Knowledge representation and reasoning

About: Knowledge representation and reasoning is a research topic. Over the lifetime, 20078 publications have been published within this topic receiving 446310 citations. The topic is also known as: KR & KR².

Ontologies for Knowledge Management: An Information Systems Perspective

Abstract: Knowledge management research focuses on concepts, methods, and tools supporting the management of human knowledge. The main objective of this paper is to survey basic concepts that have been used in computer science for the representation of knowledge and summarize some of their advantages and drawbacks. A secondary objective is to relate these techniques to information science theory and practice.The survey classifies the concepts used for knowledge representation into four broad ontological categories. Static ontologies describe static aspects of the world, i.e., what things exist, their attributes and relationships. A dynamic ontology, on the other hand, describes the changing aspects of the world in terms of states, state transitions and processes. Intentional ontologies encompass the world of things agents believe in, want, prove or disprove, and argue about. Finally, social ontologies cover social settings – agents, positions, roles, authority, permanent organizational structures or shifting networks of alliances and interdependencies.

Knowledge Maps

Abstract: To get fragmented information out of people's heads, onto paper, and ultimately into a computer is a continually challenging problem. We show how to use the concepts of influence diagrams to construct knowledge maps that capture the diverse information possessed by an individual or a group. We use redundant knowledge maps assessed iteratively to handle cases where the most comfortable way to assess the information does not correspond to any proper assessment order for the diagram. We use disjoint knowledge maps when the particular assessment to be made does not require a complete joint distribution. The necessary inferential calculations are readily performed in simple cases by spreadsheet programs. Knowledge maps facilitate the processes of representing knowledge and of determining its implications.

A semantics and complete algorithm for subsumption in the classic description logic

Abstract: This paper analyzes the correctness of the subsumption algorithm used in CLASSIC, a description logic-based knowledge representation system that is being used in practical applications. In order to deal efficiently with individuals in CLASSIC descriptions, the developers have had to use an algorithm that is incomplete with respect to the standard, model-theoretic semantics for description logics. We provide a variant semantics for descriptions with respect to which the current implementation is complete, and which can be independently motivated. The soundness and completeness of the polynomial-time subsumption algorithm is established using description graphs, which are an abstracted version of the implementation structures used in CLASSIC, and are of independent interest.

Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning

Abstract: Recommender systems in e-learning domain play an important role in assisting the learners to find useful and relevant learning materials that meet their learning needs. Personalized intelligent agents and recommender systems have been widely accepted as solutions towards overcoming information retrieval challenges by learners arising from information overload. Use of ontology for knowledge representation in knowledge-based recommender systems for e-learning has become an interesting research area. In knowledge-based recommendation for e-learning resources, ontology is used to represent knowledge about the learner and learning resources. Although a number of review studies have been carried out in the area of recommender systems, there are still gaps and deficiencies in the comprehensive literature review and survey in the specific area of ontology-based recommendation for e-learning. In this paper, we present a review of literature on ontology-based recommenders for e-learning. First, we analyze and classify the journal papers that were published from 2005 to 2014 in the field of ontology-based recommendation for e-learning. Secondly, we categorize the different recommendation techniques used by ontology-based e-learning recommenders. Thirdly, we categorize the knowledge representation technique, ontology type and ontology representation language used by ontology-based recommender systems, as well as types of learning resources recommended by e-learning recommenders. Lastly, we discuss the future trends of this recommendation approach in the context of e-learning. This study shows that use of ontology for knowledge representation in e-learning recommender systems can improve the quality of recommendations. It was also evident that hybridization of knowledge-based recommendation with other recommendation techniques can enhance the effectiveness of e-learning recommenders.