110 citations · 245 across the 14 of their papers we have counts for
16 papers
Quotegraph: A Social Network Extracted from Millions of News Quotations
Marko Čuljak, Robert West, Andreas Spitz +1
We introduce Quotegraph, a novel large-scale social network derived from speaker-attributed quotations in English news articles published between 2008 and 2020. Quotegraph consists…
Web2Wiki: Characterizing Wikipedia Linking Across the Web
Veniamin Veselovsky, Tiziano Piccardi, Ashton Anderson +2
Wikipedia is one of the most visited websites globally, yet its role beyond its own platform remains largely unexplored. In this paper, we present the first large-scale analysis of…
Are Retrials All You Need? Enhancing Large Language Model Reasoning Without Verbalized Feedback
Nearchos Potamitis, Akhil Arora
Recent advancements in large language models (LLMs) have catalyzed the development of general-purpose autonomous agents, demonstrating remarkable performance in complex reasoning t…
Entity Insertion in Multilingual Linked Corpora: The Case of Wikipedia
Tomás Feith, Akhil Arora, Martin Gerlach +2
Links are a fundamental part of information networks, turning isolated pieces of knowledge into a network of information that is much richer than the sum of its parts. However, add…
Fleet of Agents: Coordinated Problem Solving with Large Language Models
Lars Klein, Nearchos Potamitis, Roland Aydin +3
While numerous frameworks have been developed to enhance the reasoning abilities of large language models (LLMs), there is a scarcity of methods that effectively balance the trade-…
Orphan Articles: The Dark Matter of Wikipedia
Akhil Arora, Robert West, Martin Gerlach
With 60M articles in more than 300 language versions, Wikipedia is the largest platform for open and freely accessible knowledge. While the available content has been growing conti…
Generating Faithful Synthetic Data with Large Language Models: A Case Study in Computational Social Science
Veniamin Veselovsky, Manoel Horta Ribeiro, Akhil Arora +3
Large Language Models (LLMs) have democratized synthetic data generation, which in turn has the potential to simplify and broaden a wide gamut of NLP tasks. Here, we tackle a perva…
Quote Erat Demonstrandum: A Web Interface for Exploring the Quotebank Corpus
Vuk Vuković, Akhil Arora, Huan-Cheng Chang +2
The use of attributed quotes is the most direct and least filtered pathway of information propagation in news. Consequently, quotes play a central role in the conception, reception…
Strong Heuristics for Named Entity Linking
Marko Čuljak, Andreas Spitz, Robert West +1
Named entity linking (NEL) in news is a challenging endeavour due to the frequency of unseen and emerging entities, which necessitates the use of unsupervised or zero-shot methods.…
Efficient Entity Candidate Generation for Low-Resource Languages
Alberto García-Durán, Akhil Arora, Robert West
Candidate generation is a crucial module in entity linking. It also plays a key role in multiple NLP tasks that have been proven to beneficially leverage knowledge bases. Neverthel…
Wikipedia Reader Navigation: When Synthetic Data Is Enough
Akhil Arora, Martin Gerlach, Tiziano Piccardi +2
Every day millions of people read Wikipedia. When navigating the vast space of available topics using hyperlinks, readers describe trajectories on the article network. Understandin…
A Large-Scale Characterization of How Readers Browse Wikipedia
Tiziano Piccardi, Martin Gerlach, Akhil Arora +1
Despite the importance and pervasiveness of Wikipedia as one of the largest platforms for open knowledge, surprisingly little is known about how people navigate its content when se…
Low-Rank Subspaces for Unsupervised Entity Linking
Akhil Arora, Alberto García-Durán, Robert West
Entity linking is an important problem with many applications. Most previous solutions were designed for settings where annotated training data is available, which is, however, not…
HD-Index: Pushing the Scalability-Accuracy Boundary for Approximate kNN Search in High-Dimensional Spaces
Akhil Arora, Sakshi Sinha, Piyush Kumar +1
Nearest neighbor searching of large databases in high-dimensional spaces is inherently difficult due to the curse of dimensionality. A flavor of approximation is, therefore, necess…
RAQ: Relationship-Aware Graph Querying in Large Networks
Jithin Vachery, Akhil Arora, Sayan Ranu +1
The phenomenal growth of graph data from a wide variety of real-world applications has rendered graph querying to be a problem of paramount importance. Traditional techniques use s…
Holistic Influence Maximization: Combining Scalability and Efficiency with Opinion-Aware Models
Sainyam Galhotra, Akhil Arora, Shourya Roy
The steady growth of graph data from social networks has resulted in wide-spread research in finding solutions to the influence maximization problem. In this paper, we propose a ho…