A Building Method of XML Knowledge Base Using Domain Ontology

2011 ◽  
Author(s):  
Huayu Li ◽  
Xiaoming Zhang
Keyword(s):  
Knowledge Base ◽  
Domain Ontology

scholarly journals O ONTOLOGY-BASED PARAGRAPH EXTRACTION AND CAUSALITY DETECTION-BASED SIMILARITY FOR ANSWERING WHY-QUESTION

2018 ◽  
Vol 11 (1) ◽  
pp. 9
Author(s):  
A A I N Eka Karyawati
Keyword(s):  
Knowledge Base ◽  
Main Part ◽  
Question Answering ◽  
Document Retrieval ◽  
Domain Ontology ◽  
Typical Problem ◽  
Scoring Method ◽  
Method Performance ◽  
Question Answering System ◽  
Selection For

Paragraph extraction is a main part of an automatic question answering system, especially in answering why-question. It is because the answer of a why-question usually contained in one paragraph instead of one or two sentences. There have been some researches on paragraph extraction approaches, but there are still few studies focusing on involving the domain ontology as a knowledge base. Most of the paragraph extraction studies used keyword-based method with small portion of semantic approaches. Thus, the question answering system faces a typical problem often occuring in keyword-based method that is word mismatches problem. The main contribution of this research is a paragraph scoring method that incorporates the TFIDF-based and causality-detection-based similarity. This research is a part of the ontology-based why-question answering method, where ontology is used as a knowledge base for each steps of the method including indexing, question analyzing, document retrieval, and paragraph extraction/selection. For measuring the method performance, the evaluations were conducted by comparing the proposed method over two baselines methods that did not use causality-detection-based similarity. The proposed method shown improvements over the baseline methods regarding MRR (95%, 0.82-0.42), P@1 (105%, 0.78-0.38), P@5(91%, 0.88-0.46), Precision (95%, 0.80-0.41), and Recall (66%, 0.88-0.53).

Ontology-Driven Business Rule Specification

2013 ◽  
Vol 27 (1) ◽  
pp. 79-104 ◽  
Author(s):  
Frederik Gailly ◽  
Guido L. Geerts
Keyword(s):  
Knowledge Base ◽  
Semantic Annotation ◽  
Domain Ontology ◽  
Business Rules ◽  
Business Rule ◽  
Rich Domain ◽  
Novel Approach ◽  
Enterprise Model ◽  
New Business ◽  
Enterprise Ontology

ABSTRACT Discovering business rules is a complex task for which many approaches have been proposed including analysis, extraction from code, and data mining. In this paper, a novel approach is presented in which business rules for an enterprise model are generated based on the semantics of a domain ontology. Starting from an enterprise model for which the business rules need to be defined, the approach consists of four steps: (1) classification of the enterprise model in terms of the domain ontology (semantic annotation), (2) matching of the enterprise model constructs with ontology-based Enterprise Model Configurations (EMCs), (3) determination of Business Rule Patterns (BRPs) associated with the EMCs, and (4) use of the semantic annotations to instantiate the business rule patterns; that is, to specify the actual business rules. The success of this approach depends on two factors: (1) the existence of a semantically rich domain ontology, and (2) the strength of the knowledge base consisting of EMC-BRP associations. The focus of this paper is on defining and illustrating the new business rule discovery approach: Ontology-Driven Business Rule Specification (ODBRS). The domain of interest is enterprise systems, and an extended version of the Resource-Event-Agent Enterprise Ontology (REA-EO) is used as the domain ontology. A small set of EMC-BRP associations—i.e., an example knowledge base—is developed for illustration purposes. The new approach is demonstrated with an example.

scholarly journals Um Modelo para Auxiliar na Tomada de Decisão no Domínio de Gestão de Ideias

2015 ◽  
Vol 7 (2) ◽  
pp. 118
Author(s):  
Marina Carradore Sérgio ◽  
Alexandre Leopoldo Gonçalves ◽  
João Artur de Souza
Keyword(s):  
Decision Making ◽  
Cluster Analysis ◽  
Knowledge Base ◽  
Innovation Process ◽  
Domain Ontology ◽  
Allocation Of Resources ◽  
Essential Factor ◽  
Global Competitiveness ◽  
And Cluster Analysis ◽  
Idea Management

Taking into account the global competitiveness, innovation has become a challenge for organizations. Idea management is an integral part of the innovation process and it is presented as an essential factor for achieving success. Due to the volume and sudden peaks in submissions of ideas, the appropriate analysis and the allocation of resources for investment are important issues to be addressed. The objective of this paper is to present a model for the management of ideas based on ontology and cluster analysis in order to maximize resources for investment in ideas. So as to demonstrate the model feasibility it was prepared a dataset comprised of fifty-five ideas collected from the Starbucks® site. These ideas were then stored in the domain ontology and were used as subsidies for the cluster analysis and for the building of a knowledge base. As a result, it was identified groups with similar ideas that, when analyzed, foster a greater potential for observation and may indicate patterns and trends that can assist in decision making.

