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Example Driven Analytics of Open Knowledge Graphs

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 a... ver más

14/09/2021

AAU

207K€

Presupuesto del proyecto: 207K€

Líder del proyecto

AALBORG UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2021-09-14

MSCA-IF-2018: Individual Fellowships

I+D

Cerrada hace 6 años

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No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

AAU

207.31K€ | Lider

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Duración del proyecto: 29 meses Fecha Inicio: 2019-04-11
Fecha Fin: 2021-09-14

Líder del proyecto

AALBORG UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 207K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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.

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