19 June 2024

Olaf Hartig at the division Database and Information Techniques (ADIT) has won the Best Poster Award at the ESWC 2024 conference, which is one of the two top conferences on knowledge graphs and semantic web technologies.

Image of the diploma

The poster paper entitled "Datatypes for Lists and Maps in RDF Literals" was honored with the best poster award at the 21st Extended Semantic Web Conference (ESWC) in June 2024. It is authored by Olaf Hartig, Gregory Todd Williams, Michael Schmidt, Ora Lassila, Carlos Manuel Lopez Enriquez, and Bryan Thompson and presents work that Olaf Hartig, division ADIT at the Department of Computer and Information Science, has done in his role as an Amazon Scholar with the Neptune graph database team at Amazon Web Services.

Poster description by Olaf Hartig

A knowledge graph is a form of graph-structured database intended to capture knowledge about entities and their relationships. Motivated by their suitability for powering artificial intelligence (AI) applications, for connecting data about customers or products, and for data integration, knowledge graphs have become an important topic for many of today's data-driven enterprises, including Swedish companies such as Scania, Ericsson, and IKEA.

The prevalent approach to represent and to query knowledge graphs is via the graph data model of the Resource Description Framework (RDF) and its query language called SPARQL.

The awarded poster paper introduces an approach to extend the SPARQL with built-in support for generic types of composite values (lists and maps in particular). The lack of features to capture and to query such composite values has been a considerable limitation for graph database users and knowledge graph applications. The proposed approach facilitates a broad range of use cases, including the integration of lists of all kinds into knowledge graphs (for example public transport timetables, series of measurement values), the direct annotation of entities in a knowledge graph with embedding vectors for machine learning and neuro-symbolic AI applications, as well as a more streamlined integration of knowledge graph technology into the broader data ecosystem within organisations. The ultimate goal is to bring the proposed approach to standardisation by the World Wide Web Consortium (W3C) as extensions of the RDF and SPARQL standards. To this end, the poster paper is accompanied by a complete formal specification of the proposed approach, a comprehensive test suite for implementers, and two open source implementations.

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