Finding similar identities among objects from multiple web sources

Carvalho, Joyce C. P.; Silva, Altigran S. da

doi:10.1145/956718.956719

Cited by 7 publications

(9 citation statements)

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“…And also check for its typographical errors when comparing two XML elements. The concern with accuracy was later approached by Carvalho and da Silva, in [12]. Although not specifically focused on XML, their work proposes a solution to the problem of integrating treestructured data extracted from the web.…”

Section: IIImentioning

confidence: 99%

EDDDS: An Efficient Duplicate Data Detection System

Dhake¹,

S.S.²,

Y.R.³

et al. 2015

International Journal of Advanced Research in Computer and Comm

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Duplicate Detection is critical task of any database of any organization. Duplicates are nothing but the same real time entities or objects are presented in the form of different structure and in the different formats. We can find out the duplicates in relational data, in complex data and hierarchical data like XML. There are lots of works already presented in the past for finding the duplicates in the relational data. But nowadays there is more focus on finding duplicates in the XML data. Because of XML is very popular for data storing and extensively used for data exchange between the organizations. Here we have done an extensive literature survey on this topic and proposed a duplicate detection method that incorporates some of the existing paper's ideas and some of our original ideas. In addition to improving the efficiency and effectiveness, we also checks for its typographical errors when comparing the two XML elements. To test the correctness of our method, we are comparing it with existing duplicate detection system, and giving more focus on how we get higher precision and recall values in the various datasets we have used.

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Section: IIImentioning

confidence: 99%

EDDDS: An Efficient Duplicate Data Detection System

Dhake¹,

S.S.²,

Y.R.³

et al. 2015

International Journal of Advanced Research in Computer and Comm

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“…These differences arise because data is affected by many factors, such as external noise, human subjectivity, and un-calibrated measuring tools. While some systems attempt to match features by introspecting their properties [18], we avoid exhaustive attribute comparisons as they tend to increase computational complexity when many geographic features are present. To establish an efficient and effective representation of semantic relationships, we define semantic footprints and their components in the subsections below.…”

Section: Concept Of Semantic Footprintsmentioning

confidence: 99%

A framework for the expansion of spatial features based on semantic footprints

Santos

Boedihardjo

2011

2011 IEEE International Conference on Systems, Man, and Cybernetics

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“…They also focus on efficiency and effectiveness of their filtering techniques, but not on the effectiveness of the actual duplicate detection. The only approach we are aware of that considers recall and precision of their XML similarity joins is [4]. They present four different strategies to define the similarity function using the vector space model.…”

Section: Object Identification For Hierarchical and Xml Datamentioning

confidence: 99%

DogmatiX tracks down duplicates in XML

Weis

Naumann

2005

Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data

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Duplicate detection is the problem of detecting different entries in a data source representing the same real-world entity. While research abounds in the realm of duplicate detection in relational data, there is yet little work for duplicates in other, more complex data models, such as XML. In this paper, we present a generalized framework for duplicate detection, dividing the problem into three components: candidate definition defining which objects are to be compared, duplicate definition defining when two duplicate candidates are in fact duplicates, and duplicate detection specifying how to efficiently find those duplicates.Using this framework, we propose an XML duplicate detection method, DogmatiX, which compares XML elements based not only on their direct data values, but also on the similarity of their parents, children, structure, etc. We propose heuristics to determine which of these to choose, as well as a similarity measure specifically geared towards the XML data model. An evaluation of our algorithm using several heuristics validates our approach. XML DUPLICATE DETECTIONDuplicate detection is the problem of determining that different representations of entities in a data source actually represent the same real-world entity. The most prominent application area for duplicate detection is customer relationship management (CRM), where multiple entries of the same customer can result in multiple mailings to the same person, incorrect aggregation of sales to a certain customer, etc. Other application areas include bioinformatics, catalog integration, and in general any domain where independently collected data is integrated.The problem has been addressed extensively for relational data stored in tables. However, more and more of today's data is represented in non-relational form. In particular, XML is increasingly popular, especially for data published on the Web and data exchanged between organizations. Conventional methods do not trivially adapt, so there is a Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage, and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. need for methods to detect duplicate objects in nested XML data. XML data is semi-structured and is organized hierarchically. This complicates the object identification task, compared to relational data that is flat and usually wellstructured. We face two problems: object definition and structural diversity.Object definition refers to the problem of defining which data values actually describe an object, i.e., which values to consider when comparing two objects. Methods for relational duplicate detection assume that each tuple represents an object and all attribute values describe that object. Sufficiently similar data values of two tu...

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Finding similar identities among objects from multiple web sources

Cited by 7 publications

References 0 publications

EDDDS: An Efficient Duplicate Data Detection System

EDDDS: An Efficient Duplicate Data Detection System

A framework for the expansion of spatial features based on semantic footprints

DogmatiX tracks down duplicates in XML

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