Mutual Transformation of Information and Knowledge
📝 Abstract
Information and knowledge are transformable into each other. Information transformation into knowledge by the example of rule generation from OWL (Web Ontology Language) ontology has been shown during the development of the SWES (Semantic Web Expert System). The SWES is expected as an expert system for searching OWL ontologies from the Web, generating rules from the found ontologies and supplementing the SWES knowledge base with these rules. The purpose of this paper is to show knowledge transformation into information by the example of ontology generation from rules.
💡 Analysis
Information and knowledge are transformable into each other. Information transformation into knowledge by the example of rule generation from OWL (Web Ontology Language) ontology has been shown during the development of the SWES (Semantic Web Expert System). The SWES is expected as an expert system for searching OWL ontologies from the Web, generating rules from the found ontologies and supplementing the SWES knowledge base with these rules. The purpose of this paper is to show knowledge transformation into information by the example of ontology generation from rules.
📄 Content
MUTUAL TRANSFORMATION OF INFORMATION AND KNOWLEDGE Olegs Verhodubs oleg.verhodub@inbox.lv
Abstract-Information and knowledge are transformable into each other. Information transformation into knowledge by the example of rule generation from OWL (Web Ontology Language) ontology has been shown during the development of the SWES (Semantic Web Expert System). The SWES is expected as an expert system for searching OWL ontologies from the Web, generating rules from the found ontologies and supplementing the SWES knowledge base with these rules. The purpose of this paper is to show knowledge transformation into information by the example of ontology generation from rules. Keywords-information;knowledge;ontologies;rules I. INTRODUCTION The development of the Web during the last few decades has led to the accumulation of large amount of info rmation in the common information environment. In 1997 search engines claimed to index from 2 million to 100 million web documents [1]. The number of documents, indexed by web search engines, is increasing from year to year. For example, Google has an index of over 30 trillion web pages now [2]. The information, indexed by web search engines, is not homogeneous, and it is presented in the Web in different forms. Web pages, different documents, pictures, ontologies, archives and others are these forms of information in the Web. In general, it is necessary to distinguish information, data and knowledge. This difference of data, information and knowledge will be explained hereinafter, but here it can be argued that this difference is a serious obstacle to the full use of the potential of the Web. There are several ways to eliminate this obstacle. For instance, it is theoretically possible to develop one unified language for the Web in order to represent data, information or knowledge, but in practice it is hardly feasible in terms of its use. In this connection, the way of mutual transformations of data, information and knowledge is the most suitable in terms of implementation. This work will not start from the scratch, because some types of these transformations have already been developed. Generation of rules from OWL (Web Ontology Language) ontology was investigated as part of SWES (Semantic Web Expert System) development [3], [4]. The development of the SWES is the main purpose of the research. An expert system, which is based on the Semantic Web technologies, is meant under the SWES. The SWES is being developed as the system, which looks for OWL ontologies from the Web, generates rules from the OWL ontologies and supplements its own knowledge base with these rules. These actions, as well as communication with the user, will give an opportunity to get the exact answer to the user’s request. This style of work is significantly different from the existing systems in the Web, which as a result provide a list of links to resources that may contain the answer. Moreover, it is expected that the SWES will extract more knowledge from the Web than existing systems are able to do so. It is logical to assume that the task of ontology generation from rules, which is the opposite task to the task of rule generation from ontology, is realizable. The main reason for confidence in the fact that this so, is the essence of data, information and knowledge. Point is that data, information and knowledge are a single entity having different forms. Different forms of a single entity are perceived as entities with fundamental differences. Apparently this is due to limitations in the human perception of reality. This paper will fit this gap. The purpose of this paper is to understand what are data, information and knowledge, as well as to find out their differences, and in addition to present the way of ontology generation from rules. This paper structured as follows. The next section clarifies terms such as data, information and knowledge. The third section introduces the new conception of data, information and knowledge. The fourth section describes the way of ontology generation from rules. Finally, the conclusions follow. II. BACKGROUND It is necessary to clarify the definitions of such concepts as data, information and knowledge. This is especially important, because many people, including professionals, are confused in these terms and use them interchangeable [5]. There are different definitions of data, information and knowledge. According to Ackoff data are symbols that represent properties of objects, events and their environment, and they are the results of observation [7]. Information is in descriptions and answers to questions that begin with the words who, what, when, how many; information is inferred from data [7]. Knowledge is something that makes possible to transform information to instructions and is obtained by extracting from experience [7]. There are alternative definitions of dat
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