Ontology construction for explicit description of domain knowledge

2015 ◽  
Author(s):  
R. Mervin ◽  
S. Murugesh ◽  
A. Jaya
Keyword(s):  
Domain Knowledge ◽  
Explicit Description ◽  
Ontology Construction

AUTOMATIC ONTOLOGY CONSTRUCTION IN FICTION-BASED DOMAIN

2011 ◽  
Vol 21 (08) ◽  
pp. 1147-1167 ◽  
Author(s):  
HUI-NGO GOH ◽  
CHING-CHIEH KIU ◽  
LAY-KI SOON ◽  
BALI RANAIVO-MALANÇON
Keyword(s):  
Domain Knowledge ◽  
Threshold Value ◽  
New Techniques ◽  
Ontology Construction ◽  
Non Fiction ◽  
Algorithmic Framework

The field of ontology has received attention lately due to the increasing needs in conceptualizing the domain knowledge for resolving various jobs' demand. Numerous new techniques, tools and applications have then been developed for their suitability in managing knowledge. However, most works carried out focused on non-fiction domain and categorizing the concepts into component or cluster. Hence, the originality of the content flow is not preserved. This paper presents an automated ontology construction in fiction domain. The significance of the study lies in (1) designing a simple and easy algorithmic framework for automated ontology construction while preserving the originality of the content flow in an ontology, (2) identification of suitable threshold value in extracting true terms, and (3) process an unstructured fiction-based domain text into meaningful structure automatically.

Human-centric Faceted Approach for Ontology Construction

2020 ◽  
Vol 47 (1) ◽  
pp. 31-44
Author(s):  
Shiv Shakti Ghosh ◽  
Subhashis Das ◽  
Sunil Kumar Chatterjee
Keyword(s):  
Domain Knowledge ◽  
Information Science ◽  
Library And Information Science ◽  
Construction Process ◽  
Information Need ◽  
Ontology Construction ◽  
System P ◽  
Facet Analysis ◽  
Tourism Information ◽  
Selective Dissemination Of Information

In this paper, we propose an ontology building method, called human-centric faceted approach for ontology construction (HCFOC). HCFOC uses the human-centric approach, improvised with the idea of selective dissemination of information (SDI), to deal with context. Further, this ontology construction process makes use of facet analysis and an analytico-synthetic classification approach. This novel fusion contributes to the originality of HCFOC and distinguishes it from other existing ontology construction methodologies. Based on HCFOC, an ontology of the tourism domain has been designed using the Protégé-5.5.0 ontology editor. The HCFOC methodology has provided the necessary flexibility, extensibility, robustness and has facilitated the capturing of background knowledge. It models the tourism ontology in such a way that it is able to deal with the context of a tourist’s information need with precision. This is evident from the result that more than 90% of the user’s queries were successfully met. The use of domain knowledge and techniques from both library and information science and computer science has helped in the realization of the desired purpose of this ontology construction process. It is envisaged that HCFOC will have implications for ontology developers. The demonstrated tourism ontology can support any tourism information retrieval system.

The Reflexive Practitioner

2012 ◽  
pp. 135-148
Author(s):  
Stephen Dobson
Keyword(s):  
Action Research ◽  
Collaborative Research ◽  
Domain Knowledge ◽  
Critical Reflection ◽  
Formal Ontology ◽  
Research Setting ◽  
Ontology Construction ◽  
Planning Strategies

This chapter aims to set out relevant discourse and approaches to consider when planning strategies for acquiring and building knowledge for formal ontology construction. Action Research (AR) is offered as a key means to help structure the necessary reflexivity required to enrich the researcher’s understanding of how they know what they know, particularly within a collaborative research setting. This is especially necessary when revealing tacit domain knowledge through participation with actors and stakeholders: “In this kind of research it is permissible to be openly normative and to strive for change, but not to neglect critical reflection” (Elfors & Svane 2008, 1).

scholarly journals Automatic Ontology Construction Using Text Corpora and Ontology Design Patterns (ODPs) in Alzheimer’s Disease

