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.

LaSSIE: a knowledge-based software information system

Abstract: The authors discuss the important problem of invisibility that is inherent in the task of developing large software systems It is pointed out that there are no direct solutions to this problem; however, there are several categories of systems-relational code analyzers, reuse librarians, and project management databases-that can be seen as addressing aspects of the invisibility problem It is argued that these systems do not adequately deal with certain important aspects of the problem of invisibility-semantic proliferation, multiple views, and the need for intelligent indexing A system called LaSSIE, which uses knowledge representation and reasoning technology to address each of these three issues directly and thereby help with the invisibility problem, has been built The authors conclude with an evaluation of the system and a discussion of open problems and ongoing work >

Ontology languages for the Semantic Web

Abstract: Ontologies have proven to be an essential element in many applications. They are used in agent systems, knowledge management systems, and e-commerce platforms. They can also generate natural language, integrate intelligent information, provide semantic-based access to the Internet, and extract information from texts in addition to being used in many other applications to explicitly declare the knowledge embedded in them. However, not only are ontologies useful for applications in which knowledge plays a key role, but they can also trigger a major change in current Web contents. This change is leading to the third generation of the Web-known as the Semantic Web-which has been defined as the conceptual structuring of the Web in an explicit machine-readable way. New ontology-based applications and knowledge architectures are developing for this new Web. A common claim for all of these approaches is the need for languages to represent the semantic information that this Web requires-solving heterogeneous data exchange in this heterogeneous environment. Our goal is to help developers find the most suitable language for their representation needs.

Automatic fuzzy ontology generation for semantic Web

Abstract: Ontology is an effective conceptualism commonly used for the semantic Web. Fuzzy logic can be incorporated to ontology to represent uncertainty information. Typically, fuzzy ontology is generated from a predefined concept hierarchy. However, to construct a concept hierarchy for a certain domain can be a difficult and tedious task. To tackle this problem, this paper proposes the FOGA (fuzzy ontology generation framework) for automatic generation of fuzzy ontology on uncertainty information. The FOGA framework comprises the following components: fuzzy formal concept analysis, concept hierarchy generation, and fuzzy ontology generation. We also discuss approximating reasoning for incremental enrichment of the ontology with new upcoming data. Finally, a fuzzy-based technique for integrating other attributes of database to the ontology is proposed

KnowRob: A knowledge processing infrastructure for cognition-enabled robots

Abstract: Autonomous service robots will have to understand vaguely described tasks, such as “set the table” or “clean up”. Performing such tasks as intended requires robots to fully, precisely, and appropriately parameterize their low-level control programs. We propose knowledge processing as a computational resource for enabling robots to bridge the gap between vague task descriptions and the detailed information needed to actually perform those tasks in the intended way. In this article, we introduce the KnowRob knowledge processing system that is specifically designed to provide autonomous robots with the knowledge needed for performing everyday manipulation tasks. The system allows the realization of “virtual knowledge bases”: collections of knowledge pieces that are not explicitly represented but computed on demand from the robot's internal data structures, its perception system, or external sources of information. This article gives an overview of the different kinds of knowledge, the different inference mechanisms, and interfaces for acquiring knowledge from external sources, such as the robot's perception system, observations of human activities, Web sites on the Internet, as well as Web-based knowledge bases for information exchange between robots. We evaluate the system's scalability and present different integrated experiments that show its versatility and comprehensiveness.

Ontology reasoning in the SHOQ(D) description logic

Abstract: Ontologies are set to play a key role in the "Semantic Web" by providing a source of shared and precisely defined terms that can be used in descriptions of web resources. Reasoning over such descriptions will be essential if web resources are to be more accessible to automated processes. SHOQ(D) is an expressive description logic equipped with named individuals and concrete datatypes which has almost exactly the same expressive power as the latest web ontology languages (e.g., OIL and DAML). We present sound and complete reasoning services for this logic.