A multi-attribute based framework for ontology aligning

Pietranik, Marcin; Nguyên, Ngoc Thanh

doi:10.1016/j.neucom.2014.03.067

Cited by 42 publications

(11 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main difference between the works mentioned above and the ideas presented in this article lies in the approach to the definition of ontologies. We proved in [25] that an approach to ontology alignment based on analyzing attributes semantics (as described in Section 2) gives good results. However, defining semantics manually is very errorprone, time and cost consuming.…”

Section: Related Workmentioning

confidence: 89%

“…By we denote a domain of a particular attribute a, hence it is obvious that the following property occurs: = ⋃ ∈ . We define an (A,V)-based ontology as a quintuple [25]:…”

Section: Basic Notionsmentioning

confidence: 99%

“…To the best of our knowledge, the research presented in [10] and [26] are the closest to our work. The aforementioned virtual documents ( [26]) and term vectors ( [10]) can be identified with the concepts' context from (5) and similarity calculations are close to methods presented in [25]. However, our ideas are independent of WordNet and can be used without any lexical database.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

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

2020

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 89%

“…By we denote a domain of a particular attribute a, hence it is obvious that the following property occurs: = ⋃ ∈ . We define an (A,V)-based ontology as a quintuple [25]:…”

Section: Basic Notionsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Also, this allows information exchange between different e-learning systems (Pietranik & Nguyen, 2014).…”

Section: Introductionmentioning

confidence: 99%

Instance-Based Ontology Matching For Open and Distance Learning Materials

Cerón-Figueroa

López-Yáñez

Villuendas-Rey

et al. 2017

IRRODL

View full text Add to dashboard Cite

show abstract

“…Another relevant interaction with knowledge engineering is ontology matching and integration [27], a fundamental activity in order to collect and process information in CCI. Since it is well known that this process is both time and resource-consuming, it is necessary to provide advanced architectures and platforms to reduce these costs [15] and good algorithms and frameworks to efficiently integrate ontologies [16,21].…”

mentioning

confidence: 99%

Collective intelligent information and database systems

Nguyen

Núñez

Trawiński

2017

IFS

View full text Add to dashboard Cite

Abstract. Collective intelligence is most often understood as group intelligence which arises on the basis of intelligences of the group members. This paper presents an overview of application of collective intelligence methods in knowledge engineering and in processing collective data. It also introduces papers included in this issue. Keywords: Collective intelligence, intelligent systems, knowledge engineeringCollective intelligence is most often understood as group intelligence which arises on the basis of intelligences of the group members. On the one hand, Newell [19] defined an intelligent collective as a social system, which is capable to act, even approximately, as a single rational agent. On the other hand, Lévy [11] understood collective intelligence as an intelligence that emerges from the collaboration and competition of many individuals; an intelligence that seemingly has a mind of its own. These definitions refer to cognitive systems. From the computational and artificial intelligence point of view, we can think about a collective as a set of autonomous units working on some common task, for example, a multi-agent system. We can say that the collective is intelligent if it can make a use of the intelligences of its members for solving some problem, for example, a decision making problem. are based on the collective intelligence aspects and which use the computational techniques for solving these problems. Regarding knowledge engineering, computational collective intelligence provides methods and techniques for determining the knowledge of a collective as a whole on the basis of collective members' knowledge. The need for processing collective knowledge is quickly increasing because of the very fast development of Internet, social networks and distributed databases. It is obvious that knowledge originating from autonomous sources for the same subject is very often inconsistent. Therefore, the aspects of inconsistency processing and integration computing are very important.Knowledge engineering [9] plays a relevant role in CCI since it is necessary to use its techniques in order to represent individual information. In fact, there are studies advocating that scientific knowledge is essentially collective knowledge [23]. In this line, recent studies state that it is very important that different individuals provide orthogonal, highly unrelated, and possible contradictory knowledge to the collectivity. In other words, "the higher the inconsistency, the better the quality of collective knowledge" [20]. Another relevant interaction

show abstract

A multi-attribute based framework for ontology aligning

Cited by 42 publications

References 22 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

Instance-Based Ontology Matching For Open and Distance Learning Materials

Collective intelligent information and database systems

Contact Info

Product

Resources

About