prediction for finance as this study is a part of a larger project called “ontology based decision support system”.
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