Using Domain Ontology as Domain Knowledge for Requirements Elicitation

2006 ◽  
Author(s):  
H. Kaiya ◽  
M. Saeki
Keyword(s):  
Domain Knowledge ◽  
Domain Ontology ◽  
Requirements Elicitation

Using Ontology as Scaffolding for Authoring Teaching Materials

2007 ◽  
pp. 203-221
Author(s):  
Jin-Tan Yang ◽  
Pao Ta Yu ◽  
Nian Shing Chen ◽  
Chun Yen Tsai ◽  
Chi-Chin Lee ◽  
...  
Keyword(s):  
Resource Description Framework ◽  
Domain Knowledge ◽  
Learning Objects ◽  
Domain Ontology ◽  
School Level ◽  
Future Research ◽  
Teaching Materials ◽  
Rich Description ◽  
Description Framework ◽  
Resource Description

The purpose of this study is to conduct teachers to author a teaching material by using visualized domain ontology as scaffolding. Based on a content repository management system (CRMS), mathematics ontology to support teachers for authoring teaching materials is developed. Although the domain ontology of mathematics at secondary school level in Taiwan provides structured vocabularies for describing domain content, those teachers who want to create a knowledge-rich description of domain knowledge, such as required by the “Semantic Web,” use ontology that turns out to provide only part of knowledge required. In this chapter, we examine problems related to capturing the learning resources or learning objects (LOs) on a CRMS. To construct ontology for a subset of mathematics course descriptions, the representation requirements by resource description framework/resource description framework schema (RDF/RDFS) was implemented. Furthermore, a visualized online authoring tool (VOAT) is designed for authoring teaching materials on the Web. Finally, discussion and future research are addressed.

Enhancing Domain Knowledge for Requirements Elicitation with Web Mining

2010 ◽  
Author(s):  
Haruhiko Kaiya ◽  
Yuutarou Shimizu ◽  
Hirotaka Yasui ◽  
Kenji Kaijiri ◽  
Motoshi Saeki
Keyword(s):  
Domain Knowledge ◽  
Web Mining ◽  
Requirements Elicitation

Capturing Evolutional Knowledge Using Time Interval Tracing

2007 ◽  
Vol 11 (4) ◽  
pp. 373-380
Author(s):  
Shun-Chieh Lin ◽  
◽  
Chia-Wen Teng ◽  
Shian-Shyong Tseng ◽  
Keyword(s):  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Time Domain ◽  
Domain Knowledge ◽  
Domain Ontology ◽  
Time Interval ◽  
Knowledge Based System ◽  
Knowledge Based ◽  
Over Time

Knowledge acquisition is a critical bottleneck in building a knowledge-based system. Much research and many tools have been developed to acquire domain knowledge with embedded rules that may be ignored in constructing the initial prototype. Due to different backgrounds and dynamic knowledge changing over time, domain knowledge constructed at one time may be degraded at any time thereafter. Here, we propose knowledge acquisition, called enhanced embedded meaning capturing under uncertainty deciding (enhanced EMCUD), which constructs a domain ontology and traces information over time to efficiently update time-related domain knowledge based on the current environment. We enrich the knowledge base and ease the construction of domain knowledge that changes with times and the environment.

Research of Intelligent Search Engine Based on Multi-Ontology

2012 ◽  
Vol 241-244 ◽  
pp. 1659-1663
Author(s):  
Shu Dong Zhang ◽  
Can Zhang ◽  
Jing Wang
Keyword(s):  
Semantic Web ◽  
Search Engine ◽  
Domain Knowledge ◽  
Recall Rate ◽  
Domain Ontology ◽  
Semantic Retrieval ◽  
Engine Model ◽  
Cross Domain ◽  
User Query ◽  
Primary Means

With the development of the Semantic Web, ontology has become the primary means of expression of many fields of knowledge. Introducing the Semantic Web technology into the field of search engine is a valuable research topic. In order to meet the complex semantic retrieval demands, the paper proposes a search engine model based on multi-domain ontology, the model using ontology mapping rewrite the user query to achieve multiple ontology query, and provide a richer and accurate semantic information for the retrieval of cross-domain knowledge; And the paper proposes a method of cross-domain ontology annotation, providing a basis for the user semantic retrieval. The experimental results show that the search results improve the precision and recall rate.

scholarly journals Development of a Framework to Understand Tables in Engineering Specification Documents

2020 ◽  
Vol 10 (18) ◽  
pp. 6182
Author(s):  
Valentin Agossou ◽  
Hyo-Won Suh ◽  
Heejung Lee ◽  
Jae Hyun Lee
Keyword(s):  
Neural Network ◽  
Domain Knowledge ◽  
Domain Ontology ◽  
Knowledge Map ◽  
Specific Domain ◽  
Tree Algorithm ◽  
New Concepts ◽  
Hidden Knowledge ◽  
Neural Network Algorithm ◽  
Product Specification

Several works have been done in the last decades for understanding tables in documents, but most of them were not specifically designed to understand tables in engineering specification documents. Tables in engineering specifications have characteristics such as various table structures with restricted terms. A framework is developed to address the issues in understanding tables in engineering specification documents. The framework consists of three steps: (1) Identifying minimal tables, (2) classifying cells, and (3) extending a domain knowledge map. A modified XY-tree algorithm was developed to find minimal tables, and a neural network algorithm was adopted to classify cells into labels and data. Then, specific domain rules were developed to discover concepts and relationships from terms in the classified cells. It is assumed a domain ontology is given, and it is extended with new concepts and relationships extracted from tables. We illustrated how each step performed with engineering table examples. The proposed framework could be used for searching product specification and for discovering hidden knowledge from tables in engineering specification documents.

Knowledge Acquisition of Domain Ontology Based on the Documents

2013 ◽  
Vol 333-335 ◽  
pp. 2243-2247
Author(s):  
Zhao Qin Hu
Keyword(s):  
Knowledge Acquisition ◽  
Domain Knowledge ◽  
Domain Ontology ◽  
The Core ◽  
Core Concepts ◽  
Core Ontology ◽  
Text Preprocessing

In this paper we put forward the framework of domain knowledge acquisition based on the documents with the ontology theory and knowledge acquisition theory. We present four aspects of the framework for knowledge acquisition: establishing domain core ontology, text preprocessing, domain ontology knowledge acquisition and improving domain ontology. A core ontology is the core concepts and relationships of the domain area. Text preprocessing mainly analyzes and processes Chinese webpage to generate sets of words. Domain ontology knowledge acquisition parses the core ontology and completes concept matching and ontology editing. Improving domain ontology is to infer and evaluate core ontology expanded and make the ontology more scientific and reasonable. The proposed framework can be applied as a useful benchmark or guidance in domain knowledge acquisition.

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