Entity alignment is the task of finding entities in two knowledge bases (KBs) that represent
the same real-world object. When facing KBs in different natural languages, conventional …

Learning knowledge graph (KG) embeddings has received increasing attention in recent
years. Most embedding models in literature interpret relations as linear or bilinear map** …

Tables contain valuable knowledge in a structured form. We employ neural language
modeling approaches to embed tabular data into vector spaces. Specifically, we consider …

Information extraction from semi-structured webpages provides valuable long-tailed facts for
augmenting knowledge graph. Relational Web tables are a critical component containing …

Since data is often stored in different sources, it needs to be integrated to gather a global
view that is required in order to create value and derive knowledge from it. A critical step in …

Here, we describe one of the first Web-scale hybrid Knowledge Graph (KG)-Large
Language Model (LLM), populated with the latest peer-reviewed medical knowledge on …

There are hundreds of millions of tables in Web pages that contain useful information for
many applications. Leveraging data within these tables is difficult because of the wide …

Predicting the performance of a retrieval method for a given query is a highly important and
challenging problem in information retrieval. Accurate Query Performance Prediction (QPP) …

Table retrieval is the task of extracting the most relevant tables to answer a user's query.
Table retrieval is an important task because many domains have tables that contain useful …

Existing approaches for table annotation with entities and types either capture the structure
of table using graphical models, or learn embeddings of table entries without accounting for …