Large Language Models for Anomaly Detection in Semantic Web Data

Project description

The semantic web, with its structured and interconnected data, has become a critical foundation for enabling machines to understand and process information in a human-like manner. However, as the volume and complexity of semantic web data grow, ensuring its integrity and accuracy becomes increasingly challenging. Anomalies in this data, such as inconsistencies, errors, or unusual patterns, can disrupt the functionality of applications relying on it, leading to erroneous conclusions or operational failures. This project aims to explore the application of Large Language Models (LLMs) for detecting anomalies in semantic web data. LLMs, trained on vast amounts of text, possess a deep understanding of language and context, making them well-suited to identify deviations from expected patterns in data that are semantically rich and context-dependent. The core objective is to leverage LLMs to model "normal" relationships and patterns within semantic web data, such as those found in RDF triples or knowledge graphs. By training an LLM on a dataset of validated semantic web data, the model can learn typical associations and structures. Once trained, the LLM can then be used to evaluate new or existing data, flagging entries that significantly deviate from the learned patterns as potential anomalies. This approach offers a novel method for maintaining the quality and reliability of semantic web data, which is crucial for applications in areas such as knowledge management, information retrieval, and AI-driven decision-making. The project will involve fine-tuning an LLM on domain-specific semantic web datasets, developing an anomaly detection framework, and testing the system on real-world data to assess its effectiveness in identifying both known and novel anomalies. Ultimately, this research could enhance the robustness of semantic web technologies, contributing to more reliable and accurate AI systems that depend on semantic data for understanding and reasoning.

Assumed knowledge

Programming in Python, Fundamentals of Machine Learning and AI, Knowledge Graphs usage.