Digital identity management domain for ontological semantics: Domain acquistion methodology and practice

Evguenia A Malaia, Purdue University

Abstract

This work focuses on ontological efforts to support information security applications---more specifically, engineering natural language processing technology---in the domain of Digital Identity Management (DIM). The present paper deals with the methodology and practice in domain acquisition for two of the static knowledge sources, the ontology and the lexicon, including: (1) Delimitation of the expanding digital identity management textual corpus with volatile vocabulary; (2) Extraction of lexical items pertaining to the domain; (3) Building ontological support for lexical items; introduction of necessary attributes and relations. I propose a domain-specific topic-source variability matrix, which can be used as an external validity source for ontological description of a "storming" domain. I have also divided sources into non-profits, academic research, industry groups or companies, US government agencies and international organizations. For the corpus, I have taken texts from each topic-source combination. Based on the corpus, I have made the decision to use a two-pronged approach to lexical and ontological domain acquisition: concept-based initial acquisition (including adding new properties) followed by corpus-based acquisition. The described process enables the acquirers to ensure external validity and internal consistency of the ontology and the lexicon, and aids in faster saturation of the lexicon of a particular domain. While the topic-source subdivision is necessarily domain-specific, the two-prong methodology is applicable to ontological and lexical acquisition for any domain. The rest of the work is devoted to the scripts of lexical and ontological items acquired for the domain, and to the elaboration on the choices and decisions in lexical and ontological acquisition.

Degree

Ph.D.

Advisors

Raskin, Purdue University.

Subject Area

Linguistics

COinS