Database Integration Using Neural Networks: Implementation and Experiences

Li, Wen-Syan; Clifton, Chris; Liu, Shu-Yao

doi:10.1007/s101150050004

Cited by 59 publications

(30 citation statements)

References 18 publications

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“…While [47] used the criteria to evaluate the ontology matching tool Prompt, Ref. [1] uses the criteria to compare several schema matching evaluations, including those of Autoplex [24], Automatch [25], COMA [6], Cupid [14], LSD [19], Glue [29], SemInt [18,48,49], and SimilarityFlooding [21]. The COMA++ evaluation was conducted and documented according the criteria proposed in [1].…”

Section: Schema Matching Evaluationsmentioning

confidence: 99%

Matching large schemas: Approaches and evaluation

Rahm

2007

Information Systems

185

177

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Current schema matching approaches still have to improve for large and complex Schemas. The large search space increases the likelihood for false matches as well as execution times. Further difficulties for Schema matching are posed by the high expressive power and versatility of modern schema languages, in particular user-defined types and classes, component reuse capabilities, and support for distributed schemas and namespaces. To better assist the user in matching complex schemas, we have developed a new generic schema matching tool, COMA++, providing a library of individual matchers and a flexible infrastructure to combine the matchers and refine their results. Different match strategies can be applied including a new scalable approach to identify context-dependent correspondences between schemas with shared elements and a fragment-based match approach which decomposes a large match task into smaller tasks. We conducted a comprehensive evaluation of the match strategies using large e-Business standard schemas. Besides providing helpful insights for future match implementations, the evaluation demonstrated the practicability of our system for matching large schemas. r

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Section: Schema Matching Evaluationsmentioning

confidence: 99%

Matching large schemas: Approaches and evaluation

Rahm

2007

Information Systems

185

177

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“…Previous studies can be classified into schema-level matching [17,5,9,16] and instance-level matching [14,6,12,4]. A recent research [21] proposes a unified solution to address the two corresponding schema matching problems of intra-site and inter-site.…”

Section: Schema Matchingmentioning

confidence: 99%

Towards a global schema for web entities

Yao

Shou

et al. 2008

Proceedings of the 17th International Conference on World Wide Web

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Popular entities often have thousands of instances on the Web. In this paper, we focus on the case where they are presented in table-like format, namely appearing with their attribute names. It is observed that, on one hand, for the same entity, different web pages often incorporate different attributes; on the other, for the same attribute, different web pages often use different attribute names (labels). Therefore, it is imaginably difficult to produce a global attribute schema for all the web entities of a given entity type based on their web instances, although the global attribute schema is usually highly desired in web entity instances integration and web object extraction. To this end, we propose a novel framework of automatically learning a global attribute schema for all web entities of one specific entity type. Under this framework, an iterative instances extraction procedure is first employed to extract sufficient web entity instances to discover enough attribute labels. Next, based on the labels, entity instances, and related web pages, a maximum entropy-based schema discovery approach is adopted to learn the global attribute schema for the target entity type. Experimental results on the Chinese Web achieve weighted average Fscores of 0.7122 and 0.7123 on two global attribute schemas for person-type and movie-type web entities, respectively. These results show that our framework is general, efficient and effective.

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“…Several instance-level matching approaches [29,30,[51][52][53] use machine-learning techniques, such as neural networks and traditional classification methods, as follows. First, value instances of the first schema are characterized and matched, on a one-to-one basis, with value instances from the second schema.…”

Section: Automated Schema Matching Techniquesmentioning

confidence: 99%

Identifying Relationships between Scientific Datasets

Alawini¹

2000

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Scientific datasets associated with a research project can proliferate over time as a result of activities such as sharing datasets among collaborators, extending existing datasets with new measurements, and extracting subsets of data for analysis. As such datasets begin to accumulate, it becomes increasingly difficult for a scientist to keep track of their derivation history, which complicates data sharing, provenance tracking, and scientific reproducibility. Understanding what relationships exist between datasets can help scientists recall their original derivation history. For instance, if dataset A is contained in dataset B, then the connection between A and B could be that A was extended to create B.We present a relationship-identification methodology as a solution to this problem. To examine the feasibility of our approach, we articulated a set of relevant relationships, developed algorithms for efficient discovery of these relationships, and organized these algorithms into a new system called ReConnect to assist scientists in relationship discovery. We also evaluated existing alternative approaches that rely on flagging differences between two spreadsheets and found that they were impractical for many relationship-discovery tasks. Additionally, we conducted a user study, which showed that relationships do occur in real-world spreadsheets, and that ReConnect can improve scientists' ability to detect such relationships between datasets.The promising results of ReConnect's evaluation encouraged us to explore a ii more automated approach for relationship discovery. In this dissertation, we introduce an automated end-to-end prototype system, ReDiscover, that identifies, from a collection of datasets, the pairs that are most likely related, and the relationship between them. Our experimental results demonstrate the overall effectiveness of ReDiscover in predicting relationships in a scientist's or a small group of researchers' collections of datasets, and the sensitivity of the overall system to the performance of its various components.

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Database Integration Using Neural Networks: Implementation and Experiences

Cited by 59 publications

References 18 publications

Matching large schemas: Approaches and evaluation

Matching large schemas: Approaches and evaluation

Towards a global schema for web entities

Identifying Relationships between Scientific Datasets

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