24 citations · 40 across the 21 of their papers we have counts for
24 papers
Learning Perception to Action Rules through Reinforcement Learning for Theory-of-Mind Reasoning
Soyoung Oh, Nursulu Sagimbayeva, Nicholas Jennings +3
In Theory-of-Mind (ToM) reasoning, large language models may correctly infer the agent's mental state but fail to use that inference when predicting or judging the agent's subseque…
LCoT-GV: Graph Attention Networks for Verifying Long Reasoning Chains in Large Language Models
Bérénice Jaulmes, Mehwish Alam
Large Reasoning Models produce Long Chains-of-Thought (LCoTs) which involve breaking down the problem into smaller reasoning steps before reaching the conclusion. However, these st…
ReLTEx: Reliable LLM-based Taxonomy Expansion
Zeinab Ghamlouch, Mehwish Alam
Recent advances in Large Language Models (LLMs) have demonstrated strong capabilities in generating semantically relevant concepts and relations, making them promising tools for ta…
ENEIDE: A High Quality Silver Standard Dataset for Named Entity Recognition and Linking in Historical Italian
Cristian Santini, Sebastian Barzaghi, Paolo Sernani +3
This paper introduces ENEIDE (Extracting Named Entities from Italian Digital Editions), a silver standard dataset for Named Entity Recognition and Linking (NERL) in historical Ital…
Analysing Lightweight Large Language Models for Biomedical Named Entity Recognition on Diverse Ouput Formats
Pierre Epron, Adrien Coulet, Mehwish Alam
Despite their strong linguistic capabilities, Large Language Models (LLMs) are computationally demanding and require substantial resources for fine-tuning, which is unadapted to pr…
Enriching Taxonomies Using Large Language Models
Zeinab Ghamlouch, Mehwish Alam
Taxonomies play a vital role in structuring and categorizing information across domains. However, many existing taxonomies suffer from limited coverage and outdated or ambiguous no…
It's All About the Confidence: An Unsupervised Approach for Multilingual Historical Entity Linking using Large Language Models
Cristian Santini, Marieke Van Erp, Mehwish Alam
Despite the recent advancements in NLP with the advent of Large Language Models (LLMs), Entity Linking (EL) for historical texts remains challenging due to linguistic variation, no…
Thinking Before Constraining: A Unified Decoding Framework for Large Language Models
Ngoc Trinh Hung Nguyen, Alonso Silva, Laith Zumot +3
Natural generation allows Large Language Models (LLMs) to produce free-form responses with rich reasoning, yet the lack of structure makes outputs difficult to verify. Conversely,…
DELICATE: Diachronic Entity LInking using Classes And Temporal Evidence
Cristian Santini, Sebastian Barzaghi, Paolo Sernani +2
In spite of the remarkable advancements in the field of Natural Language Processing, the task of Entity Linking (EL) remains challenging in the field of humanities due to complex d…
T-REX: Table -- Refute or Entail eXplainer
Tim Luka Horstmann, Baptiste Geisenberger, Mehwish Alam
Verifying textual claims against structured tabular data is a critical yet challenging task in Natural Language Processing with broad real-world impact. While recent advances in La…
Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods
Xiaohua Lu, Liubov Tupikina, Mehwish Alam
Real-world knowledge can take various forms, including structured, semi-structured, and unstructured data. Among these, Knowledge Graphs (KGs) are structured representations that i…
Markov Process-Based Graph Convolutional Networks for Entity Classification in Knowledge Graphs
Johannes Mäkelburg, Yiwen Peng, Mehwish Alam +2
Despite the vast amount of information encoded in Knowledge Graphs (KGs), information about the class affiliation of entities remains often incomplete. Graph Convolutional Networks…
Neurosymbolic Methods for Dynamic Knowledge Graphs
Mehwish Alam, Genet Asefa Gesese, Pierre-Henri Paris
Knowledge graphs (KGs) have recently been used for many tools and applications, making them rich resources in structured format. However, in the real world, KGs grow due to the add…
Refining Wikidata Taxonomy using Large Language Models
Yiwen Peng, Thomas Bonald, Mehwish Alam
Due to its collaborative nature, Wikidata is known to have a complex taxonomy, with recurrent issues like the ambiguity between instances and classes, the inaccuracy of some taxono…
Neurosymbolic Methods for Rule Mining
Agnieszka Lawrynowicz, Luis Galarraga, Mehwish Alam +3
In this chapter, we address the problem of rule mining, beginning with essential background information, including measures of rule quality. We then explore various rule mining met…
NeMig -- A Bilingual News Collection and Knowledge Graph about Migration
Andreea Iana, Mehwish Alam, Alexander Grote +5
News recommendation plays a critical role in shaping the public's worldviews through the way in which it filters and disseminates information about different topics. Given the cruc…
Towards Semantically Enriched Embeddings for Knowledge Graph Completion
Mehwish Alam, Frank van Harmelen, Maribel Acosta
Embedding based Knowledge Graph (KG) Completion has gained much attention over the past few years. Most of the current algorithms consider a KG as a multidirectional labeled graph…
RAILD: Towards Leveraging Relation Features for Inductive Link Prediction In Knowledge Graphs
Genet Asefa Gesese, Harald Sack, Mehwish Alam
Due to the open world assumption, Knowledge Graphs (KGs) are never complete. In order to address this issue, various Link Prediction (LP) methods are proposed so far. Some of these…
Entity Type Prediction Leveraging Graph Walks and Entity Descriptions
Russa Biswas, Jan Portisch, Heiko Paulheim +2
The entity type information in Knowledge Graphs (KGs) such as DBpedia, Freebase, etc. is often incomplete due to automated generation or human curation. Entity typing is the task o…
Towards Analyzing the Bias of News Recommender Systems Using Sentiment and Stance Detection
Mehwish Alam, Andreea Iana, Alexander Grote +3
News recommender systems are used by online news providers to alleviate information overload and to provide personalized content to users. However, algorithmic news curation has be…
A Knowledge Graph Embeddings based Approach for Author Name Disambiguation using Literals
Cristian Santini, Genet Asefa Gesese, Silvio Peroni +3
Scholarly data is growing continuously containing information about the articles from a plethora of venues including conferences, journals, etc. Many initiatives have been taken to…
On the Impact of Temporal Representations on Metaphor Detection
Giorgio Ottolina, Matteo Palmonari, Mehwish Alam +1
State-of-the-art approaches for metaphor detection compare their literal - or core - meaning and their contextual meaning using metaphor classifiers based on neural networks. Howev…
MigrationsKB: A Knowledge Base of Public Attitudes towards Migrations and their Driving Factors
Yiyi Chen, Harald Sack, Mehwish Alam
With the increasing trend in the topic of migration in Europe, the public is now more engaged in expressing their opinions through various platforms such as Twitter. Understanding…
Steps towards a Dislocation Ontology for Crystalline Materials
Ahmad Zainul Ihsan, Danilo Dessì, Mehwish Alam +2
The field of Materials Science is concerned with, e.g., properties and performance of materials. An important class of materials are crystalline materials that usually contain ``di…