2017 ◽  
Vol 10 (2) ◽  
pp. 59 ◽  
Author(s):  
Denis Eka Cahyani ◽  
Ito Wasito
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Design Patterns ◽  
Domain Knowledge ◽  
Expert Knowledge ◽  
Relation Extraction ◽  
Ontology Construction ◽  
Text Corpora ◽  
Ontology Design ◽  
Fully Automatic

An ontology is defined as an explicit specification of a conceptualization, which is an important tool for modeling, sharing and reuse of domain knowledge. However, ontology construction by hand is a complex and a time consuming task. This research presents a fully automatic method to build bilingual domain ontology from text corpora and ontology design patterns (ODPs) in Alzheimer’s disease. This method combines two approaches: ontology learning from texts and matching with ODPs. It consists of six steps: (i) Term & relation extraction (ii) Matching with Alzheimer glossary (iii) Matching with ontology design patterns (iv) Score computation similarity term & relation with ODPs (v) Ontology building (vi) Ontology evaluation. The result of ontology composed of 381 terms and 184 relations with 200 new terms and 42 new relations were added. Fully automatic ontology construction has higher complexity, shorter time and reduces role of the expert knowledge to evaluate ontology than manual ontology construction. This proposed method is sufficiently flexible to be applied to other domains.

Automatic Ontology Construction using Conceptualization and Semantic Roles

2017 ◽  
Vol 7 (3) ◽  
pp. 62-80
Author(s):  
Amita Arora ◽  
Manjeet Singh ◽  
Naresh Chauhan
Keyword(s):  
Domain Knowledge ◽  
Data Sources ◽  
Semantic Roles ◽  
Ontology Construction ◽  
New Approach ◽  
Meaningful Information

Ontologies are constructed to extract meaningful information from data sources. Constructing ontologies aim at capturing domain knowledge that gives a commonly agreed understanding of a domain, which may be reused, shared, among applications and groups. To ease the process of building ontologies automatically, this manuscript per the authors proposes a new approach which extracts semantic roles of nouns in the sentential structure along with usual concepts and their relationships. The extracted information about different roles, concepts and relationships among the concepts from different documents are then merged to construct an ontology for whole document. The proposed approach is implemented and the performance of the proposed technique is evaluated. Experiments show the ontology thus created captures most of the information given in the document.

Automated Assessment of Domain Knowledge With Online Knowledge Mapping

2006 ◽  
Author(s):  
Gregory K. W. K. Chung ◽  
Eva L. Baker ◽  
David G. Brill ◽  
Ravi Sinha ◽  
Farzad Saadat ◽  
...  
Keyword(s):  
Domain Knowledge ◽  
Knowledge Mapping ◽  
Automated Assessment

A Model for Structured Data Entry Based on Explicit Descriptional Knowledge

1994 ◽  
Vol 33 (05) ◽  
pp. 454-463 ◽  
Author(s):  
A. M. van Ginneken ◽  
J. van der Lei ◽  
J. H. van Bemmel ◽  
P. W. Moorman
Keyword(s):  
Domain Knowledge ◽  
Data Entry ◽  
Knowledge Bases ◽  
Structured Data ◽  
Research Quality ◽  
Free Text ◽  
Specific Knowledge ◽  
User Input ◽  
The One ◽  
Research Quality Assessment

Abstract:Clinical narratives in patient records are usually recorded in free text, limiting the use of this information for research, quality assessment, and decision support. This study focuses on the capture of clinical narratives in a structured format by supporting physicians with structured data entry (SDE). We analyzed and made explicit which requirements SDE should meet to be acceptable for the physician on the one hand, and generate unambiguous patient data on the other. Starting from these requirements, we found that in order to support SDE, the knowledge on which it is based needs to be made explicit: we refer to this knowledge as descriptional knowledge. We articulate the nature of this knowledge, and propose a model in which it can be formally represented. The model allows the construction of specific knowledge bases, each representing the knowledge needed to support SDE within a circumscribed domain. Data entry is made possible through a general entry program, of which the behavior is determined by a combination of user input and the content of the applicable domain knowledge base. We clarify how descriptional knowledge is represented, modeled, and used for data entry to achieve SDE, which meets the proposed requirements.

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