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.

Knowledge representation with ontologies: the present and future

Abstract: Recently, we have seen an explosion of interest in ontologies as artifacts to represent human knowledge and as critical components in knowledge management, the semantic Web, business-to-business applications, and several other application areas. Various research communities commonly assume that ontologies are the appropriate modeling structure for representing knowledge. However, little discussion has occurred regarding the actual range of knowledge an ontology can successfully represent.

A network-based knowledge representation and its natural deduction system

Abstract: We describe a knowledge representation scheme called KNET and a problem solving system called SNIFFER designed to answer queries using a K-NET knowledge base. K-NET uses a partitioned semantic net to combine the expressive capabilities of the first-order predicate calculus with linkage to procedural knowledge and with full indexing of objects to the relationships in which they participate. Facilities are also included for representing taxonomies of sets and for maintaining hierarchies of contexts. SNIFFER is a manager and coordinator of deductive and problem-solving processes. The basic system includes a logically complete set of natural deduction facilities that do not require statements to be converted into clause or prenex normal form. Using SNIFFER's coroutine-based control structure, alternative proofs may be constructed in pseudo-parallel and results shared among them. In addition, SNIFFER can also manage the application of specialist procedures that have specific knowledge about a particular domain or about the topology of the K-NET structures, for example, specialist procedures are used to manipulate taxonomic information and to link the system to information in external data bases.

Applications of Answer Set Programming

Abstract: ASP has been applied fruitfully to a wide range of areas in AI and in other fields, both in academia and in industry, thanks to the expressive representation languages of ASP and the continuous improvement of ASP solvers We present some of these ASP applications, in particular, in knowledge representation and reasoning, robotics, bioinformatics and computational biology as well as some industrial applications We discuss the challenges addressed by ASP in these applications and emphasize the strengths of ASP as a useful AI paradigm

Knowledge representation and the semantics of natural language

Abstract: Knowledge Representation with MultiNet.- Historical Roots.- Basic Concepts.- Semantic Characterization of Objects.- Semantic Characterization of Situations.- The Comparison of Entities.- The Spatio-temporal Characterization of Entities.- Modality and Negation.- Quantification and Pluralities.- The Role of Layer Information in Semantic Representations.- Relations Between Situations.- Lexicon and Knowledge Representation.- Question Answering and Inferences.- Software Tools for the Knowledge Engineer and Sample Applications.- Comparison Between MultiNet and Other Semantic Formalisms or Knowledge Representation Paradigms.- The Representational Means of MultiNet.- Overview and Representational Principles.- Means for Expressing Classification and Stratification.- Relational and Functional Means of Representation.