Phillip Schneider


A Decade of Knowledge Graphs in Natural Language Processing: A Survey
Phillip Schneider | Tim Schopf | Juraj Vladika | Mikhail Galkin | Elena Simperl | Florian Matthes
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

In pace with developments in the research field of artificial intelligence, knowledge graphs (KGs) have attracted a surge of interest from both academia and industry. As a representation of semantic relations between entities, KGs have proven to be particularly relevant for natural language processing (NLP), experiencing a rapid spread and wide adoption within recent years. Given the increasing amount of research work in this area, several KG-related approaches have been surveyed in the NLP research community. However, a comprehensive study that categorizes established topics and reviews the maturity of individual research streams remains absent to this day. Contributing to closing this gap, we systematically analyzed 507 papers from the literature on KGs in NLP. Our survey encompasses a multifaceted review of tasks, research types, and contributions. As a result, we present a structured overview of the research landscape, provide a taxonomy of tasks, summarize our findings, and highlight directions for future work.

Semantic Similarity-Based Clustering of Findings From Security Testing Tools
Phillip Schneider | Markus Voggenreiter | Abdullah Gulraiz | Florian Matthes
Proceedings of the 5th International Conference on Natural Language and Speech Processing (ICNLSP 2022)