The Knowledge Driven Data Exploitation (K-Drive) project is a EU funded Marie Curie project, which aims to study novel technologies on the construction, understanding and exploitation of Knowledge Graph, which is has become popular in knowledge representation and knowledge management applications widely across search engine, biomedical, media and industrial domains. In 2012, Google popularised the term Knowledge Graph (KG) with a blog post titled ‘Introducing the Knowledge Graph: things, not strings', while simultaneously applying the approach in their core business, fundamentally in the web search area. Inspired by the successful story of Google, knowledge graphs are gaining momentum in the World Wide Web arena. Recent years have witnessed increasing industrial take-ups by other internet giants, including Facebook's Open Graph and Microsoft's Satori.


The first objective of the K-Drive project is the identification of novel techniques to understand knowledge graphs, such as query generation techniques to facilitate experts to identify key dimensions within semantic data and to recommend users with related queries. Key results in this strand, in addition to query generation, include knowledge graph simplification, system categorisation of redundancies in knowledge graphs and knowledge graph compression.


The second objective of the K-Drive project is to develop novel reasoning and querying techniques on knowledge graph exploitation, including knowledge graphs with addition streams and deletion streams. Key results in this strand include stream reasoning, stream querying with consistent knowledge discovery over incomplete knowledge graphs, instance retrieval with negative atomic concepts, question answering, handling vagueness and uncertainties in knowledge graphs.


The third objective of the K-Drive project is to develop novel hypothesis and guidance techniques on knowledge graph exploitation. Key results in this strand include TBox learning over incomplete knowledge graphs, vague knowledge extraction from miniposts, approximate justifications, approximate deduction, reasoner performance and energy consumption predictions, troubleshooting and optimising named entity resolutions.


Project Homepage: Knowledge Driven Data Exploitation (K-Drive)

Contact: Jeff Z. Pan



  • Jhonatan Garcia
  • Chris Mellish
  • Samuel Okure
  • Jeff Z. Pan
  • Artemis Parvizi
  • Yuan Ren
  • Advaith Siddharthan
  • Andrew Walker
  • Honghan Wu
  • Adam Wyner


External Collaborators

  • Panos Alexopoulos, iSOCO
  • Jaime Biosca, Expert System Iberia
  • Eliana Bombieri, Expert System
  • Alessandra Coletti, IBM
  • Anna Cuccovillo,  Expert System
  • Ronald Denaux, Expert System Iberia
  • Alessandro Faraotti, IBM
  • José Manuel Gómez-Pérez, Expert System Iberia
  • Jing Mei, IBM
  • Marco Monti, IBM
  • Yue Pan, IBM
  • Fernanda Perego, IBM
  • Carlos Ruizi, iSOCO
  • Xiaofei Teng, IBM
  • Emanuela Valle, IBM
  • Guido Vetere, IBM
  • Veronica Villa, Expert System
  • Boris Villazón-Terrazas, iSOCO
  • Guotong Xie, IBM
  • Yuting Zhao, IBM
  • Man Zhu, Southeast University