The knowledge increase estimation framework for ontology integration on the concept level

Kozierkiewicz, Adrianna; Pietranik, Marcin

doi:10.3233/jifs-169116

Cited by 14 publications

(12 citation statements)

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“…• H is a concept hierarchy (this element is not applied in this paper, therefore we do not provide its formal definition; for details please refer to [11]), • is a finite set of relations between concepts,…”

Section: Basic Notionsmentioning

confidence: 99%

“…Our framework for ontology integration, presented in previous work [11], contains a measure for estimation of effectiveness of an integration. This measure can be calculated before integration is carried out.…”

Section: Introductionmentioning

confidence: 99%

“…The developed ontology framework [11] assumes the existence of the function SA mentioned above, which provides semantics for every attribute in terms of a firstorder logic formula built from atomic descriptions of attributes, e.g., SA(birthdate, Person) = year_of_birth  month_of_birth  day_of_birth. So far, we have assumed that attributes' semantics are provided directly by experts or ontology developers.…”

Section: Introductionmentioning

confidence: 99%

“…In our previous work [11] the problem of estimation of the knowledge increase which may occur during the ontology integration on the concept level was considered. We claim that it is possible to measure the potential knowledge increase before the integration starts.…”

mentioning

confidence: 99%

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Semi-Automatic Definition of Attribute Semantics for the Purpose of Ontology Integration

2020

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By ontology, we understand a knowledge structure which well reflects the complexity of a real world. Ontologies are built to store and process knowledge about objects and dependencies between them. Thus, ontologies not only structure raw data, but also contain the meaning of those data. So far, ontology developers have been forced to provide the semantics of modeled objects and relations between them manually. The goal of this paper is to address some still unresolved problems related to providing meanings in ontologies. A typical ontology consists of concepts with attributes, relations between them, and instances. In our research, we focus on concepts level where attributes must be interpretable because they are the primary carriers of the meaning of the entire ontology. In other words, we need to assign semantics for each attribute within a concept. For this purpose, we have proposed a semi-automatic method for defining attribute semantics based on WordNet. The attribute semantics designated by human experts and based on WordNet does not affect the integration result. However, the developed method allows reducing the time for preparing an ontology to the integration process. What is more, it is less sensitive to the subjective evaluations done by experts.

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Section: Basic Notionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Semi-Automatic Definition of Attribute Semantics for the Purpose of Ontology Integration

2020

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“…In the case of ontology, the multi‐level integration has been introduced in Kozierkiewicz‐Hetmanska and Pietranik (). The findings have indicated that the difference between the final collective knowledge using the single‐level and multilevel integration processes are not significant.…”

Section: Consensus As a Tool For Processing Collective Inconsistency mentioning

confidence: 99%

An overview on consensus‐based approaches to processing collective inconsistency and knowledge integration

Nguyen

Truong

Merayo

et al. 2019

WIREs Data Min & Knowl

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The possibility of connecting billions of people around the world through network is the main characteristic of cyber‐physical systems production. To date, social web, virtual communities, and information sharing have been considered as additional sources of information, knowledge which is often referred to as the wisdom of crowds for information retrieval. In these cases, there exists a need to integrate all individual knowledge states originating from different sources concerning some common problems in the real world. Knowledge integration is considered as the key to improve decision quality and enhance the core competency of organizations. For such a task, the most important issue is processing inconsistency of knowledge. The first part of the paper presents an overview of consensus‐based approaches to processing collective inconsistency arising in the integration process. In addition, some issues concerning knowledge integration such as multi‐level integration and knowledge increase in a collective are also devoted. Furthermore, we will investigate the research problem concerning the role of collective knowledge in Collective Intelligence. This article is categorized under: Technologies > Data Preprocessing Fundamental Concepts of Data and Knowledge > Knowledge Representation Fundamental Concepts of Data and Knowledge > Big Data Mining

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Increasing the Quality of Multi-step Consensus

Dang

Nguyen

Hwang

2019

Intelligent Information and Database Systems

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The knowledge increase estimation framework for ontology integration on the concept level

Cited by 14 publications

References 12 publications

Semi-Automatic Definition of Attribute Semantics for the Purpose of Ontology Integration

Semi-Automatic Definition of Attribute Semantics for the Purpose of Ontology Integration

An overview on consensus‐based approaches to processing collective inconsistency and knowledge integration

Increasing the Quality of Multi-step Consensus

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