Knowledge Extraction from Domain-Specific Documents

2017 ◽  
pp. 106-123
Author(s):  
Ram Kumar ◽  
Shailesh Jaloree ◽  
R. S. Thakur
Keyword(s):  
Knowledge Representation ◽  
Knowledge Base ◽  
Knowledge Bases ◽  
Domain Ontology ◽  
Knowledge Based Systems ◽  
Domain Specific ◽  
Knowledge Based ◽  
Representation Systems ◽  
Terminological Confusion ◽  
Knowledge Representation Systems

Knowledge-based systems have become widespread in modern years. Knowledge-base developers need to be able to share and reuse knowledge bases that they build. As a result, interoperability among different knowledge-representation systems is essential. Domain ontology seeks to reduce conceptual and terminological confusion among users who need to share various kind of information. This paper shows how these structures make it possible to bridge the gap between standard objects and Knowledge-based Systems.

A Knowledge Model of Digital Evidence Review Elements Based on Ontology

2020 ◽  
pp. 281-290
Author(s):  
Ning Wang
Keyword(s):  
Knowledge Base ◽  
Domain Ontology ◽  
Digital Evidence ◽  
Knowledge Model ◽  
Ontology Construction ◽  
Model Based ◽  
Application Ontology ◽  
Evidence Review ◽  
Ontology Application ◽  
Knowledge Classification

As existing methods cannot express, share, and reuse the digital evidence review information in a unified manner, a solution of digital evidence review elements knowledge base model based on ontology is presented. Firstly, combing with the multi-source heterogeneous characteristic of digital evidence review knowledge, classification and extraction are accomplished. Secondly, according to the principles of ontology construction, the digital evidence review elements knowledge base model which includes domain ontology, application ontology, and atomic ontology is established. Finally, model can effectively acquire digital evidence review knowledge by analyzing review scenario.

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.

A Knowledge Model of Digital Evidence Review Elements Based on Ontology

2017 ◽  
Vol 9 (3) ◽  
pp. 49-57 ◽  
Author(s):  
Ning Wang
Keyword(s):  
Knowledge Base ◽  
Domain Ontology ◽  
Digital Evidence ◽  
Knowledge Model ◽  
Ontology Construction ◽  
Model Based ◽  
Application Ontology ◽  
Evidence Review ◽  
Ontology Application ◽  
Knowledge Classification

As existing methods cannot express, share, and reuse the digital evidence review information in a unified manner, a solution of digital evidence review elements knowledge base model based on ontology is presented. Firstly, combing with the multi-source heterogeneous characteristic of digital evidence review knowledge, classification and extraction are accomplished. Secondly, according to the principles of ontology construction, the digital evidence review elements knowledge base model which includes domain ontology, application ontology, and atomic ontology is established. Finally, model can effectively acquire digital evidence review knowledge by analyzing review scenario.

scholarly journals A machine learning domain ontology to populate knowledge base to support intelligent agents working in autonomous systems domains of the Internet infrastructure

2021 ◽  
Author(s):  
Julião Braga ◽  
Francisco Regateiro ◽  
Joaquim L. R. Dias ◽  
Itana Stiubiener
Keyword(s):  
Machine Learning ◽  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Intelligent Agents ◽  
Autonomous Systems ◽  
Implicit Knowledge ◽  
Domain Ontology ◽  
The Internet ◽  
Internet Infrastructure ◽  
Learning And Collaboration

This paper describes the creation of a domain ontology to represent knowledge to populate a knowledge base to be used by agents, in the environment of Internet Infrastructure routing domains. Protégé 5 was used, which produces results suitable for both software-developed agents and humans. The knowledge created with Protégé is explicit and Protégé has itself inference machines capable of producing implicit knowledge. The resources available in Protégé 5 are presented and the ontology is made available for public use.The content produced with Protégé 5 will be used to populate the knowledge base of the Structure for Knowledge Acquisition, Use, Learning and Collaboration (SKAU), an environment to support intelligent agents over Internet Autonomous Systems domains.

scholarly journals Building a Domain Ontology in the Process of Linguistic Analysis of Text Resources

2018 ◽  
Author(s):  
Nadezhda Yarushkina ◽  
Aleksey Filippov ◽  
Vadim Moshkin ◽  
Yuri Egorov
Keyword(s):  
Content Analysis ◽  
Knowledge Base ◽  
Text Analysis ◽  
Linguistic Analysis ◽  
Domain Ontology ◽  
Problem Area ◽  
Software Systems ◽  
Intelligent Software ◽  
Linguistic Methods

Ontology is a formalized representation of the problem area (PrA). Representation of the PrA in the form of an domain ontology is often used in the process of development of intelligent software systems and used as a knowledge base. The process of building an ontology is complex and requires an expert in the PrA. A large number of researchers are working to solve this problem. The basis of our approach is the use of a pipeline of different linguistic methods of text analysis. The set of rules developed by us is used to build an ontology based on the content analysis of a text resource. This article describes the method of building a domain ontology based on the linguistic analysis of content of text resources, presents an example of the proposed approach, and also presents the architecture of our pipeline.

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