Transformers have achieved new heights modeling natural language as a sequence of text
tokens. However, in many real world scenarios, textual data inherently exhibits structures …

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

The DBpedia-entity collection has been used as a standard test collection for entity search in
recent years. We develop and release a new version of this test collection, DBpedia-Entity …

How much knowledge do pretrained language models hold? Recent research observed that
pretrained transformers are adept at modeling semantics but it is unclear to what degree …

The ever-growing usage of knowledge graphs (KGs) positions named entity disambiguation
(NED) at the heart of designing accurate KG-driven systems such as query answering …

Pre-trained language models such as BERT have been a key ingredient to achieve state-of-
the-art results on a variety of tasks in natural language processing and, more recently, also …

Machine understanding of user utterances in conversational systems is of utmost importance
for enabling engaging and meaningful conversations with users. Entity Linking (EL) is one of …

Entity cards are being used frequently in modern web search engines to offer a concise
overview of an entity directly on the results page. These cards are composed of various …

This paper presents a neural information retrieval pipeline that integrates cooperative
learning of query reformulation and neural retrieval models. Our pipeline first exploits an …

We introduce Nordlys, a toolkit for entity-oriented and semantic search. It provides
functionality for entity cataloging, entity retrieval, entity linking, and target type identification …