Knowledge representation and reasoning aims at designing computer systems that reason about a machine-interpretable representation of the world. Knowledge-based systems have a computational model of some domain of interest in which symbols serve as surrogates for real world domain artefacts, such as physical objects, events, relationships, etc. [1]. The domain of interest can cover any part of ...

A case is made for correcting a perceived imbalance between the usual ways of representing declarative and procedural knowledge. A proposed solution joins the traditional conceptual graph notation and elements of visual programming languages. The aim is to maintain the visual nature of both notations without greatly changing each one. N E W M E X I C O S T A T E U N I V E R S I T Y C O M P U T ...

The procedures of searching of solutions of problems, in Artificial Intelligence can be applied in some situations without knowledge of the Domain, and in other situations, with knowledge of it. This last procedure is usually called Heuristic Search. In such methods, for instance, the matricial and graph theory techniques reveal essential. Its introduction can give us an easy and precise way se...

Ontologies have become important tools in biomedicine, supporting critical aspects of both health care and biomedical research, including clinical research [1]. Some even see ontologies as integral to science [2]. Unlike terminologies (focusing on naming) and classification systems (developed for partitioning a domain), ontologies define the types of entities that exist, as well as their interr...

decision-tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. the resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. the most comprehensible decision trees have been designed for perfect symbolic data. classical crisp decision trees (dt) are widely applied to classification t...

In this paper a new method for knowledge extraction was presented. First, the relational knowledge representation with all crucial definitions was introduced. Second, problem statement for knowledge acquisition using relational knowledge representation was formulated. Then the modified coverage strategy was proposed. At the end a benchmark problem was considered and results were presented.

So far, within the Library and Information Science (LIS) community, Knowledge Organization (KO) has developed its own very successful solutions to document search, allowing for the classification and search of millions of books. However, current KO solutions are limited in expressivity as they only support queries by document properties, e.g., by title, author and subject. In parallel, within t...

This paper surveys some opportunities for cooperative research between linguists and computer scientists in lexical semantics. There are exciting possibilities and challenging problems. 1. I n t r o d u c t i o n I will try in this short paper to present some general thoughts on knowledge representation and word meaning for the June, 1991 SmLEX Workshop on Lexical Semantics and Knowledge Repres...

In a very basic sense, the aim of knowledge discovery is to reveal structures of knowledge which can be seen as being represented by structural relationships. In this paper, we make this notion more precise and present a method how to extract structural information from statistical data. There are two key ideas underlying our approach: First, knowledge discovery is understood as a process which...

The problem of maintaining a knowledge base is substantially concerned with keeping track of rules that share common wisdom. A knowledge representation is described in which a collection of rules that are based on common wisdom are represented as a single ‘item’. For items, maintenance hazards, caused by one item being partially hidden within another, still remain. ‘Objects’ are introduced as i...