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 and languages for representing mathematical knowledge on the Semantic Web

Abstract: Mathematics is a ubiquitous foundation of science, technology, and engineering. Specific areas of mathematics, such as numeric and symbolic computation or logics, enjoy considerable software support. Working mathematicians have recently started to adopt Web 2.0 environments, such as blogs and wikis, but these systems lack machine support for knowledge organization and reuse, and they are disconnected from tools such as computer algebra systems or interactive proof assistants. We argue that such scenarios will benefit from Semantic Web technology.Conversely, mathematics is still underrepresented on the Web of [Linked] Data. There are mathematics-related Linked Data, for example statistical government data or scientific publication databases, but their mathematical semantics has not yet been modeled. We argue that the services for the Web of Data will benefit from a deeper representation of mathematical knowledge.Mathematical knowledge comprises structures given in a logical language --formulae, statements e.g. axioms, and theo-ries --, a mixture of rigorous natural language and symbolic notation in documents, application-specific metadata, and discussions about conceptualizations, formalizations, proofs, and counter-examples. Our review of vocabularies for representing these structures covers ontologies for mathematical problems, proofs, interlinked scientific publications, scientific discourse, as well as mathematical metadata vocabularies and domain knowledge from pure and applied mathematics.Many fields of mathematics have not yet been implemented as proper Semantic Web ontologies; however, we show that MathML and OpenMath, the standard XML-based exchange languages for mathematical knowledge, can be fully integrated with RDF representations in order to contribute existing mathematical knowledge to the Web of Data.We conclude with a roadmap for getting the mathematical Web of Data started: what datasets to publish, how to interlink them, and how to take advantage of these new connections.

A new method to determine basic probability assignment from training data

Abstract: The Dempster-Shafer evidence theory (D-S theory) is one of the primary tools for knowledge representation and uncertain reasoning, and has been widely used in many information fusion systems. However, how to determine the basic probability assignment (BPA), which is the main and first step in D-S theory, is still an open issue. In this paper, based on the normal distribution, a method to obtain BPA is proposed. The training data are used to build a normal distribution-based model for each attribute of the data. Then, a nested structure BPA function can be constructed, using the relationship between the test data and the normal distribution model. A normality test and normality transformation are integrated into the proposed method to handle non-normal data. The missing attribute values in datasets are addressed as ignorance in the framework of the evidence theory. Several benchmark pattern classification problems are used to demonstrate the proposed method and to compare against existing methods. Experiments provide encouraging results in terms of classification accuracy, and the proposed method is seen to perform well without a large amount of training data.

Representation and presentation of requirements knowledge

Abstract: The approach to representation and presentation of knowledge used in ARIES, an environment to experiment with support for analysts in modeling target domains and in entering and formalizing system requirements, is described. To effectively do this, ARIES must manage a variety of notations so that analysts can enter information in a natural manner, and ARIES can present it back in different notations and from different viewpoints. To provide this functionality, a single, highly expressive internal representation is used for all information in the system. The system architecture separates representation and presentation, in order to localize consistency and propagation issues. The presentation architecture is tailored to be flexible enough so that new notations can be easily introduced on top of the underlying representation. Presentation knowledge is coupled to specification evolution knowledge thereby leveraging common representations for both in order to provide automated focusing support to users who need informative guidance in creating and modifying specifications. >

What is knowledge representation

Abstract: In this chapter, we overview eight major approaches to knowledge representation: logical representations, semantic networks, procedural representations, logic programming formalisms, frame-based representations, production system architectures, and knowledge representation languages. The fundamentals of each approach are described, and then elaborated upon through illustrative examples chosen from actual systems which employ the approach. Where appropriate, comparisons among the various schemes are drawn. The chapter concludes with a set of general principles which have grown out of the different approaches.