A Self-Adaptive and Incremental Approach for Data Profiling in the Semantic Web

Kellou-Menouer, Kenza; Kedad, Zoubida

doi:10.1007/978-3-662-54037-4_4

Cited by 6 publications

(6 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our proposal, we rely on previous works on schema discovery for RDF data sources [1], [2]. This approach discovers the types contained in a data source and the links between them, without requiring any schema related information in the data source or any parameter provided by the user.…”

Section: Schema Discoverymentioning

confidence: 99%

“…It has been extended to provide for each discovered type a profile composed of properties and their respective probabilities [1]. This profile is used to discover overlapping types [2].…”

Section: B Type Discoverymentioning

confidence: 99%

“…Furthermore, the number of classes is not required and the resulting clusters are of arbitrary shape. In [2], an extension of this algorithm is proposed to automatically determine the similarity threshold for grouping entities, according to the density distribution of the data. A cluster of similar entities corresponds to a type definition.…”

Section: B Type Discoverymentioning

confidence: 99%

“…In addition to type definitions, links between these types are also useful to understand the content of a data source. The approach presented in [1], [2] generates two types of links: semantic links, corresponding to user-defined properties and hierarchical links, corresponding to the rdfs:subClassOf property.…”

Section: Link Discoverymentioning

confidence: 99%

“…Our goal is, given a large RDF dataset where the schema is missing, to ensure the scalability of a schema discovery approach. We rely on previous works on schema discovery that describe an approach using a densitybased clustering algorithm (DBscan) to extract a schema from an RDF dataset [1], [2]; this approach provides a schema with a good quality, but the underlying algorithms are costly and not suitable for massive datasets.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Scaling Up Schema Discovery for RDF Datasets

Bouhamoum¹,

Kellou-Menouer²,

Lopes³

et al. 2018

2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW)

Self Cite

View full text Add to dashboard Cite

An increasing number of data sources is published on the Web, expressed using the languages proposed by the W3C such as RDF. In these sources, data is not constrained by a schema: data could differ from the schema-related statements provided in the source; furthermore, the schema could be incomplete or even missing; this makes the use of the data sources difficult. Some works have addressed the problem of automatic schema discovery but their scalability and their use in a big data context remain a challenge. In this work, we address this scalability issue, which is mainly related to the clustering algorithms at the core of schema discovery. In order to process large amounts of data, we propose to build a condensed representation of the initial dataset by extracting patterns representing all the existing combinations of properties. The clustering is then performed on the patterns instead of the initial dataset. In this paper, we describe our approach, and present its implementation using a big data technology. We also present some experimental evaluations performed on real datasets.

show abstract

Section: Schema Discoverymentioning

confidence: 99%

“…It has been extended to provide for each discovered type a profile composed of properties and their respective probabilities [1]. This profile is used to discover overlapping types [2].…”

Section: B Type Discoverymentioning

confidence: 99%

Section: B Type Discoverymentioning

confidence: 99%

Section: Link Discoverymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations