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 …

Web contains a vast amount of data, which are accumulated, studied, and utilized by a huge
number of users on a daily basis. A substantial amount of data on the Web is available in an …

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 …

Nowadays, customer's demands for E-commerce are more diversified, which introduces
more complications to the product retrieval industry. Previous methods are either subject to …

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 …

Table search aims to answer a query with a ranked list of tables. Unfortunately, current test
corpora have focused mostly on needle-in-the-haystack tasks, where only a few tables are …

This paper presents a Kernel Entity Salience Model (KESM) that improves text
understanding and retrieval by better estimating entity salience (importance) in documents …