SchemEX — Efficient construction of a data catalogue by stream-based indexing of linked data

Konrath, Mathias, Gottron, Thomas, Staab, Steffen and Scherp, Ansgar (2012) SchemEX — Efficient construction of a data catalogue by stream-based indexing of linked data Web Semantics: Science, Services and Agents on the World Wide Web, 16 . pp. 52-58. DOI 10.1016/j.websem.2012.06.002.

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We present SchemEX, an approach and tool for a stream-based indexing and schema extraction of Linked Open Data (LOD) at web-scale. The schema index provided by SchemEX can be used to locate distributed data sources in the LOD cloud. It serves typical LOD information needs such as finding sources that contain instances of one specific data type, of a given set of data types (so-called type clusters), or of instances in type clusters that are connected by one or more common properties (so-called equivalence classes). The entire process of extracting the schema from triples and constructing an index is designed to have linear runtime complexity. Thus, the schema index can be computed on-the-fly while the triples are crawled and provided as a stream by a linked data spider. To demonstrate the web-scalability of our approach, we have computed a SchemEX index over the Billion Triples Challenge (BTC) dataset 2011 consisting of 2,170 million triples. In addition, we have computed the SchemEX index on a dataset with 11 million triples. We use this smaller dataset for conducting a detailed qualitative analysis. We are capable to locate relevant data sources with recall between 71% and 98% and a precision between 74% and 100% at a window size of 100K triples observed in the stream and depending on the complexity of the query, i.e. if one wants to find specific data types, type clusters or equivalence classes.

Publication (PDF): SchemEX---Efficient Construction of a Data Catalogue by stream-based Indexing of Linked Data. Available from: [accessed Apr 3, 2017].

Document Type: Article
Refereed: Yes
DOI etc.: 10.1016/j.websem.2012.06.002
ISSN: 15708268
Date Deposited: 03 Apr 2017 08:37
Last Modified: 03 Apr 2017 08:37

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