Special issue on knowledge graphs and semantics in text analysis and retrieval

Knowledge graphs are an efective way to store semantics in a structured format that is easily used by computer systems. In the past few decades, work across diferent 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 DBpedia (Lehmann et al. 2014), Freebase (Bollacker et al. 2008), and others. While knowledge graphs are designed to support a wide set of diferent applications, this special issue focuses on the use case of text retrieval and analysis. Utilizing knowledge graphs for text analysis requires efective alignment techniques that associate segments of unstructured text with entries in the knowledge graph, for example using entity extraction and linking algorithms (Carmel et al. 2014; Mendes et al. 2011; Blanco et al. 2015). A wide range of approaches that combine query-document representations and  machine learning repeatedly demonstrate signifcant improvements for such tasks across diverse domains (Dalton et al. 2014; Liu and Fang 2015; Hasibi et al. 2015; Xiong and Callan 2015; Raviv et al. 2016; Ensan and Bagheri 2017; Xiong et al. 2017). 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.