DSG Seminar Series • Class Expression Learning with Multiple Representations

Speaker: Axel Ngonga, Paderborn University

Location: DC 1304

Abstract:

RDF knowledge bases are now first-class citizens of the Web with over 100 billion RDF assertions in the 2022 WebDataCommons crawl. Developing explainable machine learning approaches tailored towards this data is hence a task of increasing importance. In this talk, we focus on class expression learning (also called concept learning) on large RDF knowledge graphs. We begin by presenting approaches based on refinement operators, the most common family of solutions for concept learning. We then continue by presenting the most important performance bottlenecks of concept learning based on refinements. We then present recent works that address each of those bottlenecks using a dedicated representation (e.g., neural, symbolic). We conclude by presenting some open challenges in concept learning and related areas.

Bio: Axel Ngonga is a professor at Paderborn University, where he heads the Data Science Group. He studied Computer Science in Leipzig. In his PhD thesis, he developed knowledge-poor methods for the extraction of taxonomies from large text corpora. After completing his PhD, he wrote a Habilitation on link discovery with a focus on machine learning and runtime optimization. In his current research, he focuses on data-driven methods to improve the lifecycle of knowledge graphs. These include techniques for the extraction of knowledge graphs, the verification of their veracity, their integration and fusion, their use in machine learning, and their exploitation in user-facing applications such as question answering systems and chatbots. He is the grateful recipient of over 25 international research prizes, including a Next Einstein Fellowship and 6 best research paper awards.