Generating Possible Interpretations for Statistics from Linked Open Data

Paulheim, Heiko

doi:10.1007/978-3-642-30284-8_44

Cited by 44 publications

(35 citation statements)

References 14 publications

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“…The interpretation turns out to be an intensive and time-consuming process, where part of knowledge can remain unrevealed or unexplained. Explain-a-LOD [106] is one of the first approaches in the literature for automatically generating hypothesis for explaining statistics by using LOD. The tool uses FeGeLOD (described in Section 7.1) for enhancing statistical datasets with background information from DBpedia, and uses correlation analysis and rule learning for producing hypothesis which are presented to the user.…”

Section: Discussionmentioning

confidence: 99%

Semantic Web in Data Mining and Knowledge Discovery: A Comprehensive Survey

Ristoski

Paulheim

2016

SSRN Journal

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a b s t r a c tData Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in that field are knowledge intensive and can often benefit from using additional knowledge from various sources. Therefore, many approaches have been proposed in this area that combine Semantic Web data with the data mining and knowledge discovery process. This survey article gives a comprehensive overview of those approaches in different stages of the knowledge discovery process. As an example, we show how Linked Open Data can be used at various stages for building content-based recommender systems. The survey shows that, while there are numerous interesting research works performed, the full potential of the Semantic Web and Linked Open Data for data mining and KDD is still to be unlocked.

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Section: Discussionmentioning

confidence: 99%

Semantic Web in Data Mining and Knowledge Discovery: A Comprehensive Survey

Ristoski

Paulheim

2016

SSRN Journal

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“…publishing statistical data and interpretation of statistics [5], improving tourism experience [6], pharmaceutical R&D data sharing [7], crowdsourcing in emergency management [4], etc. A few years ago, our analysis of the adoption of Semantic Web technologies by enterprises [2] has shown that companies benefit from features that improve data sharing and re-use (57 %), improve searching (57 %), allow incremental modelling (26 %), explicit content relation (24 %), identifying new relationships (17 %), dynamic content generation (14 %), personalization (10 %), open modeling (12 %), rapid response to change (10 %), reducing time to market (5 %), and automation (5 %).…”

Section: Towards a Broader Adoptionmentioning

confidence: 99%

Supporting the Linked Data Life Cycle Using an Integrated Tool Stack

Nuffelen¹,

Janev

Martin

et al. 2014

Lecture Notes in Computer Science

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Abstract. The core of a Linked Data application is the processing of the knowledge expressed as Linked Data. Therefore the creation, management, curation and publication of Linked Data are critical aspects for an application's success. For all of these aspects the LOD2 project provides components. These components have been collected and placed under one distribution umbrella: the LOD2 stack. In this chapter we will introduce this component stack. We will show how to get access; which component covers which aspect of the Linked Data life cycle and how using the stack eases the access to Linked Data management tools. Furthermore we will elaborate how the stack can be used to support a knowledge domain. The illustrated domain is statistical data.

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“…In addition, Paulheim [18] employed datasets that are published on the Linked Data Web in order to enrich statistical data with attributes e.g. from DBpedia.…”

Section: Related Workmentioning

confidence: 99%

Linked Open Government Data Analytics

Kalampokis

Tambouris

Tarabanis

2013

Lecture Notes in Computer Science

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Abstract. Although the recently launched Open Government Data (OGD) movement promised to provide a number of benefits, recent studies have shown that its full potential has not yet realized. The difficulty in exploiting open data seems surprising if we consider the huge importance data have in modern societies. In this paper we claim that the real value of OGD will unveil from performing data analytics on top of combined statistical datasets that were previously closed in disparate sources and can now be linked to provide unexpected and unexplored insights. To support this claim, we describe the linked OGD analytics concept along with its technical requirements and demonstrate its end-user value employing a use case related to UK general elections. The use case revealed that there is a significant relationship between the probability one of the two main political parties (i.e. Labour Party and Conservative Party) to win in a UK constituency and the unemployment rate in the same constituency.

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Generating Possible Interpretations for Statistics from Linked Open Data

Cited by 44 publications

References 14 publications

Semantic Web in Data Mining and Knowledge Discovery: A Comprehensive Survey

Semantic Web in Data Mining and Knowledge Discovery: A Comprehensive Survey

Supporting the Linked Data Life Cycle Using an Integrated Tool Stack

Linked Open Government Data Analytics

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