Special issue on knowledge graphs and semantics in text analysis and retrieval
Knowledge graphs are an effective way to store semantics in a structured format that is easily used by computer systems. In the past few decades, work across different research communities led to scalable knowledge acquisition techniques for building large-scale knowledge graphs. The result is the emergence of large publicly available knowledge graphs (KGs) such as Wikidata, DBpedia, Freebase, and others. While knowledge graphs are designed to support a wide set of different applications, this special issue focuses on the use case of text retrieval and analysis.
Utilizing knowledge graphs for text analysis requires effective alignment techniques that associate segments of unstructured text with entries in the knowledge graph, for example using entity extraction and linking algorithms. A wide range of approaches that combine query-document representations and machine learning repeatedly demonstrate significant improvements for such tasks across diverse domains. The goal of this special issue is to summarize recent progress in research and practice in constructing, grounding, and utilizing knowledge graphs and similar semantic resources for text retrieval and analysis applications. The scope includes acquisition, alignment, and utilization of knowledge graphs and other semantic resources for the purpose of optimizing end-to-end performance of information retrieval systems.
For this special issue we selected six articles out of 23 submissions. Each article was reviewed by at least three reviewers and underwent at least one revision. More literature on how to effectively use of knowledge graphs in information retrieval can be found in the proceedings of the KG4IR Workshop series.