Assigning Semantic Labels to Data Sources

Ramnandan, S.K.; Mittal, Amol; Knoblock, Craig A.; Szekely, Pedro

doi:10.1007/978-3-319-18818-8_25

Cited by 51 publications

(72 citation statements)

References 9 publications

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“…The initialization and linking of any node is modeled as the RDF triples (NodeId rdf:type S:Type) and (ParentNodeId S:relation NodeId), respectively (line 12). Next ToRDF(), based on the type of the node, appends these triples to S ij (lines [6][7][8][9][10][11][12][13][14][15]. Depending on the complexity of F's structure, a labelvalue pair may represent a flat attribute in F (e.g.…”

Section: Schema Extraction Algorithmmentioning

confidence: 99%

“…Typically, computing mappings between a source schema and a tagging concept involves specifying the semantic types of the source schema elements, i.e., labeling each schema element with a semantically equivalent property in the tagging concept [13]. However, semantic labeling alone is not sufficient [41], and a precise mapping computation process requires an extra step that specifies how the elements of a source schema should be organized in accordance with the structure of its tagging concept so that the two constructs become semantically compatible and ready for mapping.…”

Section: Mapping Computation and Managementmentioning

confidence: 99%

“…Three matcher plugins are used. The first two, SemanticTyper [13] and AgreementMaker [43], are both open source matching approaches [52,53]. SemanticTyper is an instance-level schema matcher that collects statistical information about the data based on their type and decides if two schema elements match.…”

Section: Automatic Mapping Management Evaluationmentioning

confidence: 99%

“…Manually handling heterogeneity reconciliations would pose a huge burden on PDL users. Despite efforts in the fields of semantic web and data integration for automating the reconciliation process [12][13][14], existing approaches, most of which require optimized parameter tuning and expertise-based configurations to cope with the heterogeneities of data [10], cannot be adopted in PDL.…”

mentioning

confidence: 99%

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SemLinker: automating big data integration for casual users

Alrehamy

Walker

2018

J Big Data

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Section: Schema Extraction Algorithmmentioning

confidence: 99%

Section: Mapping Computation and Managementmentioning

confidence: 99%

Section: Automatic Mapping Management Evaluationmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

SemLinker: automating big data integration for casual users

Alrehamy

Walker

2018

J Big Data

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“…The majority of these approaches [2,28,45,55,66,70,73,76] assume well-formed relational tables and try to derive semantic labels for attributes in these structured data sources (such as columns in tables) which are used to (i) map the schema of the data source to ontologies or existing semantic models or (ii) categorize the content of a data source.…”

Section: Annotation Labelling and Integration Of Tabular Datamentioning

confidence: 99%

Data Integration for Open Data on the Web

Neumaier

Polleres

Steyskal

et al. 2017

Reasoning Web. Semantic Interoperability on the Web

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Abstract. In this lecture we will discuss and introduce challenges of integrating openly available Web data and how to solve them. Firstly, while we will address this topic from the viewpoint of Semantic Web research, not all data is readily available as RDF or Linked Data, so we will give an introduction to different data formats prevalent on the Web, namely, standard formats for publishing and exchanging tabular, tree-shaped, and graph data. Secondly, not all Open Data is really completely open, so we will discuss and address issues around licences, terms of usage associated with Open Data, as well as documentation of data provenance. Thirdly, we will discuss issues connected with (meta-)data quality issues associated with Open Data on the Web and how Semantic Web techniques and vocabularies can be used to describe and remedy them. Fourth, we will address issues about searchability and integration of Open Data and discuss in how far semantic search can help to overcome these. We close with briefly summarizing further issues not covered explicitly herein, such as multi-linguality, temporal aspects (archiving, evolution, temporal querying), as well as how/whether OWL and RDFS reasoning on top of integrated open data could be help.

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Table understanding approaches for extracting knowledge from heterogeneous tables

Bonfitto

Casiraghi

Mesiti

2021

WIREs Data Min & Knowl

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Table understanding methods extract, transform, and interpret the information contained in tabular data embedded in documents/files of different formats. Such automatic understanding would allow to exploit tabular information with the aim of accurately answering queries, or integrating heterogeneous repositories of information in a common knowledge base, or exchanging information among different sources. The purpose of this survey is to provide a comprehensive analysis of the research efforts so far devoted to the problem of table understanding and to describe systems that support the transformation of heterogeneous tables into meaningful information. This article is categorized under: Application Areas > Data Mining Software Tools Technologies > Data Preprocessing Technologies > Structure Discovery and Clustering

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Assigning Semantic Labels to Data Sources

Cited by 51 publications

References 9 publications

SemLinker: automating big data integration for casual users

SemLinker: automating big data integration for casual users

Data Integration for Open Data on the Web

Table understanding approaches for extracting knowledge from heterogeneous tables

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