Understanding knowledge networks. Mangaladevi, K., Beek, W., & Kuhn, T. CEUR workshop proceedings, 1946:38–49, CEUR Workshop Proceedings, 2017. abstract bibtex The emergence of Linked Open Data (LOD) enables data on the Web to have a well defined structure and thereby to represent and in? terlink information from different sources and application areas. This web of data is a complex socially created network, where concepts and rela? tions are connected in intricate ways, collectively forming a network of knowledge. These data are published in a decentralized fashion and they stem from different sources, have different types of relationships, and use different terminologies, ontologies and meta models. While this so-called LOD cloud has become a very valuable resource, we know only very little about the general structural properties of the contained data, which im? pedes our ability to use and organize this resource in an efficient and accu? rate manner. The objective of this paper is to provide a basic understand? ing of LOD from the point of view of network structures. We analyze LOD networks with respect to fundamental network properties such as degree distribution and clustering. Using these metrics, we compare our results to non-LOD networks, such as email, Web, and protein networks, that have been reported in the literature. Our results show that the LOD cloud ex? hibits a broad variety of different network structures, consistent with the diversity found in other types of networks.
@article{60914ca9ba2543df8bf408a89914faa8,
title = "Understanding knowledge networks",
abstract = "The emergence of Linked Open Data (LOD) enables data on the Web to have a well defined structure and thereby to represent and in? terlink information from different sources and application areas. This web of data is a complex socially created network, where concepts and rela? tions are connected in intricate ways, collectively forming a network of knowledge. These data are published in a decentralized fashion and they stem from different sources, have different types of relationships, and use different terminologies, ontologies and meta models. While this so-called LOD cloud has become a very valuable resource, we know only very little about the general structural properties of the contained data, which im? pedes our ability to use and organize this resource in an efficient and accu? rate manner. The objective of this paper is to provide a basic understand? ing of LOD from the point of view of network structures. We analyze LOD networks with respect to fundamental network properties such as degree distribution and clustering. Using these metrics, we compare our results to non-LOD networks, such as email, Web, and protein networks, that have been reported in the literature. Our results show that the LOD cloud ex? hibits a broad variety of different network structures, consistent with the diversity found in other types of networks.",
keywords = "Knowledge network, Linked data, Network structure",
author = "Krishna Mangaladevi and Wouter Beek and Tobias Kuhn",
year = "2017",
volume = "1946",
pages = "38--49",
journal = "CEUR workshop proceedings",
issn = "1613-0073",
publisher = "CEUR Workshop Proceedings",
}
Downloads: 0
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