Semantic conflict resolution ontology (SCROL): an ontology for detecting and resolving data and schema-level semantic conflicts

Ram, Sudha; Park, Jinsoo

doi:10.1109/tkde.2004.1269597

Cited by 137 publications

(87 citation statements)

References 21 publications

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“…Although there are several publications that classify structural heterogeneity into various types of conflicts [5,6,10,11], this paper focuses on four main kinds of such conflicts, as described below.…”

Section: B Structural Heterogeneitymentioning

confidence: 99%

“…The meaning of the information described and the differences in design among information systems lead to information heterogeneity problems (or semantic conflicts). In this paper, we classify the problem into two main levels, which are described below [5,6]. 1) Semantic heterogeneity occurs when there is disagreement about the meaning, interpretation, or intended use of the same or related data.…”

Section: Introductionmentioning

confidence: 99%

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Ontology Mapping and Rule-Based Inference for Learning Resource Integration

Jetinai¹,

Arch-int²,

Arch-int³

2016

Journal of information and communication convergence engineerin

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With the increasing demand for interoperability among existing learning resource systems in order to enable the sharing of learning resources, such resources need to be annotated with ontologies that use different metadata standards. These different ontologies must be reconciled through ontology mediation, so as to cope with information heterogeneity problems, such as semantic and structural conflicts. In this paper, we propose an ontology-mapping technique using Semantic Web Rule Language (SWRL) to generate semantic mapping rules that integrate learning resources from different systems and that cope with semantic and structural conflicts. Reasoning rules are defined to support a semantic search for heterogeneous learning resources, which are deduced by rule-based inference. Experimental results demonstrate that the proposed approach enables the integration of learning resources originating from multiple sources and helps users to search across heterogeneous learning resource systems.

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Section: B Structural Heterogeneitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ontology Mapping and Rule-Based Inference for Learning Resource Integration

Jetinai¹,

Arch-int²,

Arch-int³

2016

Journal of information and communication convergence engineerin

View full text Add to dashboard Cite

show abstract

“…Such as, aspect-oriented programming (Bergmans, 2003), database (Sattler et al, 2003;Sudha and Jinsoo, 2004), model-based development (Reiter and Altmanninger, 2007), Web services (Nagarajan et al, 2006;Nagarajan and Verma, 2007;Ying et al, 2008;Li et al, 2009) and so on.…”

Section: Related Workmentioning

confidence: 99%

Semantic Conflicts Detection of Heterogeneous Messages of Web Services: Challenges and Solution

Al-Baltah

Ghani

Rahman

et al. 2014

Journal of Computer Science

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Service Oriented Architecture (SOA) is a new design style that has promised to alleviate the semantic interoperability problem between Web services. Exchanging the messages seamlessly between heterogeneous Web services is required but, it is still difficult due to data heterogeneity. As a result, a noticeable number of works has been proposed with the aim of solving this problem, but yet, the problem has not been solved efficiently. Furthermore, it is important to observe the lack of sufficient approaches in the state of the art, whose purposes are semantic conflicts detection between heterogeneous messages of Web services. It is for these reasons we take a step back to the detection step before providing the solution. This study proposes a new ontology-based approach that aim at detecting semantic conflicts between heterogeneous messages of Web services. The main purposes of this approach are to detect any conflict types between the messages during message exchange process and to identify the conflict type for each detected conflict. The proposed approach plays as a vital step for improving the semantic interoperability between heterogeneous Web service messages, since it facilitate the process of addressing semantic conflicts. A real scenario was used in order to demonstrate the feasibility of the proposed approach.

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“…There have been a number of studies that identify and catalog various semantic heterogeneities [3,11,16,17]. A subset of the heterogeneities are related to data quality that we address in this paper and can be categorized into two main groups: (1) representational heterogeneity and (2) ontological heterogeneity.…”

Section: Heterogeneous Data Semanticsmentioning

confidence: 99%

Addressing the Challenges of Aggregational and Temporal Ontological Heterogeneity

Zhu

Madnick

2005

SSRN Journal

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-In this paper, we first identify semantic heterogeneities that, when not resolved, often cause serious data quality problems. We discuss the especially challenging problems of temporal and aggregational ontological heterogeneity, which concerns how complex entities and their relationships are aggregated and reinterpreted over time. Then we illustrate how the COntext INterchange (COIN) technology can be used to capture data semantics and reconcile semantic heterogeneities in a scalable manner, thereby improving data quality.Index Terms -Data Semantics, Semantic Heterogeneity, Aggregation, Temporal, Ontology, Context. I. INTRODUCTIONN our research, we have discovered that many problems arise due to confusion regarding data semantics. To illustrate how complex this can become, consider Fig. 1. This data summarizes the P/E ratio for DaimlerChrysler obtained from four different financial information sources -all obtained on the same day within minutes of each other. Note that the four sources gave radically different values for P/E ratio. are all correct! The issue is, what do you really mean by "P/E ratio" 1 . The answer lies in the multiple interpretations and uses of the term "P/E ratio" in financial circles. The earnings are for the entire year in some sources but in one source are only for the last quarter. Even when earnings are for a full year, are they:-the last 12 months? -the last calendar year? -the last fiscal year? or -the last three historical quarters and the estimated current quarter -a popular usage? Such information, which we call context, is often not explicitly captured in a form that can be used by the query answering system to reconcile semantic differences in data from different sources. Serious consequences can result from not being aware of the differences in contexts and data semantics. Consider a financial trader that used DBC to get P/E ratio information yesterday and got 19.19. Today he used Bloomberg and got 5.57 (low P/E's usually indicate good bargains) -thinking that something wonderful had happened he might decide to buy many shares of DaimlerChrysler today. In fact, nothing had actually changed, except for changing the source that he used. It would be natural for this trader (after possibly losing a significant amount of money due to this decision) to feel that he had encountered a data quality problem.We would argue that what appeared to be a data quality problem is actually a data misinterpretation problem. The data source did not have any "error," the data that it provided was exactly the data that it intended to provideit just did not have the meaning that the receiver expected. In other words, the issue is not what is right or wrong, it is about how data in one context can be used in a different context.Before going any further, it should be noted that if all sources and all receivers of data always had the exact same meanings, this problem would not occur. This is a desirable goal -one frequently sought through 1 Some of these sites even provide a glossary which give...

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Semantic conflict resolution ontology (SCROL): an ontology for detecting and resolving data and schema-level semantic conflicts

Cited by 137 publications

References 21 publications

Ontology Mapping and Rule-Based Inference for Learning Resource Integration

Ontology Mapping and Rule-Based Inference for Learning Resource Integration

Semantic Conflicts Detection of Heterogeneous Messages of Web Services: Challenges and Solution

Addressing the Challenges of Aggregational and Temporal Ontological Heterogeneity

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