Edward H. Y. Lim

A New Method for Knowledge and Information Management Domain Ontology Graph Model

A new ontology learning model called domain ontology graph (DOG) is proposed in this paper. There are two key components in the DOG, i.e., the definition of the ontology graph and the ontology learning process. The former defines the ontology and knowledge conceptualization model from the domain-specific text documents; the latter offers the necessary method of semiautomatic domain ontology learning and generates the corresponding ontology graphs. Two kinds of ontological operations are also defined based on the proposed DOG, i.e., document ontology graph generation and ontology-graph-based text classification. The simulation studies focused upon Chinese text data are used to demonstrate the potential effectiveness of our proposed strategy. This is accomplished by generating DOGs to represent the domain knowledge and conducting the text classifications based on the generated ontology graph. The experimental results show that the new method can produce significantly better classification accuracy (e.g., with 92.3% in f-measure) compared with other methods (such as 86.8% in f-measure for the term-frequency-inverse-document-frequency approach). The high performance demonstrates that our presented ontological operations based on the ontology graph knowledge model are effectively developed.

Knowledge Seeker - Ontology Modelling for Information Search and Management

Part I Introduction.- Part II KnowledgeSeeker - An Ontology Modeling and Learning Framework.- Part III KnowledgeSeeker Applications.

Collaborative content and user-based web ontology learning system

This paper presents a Collaborative Ontology Learning Approach for the implementation of an Ontology-based Web Content Management System (OWCMS). The proposal system integrates two supervised learning approach - Content-based Learning and User-based Learning Approach. The Content-based Learning Approach applies text mining methods to extract ontology concepts, and to build an Ontology Graph (OG) through the automatic learning of web documents. The User-based Learning Approach applies features analysis methods to extract the subset of the Ontology Graphs, in order to build a personalized ontology by using intelligent agent approach to capture user reading habit and preference through their semantic navigation and search over the ontology-based web content. This system combines the two methods to create collaborative ontology learning through an ontology matching and refinement process on the ontology created from content-based learning and user-based learning. The proposed method improves the validness of the classical ontology learning outcome by user-based learning refinement and validation.

Text Information Retrieval

Text information retrieval is the most important function in text based information system. They are used to develop search engines, content management systems (CMS), including some text classification and clustering features. Many technologies about text information retrieval are well developed in the past research. This chapter reviews those information retrieval technologies and some related algorithms which are useful for further development into ontology learning method.

KnowledgeSeeker — an ontological agent-based system for retrieving and analyzing Chinese web articles

In this paper, we present the KnowledgeSeeker, an ontological agent-based system that is designed to help users find, retrieve, and analyze news article from the Internet and then present the content in a semantic web. We present the benefits of using ontologies to analyze the semantics of Chinese text, and also the advantages of using a semantic web to organize information semantically. KnowledgeSeeker also demonstrates the advantages of using ontologies to identify topics. We use a Chinese document corpus to evaluate KnowledgeSeeker and the testing result was compared to other approaches. KnowledgeSeeker is able to identify the topics of Chinese web articles with an accuracy of nearly 87% and has a processing speed of less than one second per article. It is also able to organize content flexibly and understands knowledge more accurately than methods that use ontology definition.

Knowledge Discovery from Text Learning for Ontology Modeling

This paper presents a methodology of knowledge discovery from text learning for ontology modeling. Knowledge written in text is always hard to be extracted by automated process, and most existing ontologies are defined manually. Those ontologies are not comprehensive enough to express most human knowledge in the real world. Therefore, the most efficient way to identify knowledge is discovering it from rich text. In this paper, we proposed a statistical based method to measure the relation of appearing frequency of word in text. The method identifies and discovers knowledge by automated process. We also defined ontology model – ontology graph, to express knowledge, the graph facilitates machine and human processing. The extracted knowledge in the graph format can aid user to revise and define ontology knowledge more effectively and accurately.

iJADE InfoSeeker: On Using Intelligent Context-Aware Agents for Retrieving and Analyzing Chinese Web Articles

In this chapter, we presents iJADE InfoSeeker, an intelligent context-aware agents system that is designed to help users find, retrieve, and analyze news article from the Internet and then present the content in a semantic web. We present the advantages of using multiple intelligent agents to mine news articles on the web, the benefits of using ontologies to analyze the semantics of Chinese text, and also the advantages of using a semantic web to organize information semantically. iJADE InfoSeeker also demonstrates the advantages of using ontologies to identify topics. We use a Chinese document corpus to evaluate iJADE InfoSeeker and the testing result was compared to other approaches. It was found that the accuracy of identifying the topics of Chinese web articles is nearly 87%. It demonstrated a fast processing speed of less than one second per article. It also organizes content flexibly and understands knowledge accurately, unlike traditional text classification systems.

Computational Knowledge and Ontology

The definition of knowledge is an augmentative topic in philosophy. We do not try to find out an explicit meaning of philosophical knowledge, but the most important thing is that we should know about what is knowledge in computer system, as called computational knowledge. In this chapter, we introduce some researches and definitions related to knowledge and computational knowledge. Ontology is a word used in both philosophy and computer system to describe the formalization of knowledge. We shall look into the definition of ontology in brief and also introduce its formalization methods in computer system.

Ontology Graph Based Approach for Automatic Chinese Text Classification

Automatic classification of Chinese text documents requires a machine to process and analyze the meaning of Chinese terms. We propose an Ontology Graph based approach to measure the relations between Chinese terms for the text classification purpose. The method improves traditional high dimensional termbased text classification approach, in that the new method selects very small number of semantically related concepts to create Ontology Graphs. The Ontology Graphs can be used to represent different classes (domains). It enhances text classification performance by using its small-size but high semantically associated concepts. Our experiments show that the proposed method has classified a Chinese document set with 92% accuracy in f-measure by using Ontology Graphs containing only 80 concepts for each class. The high accuracy result shows that the Ontology Graphs used in the process are enable to represent the knowledge of a domain and also the Ontology Graph based approach of text classification is effective and accurate.

Ontology Modeling Framework

We have defined a knowledge representation model in Knowledge- Seeker called Ontology Graph, which is used to represent domain ontology and it can support ontological information search and management. The proposed Ontology Graph is a graphical based knowledge generated by semantic relations of Chinese words, and that semantic relations are formed by the ontology learning process automatically. This chapter first overviews the KnowledgeSeeker system and then presents the background idea and the implementation details of the proposed Ontology Graph.