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A Method of Ontology Integration for Designing Intelligent Problem Solvers

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A Method of Ontology Integration for Designing Intelligent Problem Solvers applied sciences Article A Method of Ontology Integration for Designing Intelligent Problem Solvers y y Nhon V. Do 1, , Hien D. Nguyen 2,*, and Thanh T. Mai 1 1 Faculty of Information Technology, Ho Chi Minh city Open University, Ho Chi Minh City 700000, Vietnam; [email protected] (N.V.D.); [email protected] (T.T.M.) 2 Faculty of Computer Science, University of Information Technology, VNU-HCM, Ho Chi Minh City 700000, Vietnam * Correspondence: [email protected] y Equal contribution. Received: 31 July 2019; Accepted: 3 September 2019; Published: 10 September 2019 Featured Application: In this paper, we present a method to integrate the knowledge-based systems based on ontology approach. Specially, this method can be used to design an integrated knowledge-based system for solving problems that involve the knowledge from multiple domains, such as Linear Algebra and Graph Theory. Given a specific problem that requires the knowledge from both domains, the system can reason upon the appropriate knowledge in the scope of the problem and generate a step-by-step solution which is very similar to that of humans. Therefore, this knowledge-based system can assist students in learning how to solve problems in many courses, thus meeting the requirements of an Intelligent Problem Solver in education. Abstract: Nowadays, designing knowledge-based systems which involve knowledge from different domains requires deep research of methods and techniques for knowledge integration, and ontology integration has become the foundation for many recent knowledge integration methods. To meet the requirements of real-world applications, methods of ontology integration need to be studied and developed. In this paper, an ontology model used as the knowledge kernel is presented, consisting of concepts, relationships between concepts, and inference rules. Additionally, this kernel is also added to other knowledge, such as knowledge of operators and functions, to form an integrated knowledge-based system. The mechanism of this integration method works upon the integration of the knowledge components in the ontology structure. Besides this, problems and the reasoning method to solve them on the integrated knowledge domain are also studied. Many related problems in the integrated knowledge domain and the reasoning method for solving them are also studied. Such an integrated model can represent the real-world knowledge domain about operators and functions with high accuracy and effectiveness. The ontology model can also be applied to build knowledge bases for intelligent problem solvers (IPS) in many mathematical courses in college, such as linear algebra and graph theory. These IPSs have great potential in helping students perform beer in those college courses. Keywords: knowledge integration; ontology integration; knowledge-based system; knowledge engineering; intelligent problems solver; intelligent software 1. Introduction Nowadays, the knowledge from several sources needs to be integrated in order for machines to accomplish different tasks in a more intelligent way than conventional systems [1]. Knowledge integration is important in intelligent software development [2]. In order to achieve this, knowledge Appl. Sci. 2019, 9, 3793; doi:10.3390/app9183793 www.mdpi.com/journal/applsci Appl. Sci. 2019, 9, 3793 2 of 27 has to be processed and synthesized into knowledge bases. Many intelligent systems have introduced knowledge integration to increase their power. Wolfram|Alpha is an engine for computing answers and providing knowledge [3]. The knowledge of this system is integrated from multiple knowledge domains, such as mathematics, science and technology, and society. The IMS Learning Information Services is a tool to share data about learning [4]. This service supports the exchange of information about courses and learning outcomes between users. It is a combined knowledge-integrated system of learning management platforms, student record systems, and personnel systems. Thus, knowledge integration is an imperative need for designing knowledge-based systems. Knowledge integration is the combination of multiple models for representing knowledge domains into a common model [1,5]. This integration has to meet some requirements, as follows: • Practicality: The method for knowledge integration must be able to represent the real-world knowledge domain in a knowledge base, produce an inference engine that reasons upon the knowledge base, and solve practical problems via a similar reasoning process to that of humans; • Accuracy: The components of the knowledge domain must be represented precisely and fully using the knowledge integration method, in a way that simulates human acquisition. Ontology design and ontology integration are a potential approach to solve the problems of the integration of heterogeneous knowledge [6]. They provide sophisticated knowledge about the environment for task execution [7]. They allow the users to organize information on the taxonomies of concepts, with their own aributes, to describe relationships between the concepts. When data are represented by means of ontologies, software agents can beer understand the content of the data and messages [8]. Domain-based knowledge can be modeled in ontology using ontology markup languages and various ontology tools, like Protege, OILed [9], PDDL (Planning Domain Definition Language)[10], and NDDL (New Domain Definition Language) [11]. The structure of an ontology consists of these basic components: concepts, relationships, and rules [12]. Concepts are the foundation for building the knowledge base, they make the representation clearer and more exact. Relationships on ontology perform connections between concepts. Inference rules are mechanisms for the reasoning to solve problems of the knowledge domain. The model for the integration of knowledge bases needs a knowledge kernel as ontology. Besides, solving a problem with the integrated model may require the knowledge of another pre-solved problem in the knowledge kernel. For example, in the knowledge domain of linear algebra, the knowledge of matrixes is the knowledge kernel. When solving a linear equations system, problems on the matrix, such as row transformation and column tranformation, have to be solved first [13]. Similarly, before solving a problem about vector spaces, it has to be converted to a matrix problem. Hence, the ontology-based model for integrating knowledge-based systems is a combination of the kernel ontology and other knowledge. In this paper, an ontology model used as the knowledge kernel is presented. It includes concepts, relationships between concepts, and inference rules. Some problems for this kernel have been proposed and solved. This kernel is integrated with other knowledge, such as the knowledge of operators and functions, to form an integrated knowledge-based system. The integrating method works by integrating the knowledge components in the ontology structure. The problems in the integrated knowledge domain and the reasoning method to solve them are also studied. With such an integrated model, a real-world knowledge domain about operators and functions can be represented more accurately and effectively. These models are also applied to build the knowledge bases of intelligent problem solvers (IPS) in linear algebra and graph theory courses in university. Appl. Sci. 2019, 9, 3793 3 of 27 2. Related Works There are various ontology-based methods for knowledge integration, most of which focus on basic kinds of ontology and are mainly used for information searching. They have not yet met the requirements of knowledge integration. The Semanticscience Integrated Ontology (SIO) is an ontology for facilitating biomedical knowledge discovery [14]. SIO provides an ontological foundation for the Bio2RDF linked data for the life sciences project and is used for semantic integration and discovery for SADI-based semantic web services (Semantic Automated Discovery and Integration - SADI). However, the concepts of SIO are only basic information for searching. Ontology-based knowledge integration is also used for semantic web services. Ontology WSMO is built based on the Web Service Modeling Framework (WSMO) [15]. WSMO defines four top level elements as the main concepts which have to be described in order to describe the semantic web services: ontologies, services, mediators, and goals. These methods only solve the integration of ontology as information but cannot support solving decision problems. Fuzzy ontology integration is used for the representation of uncertain knowledge on the semantic web [16,17]. The author in [16] used description logic and fuzzy set theory to represent fuzzy logic and reason on it. The study in [17] presented a method to integrate fuzzy ontology based on consensus theory. Nonetheless, those methods are just theoretical and cannot be applied in the complex knowledge domains in practice. Ontology COKB (Computational Objects Knowledge Base) is a useful ontology to represent complex knowledge domains [12]. This ontology can be used to describe many kinds of knowledge, such as knowledge about relationships, operators, and functions. It can be applied to build intelligent educational systems [18]. However, ontology COKB is too general to represent a specific knowledge domain, so it is very difficult to apply. Furthermore, the combination problems on the knowledge components in COKB have not yet been mentioned. Ontology is also a technique model for information retrieval via the processing and translation of ontological
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