332 citations · 1.4k across the 68 of their papers we have counts for
12 papers · 1 filter
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…
Detecting and Mitigating Test-time Failure Risks via Model-agnostic Uncertainty Learning
Preethi Lahoti, Krishna P. Gummadi, Gerhard Weikum
Reliably predicting potential failure risks of machine learning (ML) systems when deployed with production data is a crucial aspect of trustworthy AI. This paper introduces Risk Ad…
UNIQORN: Unified Question Answering over RDF Knowledge Graphs and Natural Language Text
Soumajit Pramanik, Jesujoba Alabi, Rishiraj Saha Roy +1
Question answering over RDF data like knowledge graphs has been greatly advanced, with a number of good systems providing crisp answers for natural language questions or telegraphi…
Beyond NED: Fast and Effective Search Space Reduction for Complex Question Answering over Knowledge Bases
Philipp Christmann, Rishiraj Saha Roy, Gerhard Weikum
Answering complex questions over knowledge bases (KB-QA) faces huge input data with billions of facts, involving millions of entities and thousands of predicates. For efficiency, Q…
Inside ASCENT: Exploring a Deep Commonsense Knowledge Base and its Usage in Question Answering
Tuan-Phong Nguyen, Simon Razniewski, Gerhard Weikum
ASCENT is a fully automated methodology for extracting and consolidating commonsense assertions from web contents (Nguyen et al., WWW 2021). It advances traditional triple-based co…
Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge Graphs
Magdalena Kaiser, Rishiraj Saha Roy, Gerhard Weikum
The rise of personal assistants has made conversational question answering (ConvQA) a very popular mechanism for user-system interaction. State-of-the-art methods for ConvQA over k…
ELIXIR: Learning from User Feedback on Explanations to Improve Recommender Models
Azin Ghazimatin, Soumajit Pramanik, Rishiraj Saha Roy +1
System-provided explanations for recommendations are an important component towards transparent and trustworthy AI. In state-of-the-art research, this is a one-way signal, though,…