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Example-Driven Analytics of Open Knowledge Graphs

Project description

Connecting example-based queries and business intelligence methods

The Linked Open Data (LOD) cloud is a knowledge graph that shows publicly available linked datasets. As it contains a rich amount of information, business analytics and information extraction technologies are needed to obtain valuable insights. However, using the specialised query language of gateways is a key challenge for non-expert users. This, in turn, presents a hurdle for the successful exploitation of LOD. Supporting LOD analytics, the EU-funded EDAO project will develop an innovative data exploration system that enables users to extract insights within complex and unfamiliar datasets. The project aims to employ business intelligence operators enabled by Exemplar Query paradigm for Exploratory Online Analytical Processing and will examine a new Example-Driven Exploration system.

Objective

Linked Open Data (LOD) is a standard methodology especially adopted to implement Knowledge Graphs, i.e. networks of facts where entities are connected by predicates describing relationships among them (via RDF triples). LOD are adopted in many domains, and an enormous set of information is currently shared by the private and the public sector in this form (e.g. on the EU Open Data Portal). Therefore, the LOD cloud contains a very rich corpora of information that requires dedicated business analytics and information extractions technologies for the extraction of valuable insights. Yet, to access this data and perform such analysis, the typical gateway are specialized query languages (e.g. SPARQL) that are usually challenging to use to non-expert users. This constitutes a major impediment in their successful exploitation. To support advanced LOD analytics we propose a novel data exploration system which allows users to extract insights within complex and unfamiliar datasets. We plan to implement dedicated Business Intelligence (BI) operators enabled by the Exemplar Query paradigm for Exploratory Online Analytical Processing (OLAP). Example-based methods have proven to be extremely valuable since they avoid complex query languages by using examples to represent the required information. Yet, they have never been studied in the OLAP/BI context. Therefore, we propose to study a new Example-Driven Exploration system to bridge the gap between example-based queries and BI methods. The researcher has co-authored the first paper on Exemplar Queries for graphs. Moreover, the supervisor, prof. Torben Bach Pedersen at Aalborg University, is an expert on BI/OLAP methods for web and semi-structured data. The host of the secondment, prof. Ioana Manolescu, at INRIA Saclay, is expert in advanced RDF analytics operators. These high-profile collaborations will ensure both the successful outcome of the project as well as a platform for the development of the researcher’s career.

Fields of science (EuroSciVoc)

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Programme(s)

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Topic(s)

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Funding Scheme

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MSCA-IF-EF-ST - Standard EF

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Call for proposal

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(opens in new window) H2020-MSCA-IF-2018

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Coordinator

AALBORG UNIVERSITET
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 207 312,00
Address
FREDRIK BAJERS VEJ 7K
9220 AALBORG
Denmark

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Region
Danmark Nordjylland Nordjylland
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 207 312,00
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