Named entity recognition (NER) is the task to identify mentions of rigid designators from text
belonging to predefined semantic types such as person, location, organization etc. NER …

Knowledge graph embedding (KGE) is an increasingly popular technique that aims to
represent entities and relations of knowledge graphs into low-dimensional semantic spaces …

Digital twins and artificial intelligence have shown promise for improving the robustness,
responsiveness, and productivity of industrial systems. However, traditional digital twin …

Abstract Knowledge graphs, representation of information as a semantic graph, have
caused wide concern in both industrial and academic world. Their property of providing …

This paper introduces Explicit Semantic Ranking (ESR), a new ranking technique that
leverages knowledge graph embedding. Analysis of the query log from our academic search …

Triage analysis is a fundamental stage in cyber operations in Security Operations Centers
(SOCs). The massive data sources generate great demands on cyber security analysts' …

In this survey, we provide an overview of the literature on knowledge graphs (KGs) in the
context of information retrieval (IR). Modern IR systems can benefit from information …

We introduce and address the problem of ad hoc table retrieval: answering a keyword query
with a ranked list of tables. This task is not only interesting on its own account, but is also …

This paper presents the Entity-Duet Neural Ranking Model (EDRM), which introduces
knowledge graphs to neural search systems. EDRM represents queries and documents by …

This paper presents a word-entity duet framework for utilizing knowledge bases in ad-hoc
retrieval. In this work, the query and documents are modeled by word-based representations …