arXiv subjects

Nadeen Fathallah

Publications and source records attributed to Nadeen Fathallah.

4 recordsLinked to original sources

arXiv2025

AccessGuru: Leveraging LLMs to Detect and Correct Web Accessibility Violations in HTML Code

The vast majority of Web pages fail to comply with established Web accessibility guidelines, excluding a range of users with diverse abilities from interacting with their content. Making Web pages accessible to all users requires dedicated expertise and additional manual efforts from Web page providers. To lower their efforts and promote inclusiveness, we aim to automatically detect and correct Web accessibility violations in HTML code. While previous work has made progress in detecting certain types of accessibility violations, the problem of automatically detecting and correcting accessibility violations remains an open challenge that we address. We introduce a novel taxonomy classifying Web accessibility violations into three key categories - Syntactic, Semantic, and Layout. This taxonomy provides a structured foundation for developing our detection and correction method and redefining evaluation metrics. We propose a novel method, AccessGuru, which combines existing accessibility testing tools and Large Language Models (LLMs) to detect violations and applies taxonomy-driven prompting strategies to correct all three categories. To evaluate these capabilities, we develop a benchmark of real-world Web accessibility violations. Our benchmark quantifies syntactic and layout compliance and judges semantic accuracy through comparative analysis with human expert corrections. Evaluation against our benchmark shows that AccessGuru achieves up to 84% average violation score decrease, significantly outperforming prior methods that achieve at most 50%.

cs.SE↗

arXiv2025

Retrieval-Augmented Generation of Ontologies from Relational Databases

Deriving OWL ontologies from relational database schemas supports semantic interoperability and downstream tasks such as knowledge graph population, ontology-based data access, graph-based learning, and automated reasoning. Existing approaches either require substantial expert effort or produce shallow ontologies that reflect the logical schema structure but fail to fully capture domain semantics. We present RIGOR (Retrieval-augmented Iterative Generation of RDB Ontologies), an LLM-driven pipeline that converts relational schemas into semantically rich OWL2DL ontologies with minimal human intervention. For each relational table, RIGOR generates a direct mapping to guarantee schema coverage, then enriches it via retrieval from three sources: relational schema context and documentation, external domain ontologies, and an ontology that grows incrementally as each validated fragment is integrated. A Gen-LLM produces provenance-annotated ontology fragments (delta ontologies), which are validated and, when needed, corrected by an independent Judge-LLM before integration. Guided by foreign-key constraints, the process iterates over relational tables until the full schema is covered. Experiments across three databases spanning two domains show that RIGOR consistently outperforms baseline methods across standard quality metrics while requiring no human oversight.

cs.DB↗

arXiv2024

LLMs4Life: Large Language Models for Ontology Learning in Life Sciences

Ontology learning in complex domains, such as life sciences, poses significant challenges for current Large Language Models (LLMs). Existing LLMs struggle to generate ontologies with multiple hierarchical levels, rich interconnections, and comprehensive class coverage due to constraints on the number of tokens they can generate and inadequate domain adaptation. To address these issues, we extend the NeOn-GPT pipeline for ontology learning using LLMs with advanced prompt engineering techniques and ontology reuse to enhance the generated ontologies' domain-specific reasoning and structural depth. Our work evaluates the capabilities of LLMs in ontology learning in the context of highly specialized and complex domains such as life science domains. To assess the logical consistency, completeness, and scalability of the generated ontologies, we use the AquaDiva ontology developed and used in the collaborative research center AquaDiva as a case study. Our evaluation shows the viability of LLMs for ontology learning in specialized domains, providing solutions to longstanding limitations in model performance and scalability.

cs.AI↗

arXiv2024

Empowering the Deaf and Hard of Hearing Community: Enhancing Video Captions Using Large Language Models

In today's digital age, video content is prevalent, serving as a primary source of information, education, and entertainment. However, the Deaf and Hard of Hearing (DHH) community often faces significant challenges in accessing video content due to the inadequacy of automatic speech recognition (ASR) systems in providing accurate and reliable captions. This paper addresses the urgent need to improve video caption quality by leveraging Large Language Models (LLMs). We present a comprehensive study that explores the integration of LLMs to enhance the accuracy and context-awareness of captions generated by ASR systems. Our methodology involves a novel pipeline that corrects ASR-generated captions using advanced LLMs. It explicitly focuses on models like GPT-3.5 and Llama2-13B due to their robust performance in language comprehension and generation tasks. We introduce a dataset representative of real-world challenges the DHH community faces to evaluate our proposed pipeline. Our results indicate that LLM-enhanced captions significantly improve accuracy, as evidenced by a notably lower Word Error Rate (WER) achieved by ChatGPT-3.5 (WER: 9.75%) compared to the original ASR captions (WER: 23.07%), ChatGPT-3.5 shows an approximate 57.72% improvement in WER compared to the original ASR captions.

cs.AI↗