Building knowledge graphs in the real world | Panel

As the interest in, and hype around, Knowledge Graphs is growing, there is also a growing need for sharing experience and best practices around them. Let’s talk about definitions, best practices, hype, and reality.

What is a Knowledge Graph? How can I use a Knowledge Graph & how do i start building one? This panel is an opportunity to hear from industry experts using these technologies & approaches to discuss best practices, common pitfalls and where this space is headed next.

---

Katariina Kari

Research Engineer, Zalando Tech-Hub

Katariina Kari (née Nyberg) is a research engineer at the Zalando Tech-Hub in Helsinki. Katariina holds a Master in Science and Master in Music and is specialised in semantic web and guiding the art business to the digital age. At Zalando she is modelling the Fashion Knowledge Graph, a common vocabulary for fashion with which Zalando improves is customer experience. Katariina also consults art institutions to embrace the digital age in their business and see its opportunities.

Panos Alexopoulos

Head of Ontology, Textkernel BV

Panos Alexopoulos has been working at the intersection of data, semantics, language and software for years, and is leading a team at Textkernel developing a large cross-lingual Knowledge Graph for HR and Recruitment. Alexopoulos holds a PhD in Knowledge Engineering and Management from National Technical University of Athens, and has published 60 papers at international conferences, journals and books.

Sebastian Hellman

dbpedia.org

Sebastian is a senior member of the “Agile Knowledge Engineering and Semantic Web” AKSW research center, focusing on semantic technology research – often in combination with other areas such as machine learning, databases, and natural language processing.

Sebastian is head of the “Knowledge Integration and Language Technologies (KILT)” Competence Center at InfAI. He also is the executive director and board member of the non-profit DBpedia Association.

Sebastian is also a contributor to various open-source projects and communities such as DBpedia, NLP2RDF, DL-Learner and OWLG, and has been involved in numerous EU research projects.

Natasa Varitimou

Information Architect, Thomson Reuters

Natasa has been working as a Linked Data architect in banking, life science, consumer goods, oil & gas and EU projects. She believes data will eventually become the strongest asset in any organization, and works with Semantic Web technologies, which she finds great in describing the meaning of data, integrating data and making it interoperable and of high quality.

Natasa combines, links, expands and builds upon vocabularies from various sources to create flexible and lightweight information easily adaptable to different use cases. She queries these models with their data directly with SPARQL, guarantees data quality based on business rules, creates new information and defines services to bring together diverse data from different applications easily and with the semantics of data directly accessible.