A Detailed Analysis of the Quality of Stream-Based Schema Construction on Linked Open Data

The Semantic Web: Semantics and Big Data

Knauf

Scheglmann

et al. 2013

Self Cite

Schema information about resources in the Linked Open Data (LOD) cloud can be provided in a twofold way: it can be explicitly defined by attaching RDF types to the resources. Or it is provided implicitly via the definition of the resources' properties. In this paper, we present a method and metrics to analyse the information theoretic properties and the correlation between the two manifestations of schema information. Furthermore, we actually perform such an analysis on large-scale linked data sets. To this end, we have extracted schema information regarding the types and properties defined in the data set segments provided for the Billion Triples Challenge 2012. We have conducted an in depth analysis and have computed various entropy measures as well as the mutual information encoded in the two types of schema information. Our analysis provides insights into the information encoded in the different schema characteristics. Two major findings are that implicit schema information is far more discriminative and that applications involving schema information based on either types or properties alone will only capture between 63.5% and 88.1% of the schema information contained in the data. Based on these observations, we derive conclusions about the design of future schemas for LOD as well as potential application scenarios.

Section: Data Setssupporting

confidence: 89%

Section: Related Workmentioning

confidence: 99%

Section: The Schemex Index As Basis For the Analysismentioning

confidence: 99%

“…https://github.com/cygri/make-void (accessed 9 March 2013) 3. http://rdfstats.sourceforge.net/ (accessed 9 March 2013) 4. http://webdatacommons.org/ (accessed 9 March 2013).…”

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confidence: 99%

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A Systematic Investigation of Explicit and Implicit Schema Information on the Linked Open Data Cloud

The Semantic Web: Semantics and Big Data

Knauf

Scheglmann

et al. 2013

Self Cite

“…As basis for LODatio we make use of a schema-level index called SchemEX [6,4]. A SchemEX index consists of two parts: (a) the schema which serves for the look-up functionality and (b) the references to meta data which corresponds to the payload in the index.…”

Section: A Schema-level Indexmentioning

confidence: 99%

LODatio

Proceedings of the Seventh International Conference on Knowledge Capture

Scherp

Krayer

et al. 2013

Self Cite

The Linked Open Data (LOD) cloud provides a vast amount of heterogeneous data, distributed over numerous data sources. This makes it difficult to find those data sources in the cloud which are relevant for a given information need. Existing search engines for the Semantic Web focus on instance-oriented information needs, i. e., searching for specific RDF instances or literals and exploring the search results. However, they do not address the question of finding linked data sources relevant to a schema-oriented information need, i. e., queries based on triple patterns relating to a specific combination of RDF types and/or properties. In this paper, we present the semantic search system LODatio leveraging a schema-level index for finding sources of Linked Data relevant to a schema-oriented information need. Beyond its capability to retrieve relevant data sources, LODatio actively supports the user in his schema-oriented search tasks. To this end, it provides ranked result lists of relevant data sources together with example snippets and an estimation of the result set size. Furthermore, LODatio provides support for novel features in semantic search such as recommending alternative queries in order to refine or broaden the result set.

Perplexity of Index Models over Evolving Linked Data

Lecture Notes in Computer Science

2014

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In this paper we analyse the sensitivity of twelve prototypical Linked Data index models towards evolving data. Thus, we consider the reliability and accuracy of results obtained from an index in scenarios where the original data has changed after having been indexed. Our analysis is based on empirical observations over real world data covering a time span of more than one year. The quality of the index models is evaluated w.r.t. their ability to give reliable estimations of the distribution of the indexed data. To this end we use metrics such as perplexity, cross-entropy and Kullback-Leibler divergence. Our experiments show that all considered index models are affected by the evolution of data, but to different degrees and in different ways. We also make the interesting observation that index models based on schema information seem to be relatively stable for estimating densities even if the schema elements diverge a lot.