332 citations · 1.4k across the 68 of their papers we have counts for
71 papers
Recall Them All: Retrieval-Augmented Language Models for Long Object List Extraction from Long Documents
Sneha Singhania, Simon Razniewski, Gerhard Weikum
Methods for relation extraction from text mostly focus on high precision, at the cost of limited recall. High recall is crucial, though, to populate long lists of object entities t…
Faithful Temporal Question Answering over Heterogeneous Sources
Zhen Jia, Philipp Christmann, Gerhard Weikum
Temporal question answering (QA) involves time constraints, with phrases such as "... in 2019" or "... before COVID". In the former, time is an explicit condition, in the latter it…
Roadmap on Data-Centric Materials Science
Stefan Bauer, Peter Benner, Tristan Bereau +58
Science is and always has been based on data, but the terms "data-centric" and the "4th paradigm of" materials research indicate a radical change in how information is retrieved, h…
Cultural Commonsense Knowledge for Intercultural Dialogues
Tuan-Phong Nguyen, Simon Razniewski, Gerhard Weikum
Despite recent progress, large language models (LLMs) still face the challenge of appropriately reacting to the intricacies of social and cultural conventions. This paper presents…
Recommendations by Concise User Profiles from Review Text
Ghazaleh Haratinezhad Torbati, Anna Tigunova, Andrew Yates +1
Recommender systems perform well for popular items and users with ample interactions (likes, ratings etc.). This work addresses the difficult and underexplored case of users who ha…
Evaluating the Knowledge Base Completion Potential of GPT
Blerta Veseli, Simon Razniewski, Jan-Christoph Kalo +1
Structured knowledge bases (KBs) are an asset for search engines and other applications, but are inevitably incomplete. Language models (LMs) have been proposed for unsupervised kn…
Robust Training for Conversational Question Answering Models with Reinforced Reformulation Generation
Magdalena Kaiser, Rishiraj Saha Roy, Gerhard Weikum
Models for conversational question answering (ConvQA) over knowledge graphs (KGs) are usually trained and tested on benchmarks of gold QA pairs. This implies that training is limit…
Extracting Multi-valued Relations from Language Models
Sneha Singhania, Simon Razniewski, Gerhard Weikum
The widespread usage of latent language representations via pre-trained language models (LMs) suggests that they are a promising source of structured knowledge. However, existing m…
Knowledge Base Completion for Long-Tail Entities
Lihu Chen, Simon Razniewski, Gerhard Weikum
Despite their impressive scale, knowledge bases (KBs), such as Wikidata, still contain significant gaps. Language models (LMs) have been proposed as a source for filling these gaps…
CompMix: A Benchmark for Heterogeneous Question Answering
Philipp Christmann, Rishiraj Saha Roy, Gerhard Weikum
Fact-centric question answering (QA) often requires access to multiple, heterogeneous, information sources. By jointly considering several sources like a knowledge base (KB), a tex…
Explainable Conversational Question Answering over Heterogeneous Sources via Iterative Graph Neural Networks
Philipp Christmann, Rishiraj Saha Roy, Gerhard Weikum
In conversational question answering, users express their information needs through a series of utterances with incomplete context. Typical ConvQA methods rely on a single source (…
Evaluating Language Models for Knowledge Base Completion
Blerta Veseli, Sneha Singhania, Simon Razniewski +1
Structured knowledge bases (KBs) are a foundation of many intelligent applications, yet are notoriously incomplete. Language models (LMs) have recently been proposed for unsupervis…
Class Cardinality Comparison as a Fermi Problem
Shrestha Ghosh, Simon Razniewski, Gerhard Weikum
Questions on class cardinality comparisons are quite tricky to answer and come with its own challenges. They require some kind of reasoning since web documents and knowledge bases,…
Extracting Cultural Commonsense Knowledge at Scale
Tuan-Phong Nguyen, Simon Razniewski, Aparna Varde +1
Structured knowledge is important for many AI applications. Commonsense knowledge, which is crucial for robust human-centric AI, is covered by a small number of structured knowledg…
Answering Count Questions with Structured Answers from Text
Shrestha Ghosh, Simon Razniewski, Gerhard Weikum
In this work we address the challenging case of answering count queries in web search, such as ``number of songs by John Lennon''. Prior methods merely answer these with a single,…
UnCommonSense: Informative Negative Knowledge about Everyday Concepts
Hiba Arnaout, Simon Razniewski, Gerhard Weikum +1
Commonsense knowledge about everyday concepts is an important asset for AI applications, such as question answering and chatbots. Recently, we have seen an increasing interest in t…
Conversational Question Answering on Heterogeneous Sources
Philipp Christmann, Rishiraj Saha Roy, Gerhard Weikum
Conversational question answering (ConvQA) tackles sequential information needs where contexts in follow-up questions are left implicit. Current ConvQA systems operate over homogen…
Answering Count Queries with Explanatory Evidence
Shrestha Ghosh, Simon Razniewski, Gerhard Weikum
A challenging case in web search and question answering are count queries, such as \textit{"number of songs by John Lennon"}. Prior methods merely answer these with a single, and s…
Refined Commonsense Knowledge from Large-Scale Web Contents
Tuan-Phong Nguyen, Simon Razniewski, Julien Romero +1
Commonsense knowledge (CSK) about concepts and their properties is helpful for AI applications. Prior works, such as ConceptNet, have compiled large CSK collections. However, they…
Predicting Document Coverage for Relation Extraction
Sneha Singhania, Simon Razniewski, Gerhard Weikum
This paper presents a new task of predicting the coverage of a text document for relation extraction (RE): does the document contain many relational tuples for a given entity? Cove…
Language Models As or For Knowledge Bases
Simon Razniewski, Andrew Yates, Nora Kassner +1
Pre-trained language models (LMs) have recently gained attention for their potential as an alternative to (or proxy for) explicit knowledge bases (KBs). In this position paper, we…
Complex Temporal Question Answering on Knowledge Graphs
Zhen Jia, Soumajit Pramanik, Rishiraj Saha Roy +1
Question answering over knowledge graphs (KG-QA) is a vital topic in IR. Questions with temporal intent are a special class of practical importance, but have not received much atte…
You Get What You Chat: Using Conversations to Personalize Search-based Recommendations
Ghazaleh Haratinezhad Torbati, Andrew Yates, Gerhard Weikum
Prior work on personalized recommendations has focused on exploiting explicit signals from user-specific queries, clicks, likes, and ratings. This paper investigates tapping into a…
Personalized Entity Search by Sparse and Scrutable User Profiles
Ghazaleh Haratinezhad Torbati, Andrew Yates, Gerhard Weikum
Prior work on personalizing web search results has focused on considering query-and-click logs to capture users individual interests. For product search, extensive user histories a…