Graph Structured Data for real world scenarios

Published on:
Wednesday, 25th November, 2020
Guy Daniels, Director of Content, TelecomTV
Lindsay Frost, Chairman ISG CIM, (NEC Labs)
Bhushan Kotnis, AI Research Lead, (NEC Labs)

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How answering complex queries in Graph Structured Data makes Big Data management simpler. Relational learning in graphs has focused on answering simple link prediction queries. That is, exploiting similar examples for predicting connections. In this talk we address predicting answers to conjunctive queries. For example, querying for patients with conjunctive constrains such as a specific blood type, symptoms and pre-existing conditions from a hospital knowledge base given that some of this data is missing. We present a method that embeds conjunctive queries with models that capture interactions among all the elements of a query graph. The challenge is to obtain reliable data, with minimum errors which allow for evaluating varieties of queries and this is where standards really help.


Guy Daniels

Director of Content, TelecomTV


Lindsay Frost

Chairman ISG CIM, (NEC Labs)


Lindsay Frost is Chief Standardization Engineer at NEC Laboratories Europe GmbH, chairman of the ETSI ISG CIM group for Context Information Management, chairman of ETSI OCG AI group, Board member of ETSI, and ETSI lead delegate to the three CEN/CENELEC/ETSI groups SF-SSCC (Sector Forum for Smart and Sustainable Cities and Communities), Focus Group on AI, and CG-SMa (Coordination Group for Smart Manufacturing) as well as external Advisor to the 2023 Project for coordinating standardization (in Smart Cities and AI). His previous roles included: board member of HGI, co-chairman of HGI Smart Home group, Chair of ETSI TISPAN WG5 Home Networks, Chair of WFA Mobile Convergence group. Lindsay Frost has a Ph.D in experimental physics, a strong interest in medical engineering and a passion for promoting better use of standards for IoT, AI, Smart Cities and communities.

Bhushan Kotnis

AI Research Lead, (NEC Labs)


Dr. Bhushan Kotnis is a research scientist at NEC Laboratories Europe, Heidelberg. His work spans interdisciplinary fields including network science, Knowledge Graphs and Natural Language Processing. He pursued his PhD at the Indian Institute of Science, Bangalore, India focused on graph dynamics and optimization problems in social networks. Subsequently, he moved to Heidelberg University pursuing research at the intersection of Knowledge Graphs and Natural Language Processing. His current research at NEC Laboratories Europe, is focused on relational learning in Knowledge Graphs, information extraction, Knowledge Graph construction from text and conversational dialogue systems.

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