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20212026
most citedLarge Language Models Assisting Ontology Evaluation

3 citations · 4 across the 6 of their papers we have counts for

collaborators

7 papers

cs.AI2026

OntoExtend: A Framework for Requirement-driven and Scalable Ontology Extension with LLMs

Anna Sofia Lippolis, Mohammad Javad Saeedizade, Stefan Schmid +5

Ontology extension refers to the process of enriching an existing ontology in response to emerging requirements, making it more complete. This task is a resource-intensive and erro…

cs.AI2026

When CQs Go Wrong: Challenges in CQ Verification with OE-Assist

Anna Sofia Lippolis, Mohammad Javad Saeedizade, Robin Keskisärkkä +3

Competency Questions (CQs) are the central component of CQ-verification, an established process in which an ontology is evaluated against a set of natural language questions to det…

cs.AI2025★ 3 cited

Large Language Models Assisting Ontology Evaluation

Anna Sofia Lippolis, Mohammad Javad Saeedizade, Robin Keskisärkkä +3

Ontology evaluation through functional requirements, such as testing via competency question (CQ) verification, is a well-established yet costly, labour-intensive, and error-prone…

cs.AI2025

Assessing the Capability of Large Language Models for Domain-Specific Ontology Generation

Anna Sofia Lippolis, Mohammad Javad Saeedizade, Robin Keskisarkka +3

Large Language Models (LLMs) have shown significant potential for ontology engineering. However, it is still unclear to what extent they are applicable to the task of domain-specif…

cs.AI2025★ 1 cited

Ontology Generation using Large Language Models

Anna Sofia Lippolis, Mohammad Javad Saeedizade, Robin Keskisärkkä +5

The ontology engineering process is complex, time-consuming, and error-prone, even for experienced ontology engineers. In this work, we investigate the potential of Large Language…

cs.AI2025

Semantic Web and Creative AI -- A Technical Report from ISWS 2023

Raia Abu Ahmad, Reham Alharbi, Roberto Barile +74

The International Semantic Web Research School (ISWS) is a week-long intensive program designed to immerse participants in the field. This document reports a collaborative effort p…

cs.CL2021

KGRefiner: Knowledge Graph Refinement for Improving Accuracy of Translational Link Prediction Methods

Mohammad Javad Saeedizade, Najmeh Torabian, Behrouz Minaei-Bidgoli

The Link Prediction is the task of predicting missing relations between entities of the knowledge graph. Recent work in link prediction has attempted to provide a model for increas…