Integration of the Image and NL-text Analysis/Synthesis Systems

Integration of the Image and NL-text Analysis/Synthesis Systems

Gennady K. Khakhalin (Freelancer, Russia), Sergey S. Kurbatov (Research Centre of Electronic Computing Engineering (RCECE), Russia), Xenia Naidenova (Military Medical Academy, Russia) and Alex P. Lobzin (Research Centre of Electronic Computing Engineering (RCECE), Russia)
DOI: 10.4018/978-1-4666-1806-0.ch009
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

A complex combining multimodal intelligent systems is described. The complex consists of the following systems: image analyzer, image synthesizer, linguistic analyzer of NL-text, and synthesizer of NL-text and applied ontology. The ontology describes the knowledge common for these systems. The analyzers use the applied ontology language for describing the results of their work, and this language is input for the synthesizers. The language of semantic hypergraphs has been selected for ontological knowledge representation. It is an extension of semantic networks. Plane geometry (planimetry) has been selected as an applied domain of the complex. The complex’s systems and their interaction are described.
Chapter Preview
Top

Applied Ontology

By ontology, we imply a conceptual “model of world”. Applied ontologies include concepts related both to ontology of tasks and ontology supporting knowledge representation in some practical application. Ontological engineering embraces: introducing classes of concepts and concept taxonomies, developing concept structures and situations, giving concept properties (attributes) and their ranges, working out inference procedures based on ontology and procedures of transforming concept relations into ontology.

If ontology is taken as an interface of integrated systems, then two problems arise. The first problem concerns the choice of ontological knowledge representation language such that this language would subsequently make it possible “to immerse” in it the required expansions, for example, fuzzy knowledge, cognitive procedures, etc. The second problem relates to the development of general applied ontology, capable, at the conceptual level, to integrate heterogeneous entrances for the synthesizers and outputs of the analyzers of the integral system. It is, of course, assumed that “inside” each sub-system there can be appropriate knowledge representation languages and bases of “internal” knowledge. For example, the Image Analyzer uses specific procedures or rules for extracting “non-derivative” (= primitive) objects; the Analyzer of NL-text uses specific methods for morphological, syntactical, and semantic NL-language processing.

Complete Chapter List

Search this Book:
Reset