ONTOLOGY VALIDATION ALGORITHM ON DATA DRIVEN APPROACH AND VOCABULARY ASPECT

Ontology evaluation is required before using the ontology within applications. Similar with software practice, the purpose of ontology evaluation is to identify the achievement of requirement criteria. Users who require coverage criteria often seeking ontology that contain the terms related to their focused domain knowledge. Users encounter the difficulty to select a suitable ontology from variety of ontology evaluation approaches. Conceptualization of information related to ontology evaluation helps to identify the important component within ontology that helps towards coverage criteria achievement. This work proposes an algorithm to extract ontology documents gained from public ontology repositories like Falcons into its vocabulary parts focused on classes and literals. The algorithm then processes the extracted ontology components with similarity algorithm and later displays the result on the coverage match of ontology with provided terms and the terms that are synonym expanded using WordNet.

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TermFrame: A Systematic Approach to Karst Terminology

Dela ◽

10.4312/dela.54.149-167 ◽

2021 ◽

pp. 149-167

Author(s):

Špela Vintar ◽

Uroš Stepišnik

Keyword(s):

Knowledge Representation ◽

Domain Knowledge ◽

Systematic Approach ◽

Interdisciplinary Approach ◽

Domain Model ◽

Knowledge Network ◽

Data Driven ◽

Knowledge Representations ◽

Visual Knowledge ◽

Data Driven Approach

We describe a systematic and data-driven approach to karst terminology where knowledge from different textual sources is structured into a comprehensive multilingual knowledge representation. The approach is based on a domain model which is constructed in line with the frame-based approach to terminology and the analytical geomorphological method of describing karst phenomena. The domain model serves as a basis for annotating definitions and aggregating the information obtained from different definitions into a knowledge network. We provide examples of visual knowledge representations and demonstrate the advantages of a systematic and interdisciplinary approach to domain knowledge.

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Toward data-driven solutions to interactive dynamic influence diagrams

Knowledge and Information Systems ◽

10.1007/s10115-021-01600-5 ◽

2021 ◽

Author(s):

Yinghui Pan ◽

Jing Tang ◽

Biyang Ma ◽

Yifeng Zeng ◽

Zhong Ming

Keyword(s):

Decision Making ◽

Domain Knowledge ◽

Strong Support ◽

Study Data ◽

Data Driven ◽

Influence Diagrams ◽

Sequential Decision ◽

Data Driven Decision Making ◽

Data Driven Approach ◽

Interactive Dynamic

AbstractWith the availability of significant amount of data, data-driven decision making becomes an alternative way for solving complex multiagent decision problems. Instead of using domain knowledge to explicitly build decision models, the data-driven approach learns decisions (probably optimal ones) from available data. This removes the knowledge bottleneck in the traditional knowledge-driven decision making, which requires a strong support from domain experts. In this paper, we study data-driven decision making in the context of interactive dynamic influence diagrams (I-DIDs)—a general framework for multiagent sequential decision making under uncertainty. We propose a data-driven framework to solve the I-DIDs model and focus on learning the behavior of other agents in problem domains. The challenge is on learning a complete policy tree that will be embedded in the I-DIDs models due to limited data. We propose two new methods to develop complete policy trees for the other agents in the I-DIDs. The first method uses a simple clustering process, while the second one employs sophisticated statistical checks. We analyze the proposed algorithms in a theoretical way and experiment them over two problem domains.

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