Interoperability In Collaborative Network Of Biodiversity Organizations

Unal, Ozgul

doi:10.1007/978-0-387-38269-2_54

Cited by 5 publications

(3 citation statements)

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“…In this respect, the Semi Automatic Schema Matching and INTegration (SASMINT) approach and system [60][61][62] described in this paper is designed and implemented to enable semi-automated matching of relational schemas by using a combination of metrics and algorithms from Natural Language Processing (NLP) and Graph Theory. Furthermore, after the schema matching is executed, SASMINT allows its user to modify and approve the results.…”

Section: Introductionmentioning

confidence: 99%

Semi-automated schema integration with SASMINT

Unal

2009

Knowl Inf Syst

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The emergence of increasing number of collaborating organizations has made clear the need for supporting interoperability infrastructures, enabling sharing and exchange of data among organizations. Schema matching and schema integration are the crucial components of the interoperability infrastructures, and their semi-automation to interrelate or integrate heterogeneous and autonomous databases in collaborative networks is desired. The Semi-Automatic Schema Matching and INTegration (SASMINT) System introduced in this paper identifies and resolves several important syntactic, semantic, and structural conflicts among schemas of relational databases to find their likely matches automatically. Furthermore, after getting the user validation on the matched results, it proposes an integrated schema. SASMINT uses a combination of a variety of metrics and algorithms from the Natural Language Processing and Graph Theory domains for its schema matching. For the schema integration, it utilizes a number of derivation rules defined in the scope of the research work explained in this paper. Furthermore, a derivation language called SASMINT Derivation Markup Language (SDML) is defined for capturing and formulating both the results of matching and the integration that can be further used, for example for federated query processing from independent databases. In summary, the paper focuses on addressing: (1) conflicts among schemas that make automatic schema matching and integration difficult, (2) the main components of the SASMINT approach and system, (3) in-depth exploration of SDML, (4) heuristic rules designed and implemented as part of the schema integration component of the SASMINT system, and (5) experimental evaluation of SASMINT.

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Section: Introductionmentioning

confidence: 99%

Semi-automated schema integration with SASMINT

Unal

2009

Knowl Inf Syst

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“…Another limitation of the previous approaches is that they typically do not combine different match algorithms in a flexible way. Taking these limitations into account, we propose the SASMINT (Semi Automatic Schema Matching and INTegration) system and approach [3][4][5]. SASMINT proposes a solution to automate the processes related to interlinking of heterogeneous relational databases, particularly focused on schema matching and schema integration in collaborative environments including different forms of collaborative networks.…”

Section: Introductionmentioning

confidence: 99%

Schema Matching and Integration for Data Sharing Among Collaborating Organizations

Unal¹

2009

JSW

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Abstract-Schema matching and schema integration are important components of the data sharing infrastructure in Collaborative Networks. In order to achieve more accurate matching and integration results and enhance efficiency, it is required to provide some mechanisms to carry out these processes as automatically as possible. This paper addresses the problems and challenges related to schema matching and schema integration and introduces the Semi-Automatic Schema Matching and INTegration (SASMINT) system to automate these processes. Other systems aiming at database interoperability typically focus either on schema matching or on schema integration. On the other hand, the SASMINT system combines them and uses the results of schema matching for semi-automatic schema integration. SASMINT follows a composite approach in schema matching, which means it combines the results of variety of algorithms, making it a generic tool applicable for different types of schemas. It also proposes a Sampler component for helping the user to assign the weights to algorithms. Furthermore, SASMINT uses an XML-based derivation language to save the results of schema matching and schema integration, and also to define the components of integrated schemas, in order to further support automated query processing against integrated sources.

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“…In most previous approaches aiming to enable interoperability among databases, schema matching is performed manually, which is an error-prone and time consuming task. However, the SASMINT (Semi-Automatic Schema Matching and INTegration) system (Unal and Afsarmanesh, 2006), introduced as a part of the Collaborative Information Management System (CIMS) proposed in the ENBI project for the biodiversity domain, identifies the syntactic, semantic, and structural similarities automatically, and expects the user to accept, change, or reject the results. Furthermore, if there is a need to produce an integrated schema of the domain, it automatically generates the integrated schema using the results of schema matching.…”

Section: Introductionmentioning

confidence: 99%

Using Linguistic Techniques for Schema Matching

Unal¹

2006

Proceedings of the First International Conference on Software and Data Technologies

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In order to deal with the problem of semantic and schematic heterogeneity in collaborative networks, matching components among database schemas need to be identified and heterogeneity needs to be resolved, by creating the corresponding mappings in a process called schema matching. One important step in this process is the identification of the syntactic and semantic similarity among elements from different schemas, usually referred to as Linguistic Matching. The Linguistic Matching component of a schema matching and integration system, called SASMINT, is the focus of this paper. Unlike other systems, which typically utilize only a limited number of similarity metrics, SASMINT makes an effective use of NLP techniques for the Linguistic Matching and proposes a weighted usage of several syntactic and semantic similarity metrics. Since it is not easy for the user to determine the weights, SASMINT provides a component called Sampler as another novelty, to support automatic generation of weights.

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Interoperability In Collaborative Network Of Biodiversity Organizations

Cited by 5 publications

References 12 publications

Semi-automated schema integration with SASMINT

Semi-automated schema integration with SASMINT

Schema Matching and Integration for Data Sharing Among Collaborating Organizations

Using Linguistic Techniques for Schema Matching

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