Personalized Knowledge Graph Summarization: From the Cloud to Your Pocket

Tara Safavi, Caleb Belth, Lukas Faber, Davide Mottin, Emmanuel Müller, Danai Koutra

ICDM 2019

Is it possible to find a summary of a knowledge graph which reflects the user preferences?


First summarization method for knowledge graphs workload- and space-aware.

In this paper:

  • We summarize a knowledge graph on a personalized basis inferring preferences from user-search or queries.
  • We formulate the problem as maximizing the likelihood of answering user queries given the summary and a space constraint.
  • We reduce the problem to that of a maximum coverage.
  • We propose an efficient greedy algorithm which is at the same time fast and with quality guarantees.
  • We validate our algorithms on large knowledge graphs and show that our method is effective and efficient.
  • We design effective methods that simulate real users based on preferences harvested manually.
  • We show that our method can outperform strong baseline scoring up to 19% in quality.