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.

What's Important About Knowledge Representation?

Abstract: The author discusses a number of issues that serve as research goals for discovering the principles of knowledge representation, using techniques and concepts evolved while developing the knowledge-representation system KL-one as illustrations. The focus is on what constitutes a good representational system and a good set of representational primitives for dealing with an open-ended range of knowledge domains. Issues of interest include those problems that arise in attempting to construct intelligent computer programs that use knowledge to perform some task. 7 references.

A unified semantic knowledge base for IoT

Abstract: In the Internet of Things (IoT), interoperability among heterogeneous entities is an important issue. Semantic modeling is a key catalyst to support interoperability. In this work, we present a unified semantic knowledge base for IoT that uses ontologies as the building blocks. Most of the current ontologies for IoT mainly focus on resources, services and location information. We build upon the current state-of-the-art ontologies to provide contextual information and set of policies to execute services. Our knowledge base consists of several ontologies viz, resource, location, context & domain, policy and service ontologies. This helps in building a unified knowledge representation for IoT entities. In our knowledge base, we specifically model dynamic environments in which IoT entities operate. Our knowledge base also facilitates service-composition, discovery and modeling for IoT in dynamic environments.

Determining Type, Part, Color, and Time Relationships

Abstract: The development of a simple question-answering system is considered. In particular, methods are described that are designed to supplement a deductive question-answering algorithm that is now operational. The algorithm draws on a base of logical propositions organized as a semantic net. The net permits selective access to the contents of individual mental worlds and narratives, to sets of entities of any specified type, and to propositions involving any specified entity and classified under any specified topic. The problems involved in determining type, part-of, color, and time relationships are discussed. It is shown that much combinatory reasoning in a question-answering system can be short-circuited by the use of special graphical and geometrical methods. 13 references.

Hybrid knowledge bases

Abstract: Deductive databases that interact with, and are accessed by, reasoning agents in the real world (such as logic controllers in automated manufacturing, weapons guidance systems, aircraft landing systems, land-vehicle maneuvering systems, and air-traffic control systems) must have the ability to deal with multiple modes of reasoning. Specifically, the types of reasoning we are concerned with include, among others, reasoning about time, reasoning about quantitative relationships that may be expressed in the form of differential equations or optimization problems, and reasoning about numeric modes of uncertainty about the domain which the database seeks to describe. Such databases may need to handle diverse forms of data structures, and frequently they may require use of the assumption-based nonmonotonic representation of knowledge. A hybrid knowledge base is a theoretical framework capturing all the above modes of reasoning. The theory tightly unifies the constraint logic programming scheme of Jaffar and Lassez (1987), the generalized annotated logic programming theory of Kifer and Subrahmanian (1989), and the stable model semantics of Gelfond and Lifschitz (1988). New techniques are introduced which extend both the work on annotated logic programming and the stable model semantics.