Automated resolution of semantic heterogeneity in multidatabases

Bright, M. W.; Hurson, Ali R.; Pakzad, S. H.

doi:10.1145/176567.176569

Cited by 177 publications

(97 citation statements)

References 21 publications

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“…(1) the structural approach: concepts are in an is-a taxonomy, and the similarity is computed by counting the (weighted) edges in the paths from the considered concepts to their most specific ancestor [41,31,10,27,34]. Concepts that are connected by a few links are similar; concepts that are connected by many links are less similar.…”

Section: The Gcs-based Similarity Measurementioning

confidence: 99%

“…6). Opposite to other semantic similarity measures, this rationale does not require the overlap of the compared concepts [42,9,13,14], and does not take into account the structural path distance between concepts [41,31,10,34]. The measure combines the extensional size of concept expressions (to reflect their model semantics) and the intensional generalization (the GCS) of the considered concepts so that the KB of reference is also exploited.…”

Section: The Gcs-based Similarity Measurementioning

confidence: 99%

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Dl-Link: A Conceptual Clustering Algorithm for Indexing Description Logics Knowledge Bases

d’Amato

Staab

Fanizzi

et al. 2010

Int. J. Semantic Computing

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Efficient resource retrieval is a crucial issue, particularly when semantic resource descriptions are considered which enable the exploitation of reasoning services during the retrieval process. In this context, resources are commonly retrieved by checking if each available resource description satisfies the given query. This approach becomes inefficient with the increase of available resources. We propose a method for improving the retrieval process by constructing a tree index through a new conceptual clustering method for resources expressed as class definitions or as instances of classes in ontology languages. The available resource descriptions are located at the leaf nodes of the index, while inner nodes represent intensional descriptions (generalizations) of their child nodes. The retrieval is performed by following the tree branches whose nodes satisfy the query. Query answering time may be improved as the number of retrieval steps may be O(log n) in the best case.

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Section: The Gcs-based Similarity Measurementioning

confidence: 99%

Section: The Gcs-based Similarity Measurementioning

confidence: 99%

Dl-Link: A Conceptual Clustering Algorithm for Indexing Description Logics Knowledge Bases

d’Amato

Staab

Fanizzi

et al. 2010

Int. J. Semantic Computing

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“…[3,4] The first step in handling semantic heterogeneity should be the attempt to enrich the semantic information about documents, i.e. to fill up the gaps in the documents meta-data automatically.…”

Section: Treatment Of Semantic Heterogeneitymentioning

confidence: 99%

Meta-data Extraction and Query Translation. Treatment of Semantic Heterogeneity

Strötgen¹

2002

Research and Advanced Technology for Digital Libraries

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Abstract. The project CARMEN 1 ("Content Analysis, Retrieval and Metadata: Effective Networking") aimed among other goals at improving the expansion of searches in bibliographic databases into Internet searches. We pursued a set of different approaches to the treatment of semantic heterogeneity (meta-data extraction, query translation using statistic relations and cross-concordances). This paper describes the concepts and implementation of this approaches and the evaluation of the impact for the retrieval result.

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“…The Summary Schemas Model (SSM) has been proposed in [6] as an efficient means to access data in a heterogeneous multidatabase environment. The SSM uses a hierarchical meta structure that provides an incrementally concise view of the data in the form of summary schemas.…”

Section: Summary Schemas Modelmentioning

confidence: 99%

“…The model is built on the concept of global transactions in multidatabase based on the Summary Schemas Model [6]. This work expands our effort reported in [13] by implementing an additional layer on top of the MDBS that handles mobile transactions, disconnection, and long-lived transaction.…”

Section: Introductionmentioning

confidence: 99%

A Transaction Processing Model for the Mobile Data Access System

Segun

Hurson

Spink

2001

Lecture Notes in Computer Science

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Abstract. Advances in wireless networking technology and portable computing devices have led to the emergence of a new computing paradigm known as mobile computing and a number of applications. As a result, software applications have to be redesigned to take advantage of this environment while accommodating the new challenges posed by mobility. As mobile users wander about, they are bound to encounter a variety of different information sources (databases) that are often autonomous and heterogeneous in nature. Such a collection of autonomous and heterogeneous database is often known as a multidatabase. The existing multidatabase systems do not readily support mobile computing. A new class of multidatabase that provides access to a large collection of data via a wireless networking connection is proposed -a Mobile Data Access System (MDAS). Within the scope of MDAS, a new transaction-processing model is proposed that allows timely and reliable access to heterogeneous and autonomous data sources while coping with the mobility issue. The proposed model extends the existing multidatabase system without any adverse effect to the preexisting local and global users. This is accomplished through the implementation of multi tiered mobile transaction proxies that manage the execution of mobile transactions on behalf of the mobile user. The proposed transaction-processing model is simulated and the results are analyzed.

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Automated resolution of semantic heterogeneity in multidatabases

Cited by 177 publications

References 21 publications

Dl-Link: A Conceptual Clustering Algorithm for Indexing Description Logics Knowledge Bases

Dl-Link: A Conceptual Clustering Algorithm for Indexing Description Logics Knowledge Bases

Meta-data Extraction and Query Translation. Treatment of Semantic Heterogeneity

A Transaction Processing Model for the Mobile Data Access System

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