Junior Conference on DataScience and Engeneering 2022 (JDSE)

• Pierre Monnin

Title: Knowledge graphs: construction, matching, and mining

Abstract: In the Web of data, an increasing number of knowledge graphs (KGs) are concurrently published, edited, and accessed by human and software agents. Their wide adoption makes the three tasks of construction, matching, and mining key. The construction of KGs can rely on knowledge extraction from various types of data (e.g., text, tables). Matching consists in identifying equivalent, more specific, or somewhat similar units within and across KGs. This task is crucial since concurrent publication and edition may result in coexisting and complementary KGs. However, this task is challenging because of the inherent heterogeneity of KGs, e.g., in terms of granularities, vocabularies, and completeness. Mining aims at discovering new and useful knowledge units from KGs. Such a process faces scalability issues due to the increasing size and the combinatorial nature of KGs. In the first part of this talk, I will present my Ph.D. work on knowledge graph construction, matching, and mining with applications in pharmacogenomics. In the second part of this talk, I will focus on knowledge extraction from tabular data, namely Semantic Table Interpretation, with the DAGOBAH project. Throughout all my presentation, I will highlight the advantages of domain knowledge in the form of ontologies associated with knowledge graphs. Indeed, when considered, such domain knowledge supports the construction, matching, and mining of KGs. Finally, I will discuss the perspectives of my work and future projects.

Bio:
Pierre Monnin is a researcher with the Orange Innovation/Data-AI entity of Orange. He holds a Ph.D. from the University of Lorraine, where he worked on extracting, comparing, and mining knowledge in the biomedical domain of pharmacogenomics in the context of the ANR PractiKPharma project. His Ph.D. work was awarded the “2022 best thesis award” from the French association EGC (Extraction et Gestion des Connaissances – Knowledge Extraction and Management). Pierre’s research broadly focuses on knowledge extraction, matching, and mining to build and refine knowledge graphs. He is particularly interested in uncertain knowledge management, graph embedding techniques for knowledge graphs, and hybrid approaches that combine symbolic and numeric methods. He is Proceedings & Metadata co-chair of ISWC 2022.