TermFrame: A Systematic Approach to Karst Terminology

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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Domain Modeling in the Context of Ontology

Foundations of Computing and Decision Sciences ◽

10.1515/fcds-2015-0001 ◽

2015 ◽

Vol 40 (1) ◽

pp. 3-15 ◽

Cited By ~ 3

Author(s):

Iwona Dubielewicz ◽

Bogumiła Hnatkowska ◽

Zbigniew Huzar ◽

Lech Tuzinkiewicz

Keyword(s):

Information System ◽

Design Process ◽

Domain Knowledge ◽

Systematic Approach ◽

Domain Ontology ◽

Domain Model ◽

General Level ◽

Domain Modeling ◽

Application Domain ◽

Model Domain

Abstract The domain knowledge represented by ontology should be widely used in the design process of information system. The aim of the paper is to outline a systematic approach of developing a CIM model (domain model, precisely) on the basis of a selected domain ontology. There are presented some hints how ontology concepts can be expressed in domain model. Elaborated example realizes some difficulties in proposed approach, e.g. the domain knowledge is spread over many ontologies, some facts are defined at very general level (their interpretation is more difficult), ontology may contain many irrelevant elements. Nevertheless, we are believed that applying ontology in conscious way can help to achieve higher compliance of the domain model with the application domain.

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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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A Data-Driven Approach to Infer Knowledge Base Representation for Natural Language Relations

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/163 ◽

2017 ◽

Author(s):

Kangqi Luo ◽

Xusheng Luo ◽

Xianyang Chen ◽

Kenny Q. Zhu

Keyword(s):

Knowledge Representation ◽

Natural Language ◽

Knowledge Base ◽

Search Algorithm ◽

Knowledge Bases ◽

Data Driven ◽

Learning Approach ◽

Data Driven Approach ◽

Structured Knowledge

This paper studies the problem of discovering the structured knowledge representation of binary natural language relations.The representation, known as the schema, generalizes the traditional path of predicates to support more complex semantics.We present a search algorithm to generate schemas over a knowledge base, and propose a data-driven learning approach to discover the most suitable representations to one relation. Evaluation results show that inferred schemas are able to represent precise semantics, and can be used to enrich manually crafted knowledge bases.

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LatRec: Recognizing Goals in Latent Space (Student Abstract)

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i10.7145 ◽

2020 ◽

Vol 34 (10) ◽

pp. 13747-13748

Author(s):

Leonardo Amado ◽

Felipe Meneguzzi

Keyword(s):

Data Streams ◽

Domain Knowledge ◽

Automated Planning ◽

Domain Model ◽

Data Driven ◽

Domain Expert ◽

Latent Space ◽

Real World Applications ◽

Goal Recognition ◽

Recognition Technique

Recent approaches to goal recognition have progressively relaxed the requirements about the amount of domain knowledge and available observations, yielding accurate and efficient algorithms. These approaches, however, assume that there is a domain expert capable of building complete and correct domain knowledge to successfully recognize an agent's goal. This is too strong for most real-world applications. We overcome these limitations by combining goal recognition techniques from automated planning, and deep autoencoders to carry out unsupervised learning to generate domain theories from data streams and use the resulting domain theories to deal with incomplete and noisy observations. Moving forward, we aim to develop a new data-driven goal recognition technique that infers the domain model using the same set of observations used in recognition itself.

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ONTOLOGY VALIDATION ALGORITHM ON DATA DRIVEN APPROACH AND VOCABULARY ASPECT

Jurnal Teknologi ◽

10.11113/jt.v77.6184 ◽

2015 ◽

Vol 77 (9) ◽

Author(s):

Radziah Mohamad ◽

Nurhamizah Mohd-Hamka

Keyword(s):

Domain Knowledge ◽

Data Driven ◽

Coverage Criteria ◽

Ontology Evaluation ◽

Similarity Algorithm ◽

Data Driven Approach ◽

Evaluation Approaches

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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