LLMs4OM: Matching Ontologies with Large Language Models

• URL: http://arxiv.org/abs/2404.10317v2
• Date: Tue, 23 Apr 2024 10:37:51 GMT
• Title: LLMs4OM: Matching Ontologies with Large Language Models
• Authors: Hamed Babaei Giglou, Jennifer D'Souza, Felix Engel, Sören Auer,
• Abstract summary: Ontology Matching (OM) is a critical task in knowledge integration, where aligning heterogeneous data interoperability and knowledge sharing. We present the LLMs4OM framework, a novel approach to evaluate the effectiveness of Large Language Models (LLMs) in OM tasks.
• Score: 0.14999444543328289
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ontology Matching (OM), is a critical task in knowledge integration, where aligning heterogeneous ontologies facilitates data interoperability and knowledge sharing. Traditional OM systems often rely on expert knowledge or predictive models, with limited exploration of the potential of Large Language Models (LLMs). We present the LLMs4OM framework, a novel approach to evaluate the effectiveness of LLMs in OM tasks. This framework utilizes two modules for retrieval and matching, respectively, enhanced by zero-shot prompting across three ontology representations: concept, concept-parent, and concept-children. Through comprehensive evaluations using 20 OM datasets from various domains, we demonstrate that LLMs, under the LLMs4OM framework, can match and even surpass the performance of traditional OM systems, particularly in complex matching scenarios. Our results highlight the potential of LLMs to significantly contribute to the field of OM.

Related papers

• OAEI-LLM: A Benchmark Dataset for Understanding Large Language Model Hallucinations in Ontology Matching [8.732396482276332]
  Hallucinations of large language models (LLMs) commonly occur in domain-specific downstream tasks, with no exception in ontology matching (OM) The OAEI-LLM dataset is an extended version of the Ontology Alignment Evaluation Initiative (OAEI) datasets that evaluate LLM-specific hallucinations in OM tasks.
  arXiv  Detail & Related papers  (2024-09-21T06:49:34Z)
• SoupLM: Model Integration in Large Language and Multi-Modal Models [51.12227693121004]
  Training large language models (LLMs) requires significant computing resources. Existing publicly available LLMs are typically pre-trained on diverse, privately curated datasets spanning various tasks.
  arXiv  Detail & Related papers  (2024-07-11T05:38:15Z)
• FAC$^2$E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition [56.76951887823882]
  Large language models (LLMs) are primarily evaluated by overall performance on various text understanding and generation tasks. We present FAC$2$E, a framework for Fine-grAined and Cognition-grounded LLMs' Capability Evaluation.
  arXiv  Detail & Related papers  (2024-02-29T21:05:37Z)
• Knowledge Fusion of Large Language Models [73.28202188100646]
  This paper introduces the notion of knowledge fusion for large language models (LLMs) We externalize their collective knowledge and unique strengths, thereby elevating the capabilities of the target model beyond those of any individual source LLM. Our findings confirm that the fusion of LLMs can improve the performance of the target model across a range of capabilities such as reasoning, commonsense, and code generation.
  arXiv  Detail & Related papers  (2024-01-19T05:02:46Z)
• Agent-OM: Leveraging LLM Agents for Ontology Matching [4.222245509121683]
  This study introduces a novel agent-powered design paradigm for Ontology matching systems. We propose a framework, namely Agent-OMw.r.t. Agent for Ontology Matching, consisting of two Siamese agents for matching and retrieval. Our system can achieve results very close to the long-standing best performance on simple OM tasks.
  arXiv  Detail & Related papers  (2023-12-01T03:44:54Z)
• Towards Vision Enhancing LLMs: Empowering Multimodal Knowledge Storage and Sharing in LLMs [72.49064988035126]
  We propose an approach called MKS2, aimed at enhancing multimodal large language models (MLLMs) Specifically, we introduce the Modular Visual Memory, a component integrated into the internal blocks of LLMs, designed to store open-world visual information efficiently. Our experiments demonstrate that MKS2 substantially augments the reasoning capabilities of LLMs in contexts necessitating physical or commonsense knowledge.
  arXiv  Detail & Related papers  (2023-11-27T12:29:20Z)
• Knowledge Plugins: Enhancing Large Language Models for Domain-Specific Recommendations [50.81844184210381]
  We propose a general paradigm that augments large language models with DOmain-specific KnowledgE to enhance their performance on practical applications, namely DOKE. This paradigm relies on a domain knowledge extractor, working in three steps: 1) preparing effective knowledge for the task; 2) selecting the knowledge for each specific sample; and 3) expressing the knowledge in an LLM-understandable way.
  arXiv  Detail & Related papers  (2023-11-16T07:09:38Z)
• ChEF: A Comprehensive Evaluation Framework for Standardized Assessment of Multimodal Large Language Models [49.48109472893714]
  Multimodal Large Language Models (MLLMs) have shown impressive abilities in interacting with visual content with myriad potential downstream tasks. We present the first Comprehensive Evaluation Framework (ChEF) that can holistically profile each MLLM and fairly compare different MLLMs. We will publicly release all the detailed implementations for further analysis, as well as an easy-to-use modular toolkit for the integration of new recipes and models.
  arXiv  Detail & Related papers  (2023-11-05T16:01:40Z)
• Metacognitive Prompting Improves Understanding in Large Language Models [12.112914393948415]
  We introduce Metacognitive Prompting (MP), a strategy inspired by human introspective reasoning processes. We conduct experiments on four prevalent Large Language Models (LLMs) across ten natural language understanding (NLU) datasets. MP consistently outperforms existing prompting methods in both general and domain-specific NLU tasks.
  arXiv  Detail & Related papers  (2023-08-10T05:10:17Z)
• Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate [41.949869545423375]
  Large Language Models (LLMs) have shown impressive capabilities in various applications, but they still face various inconsistency issues. To examine whether LLMs can collaborate effectively to achieve a consensus for a shared goal, we focus on commonsense reasoning. Our work contributes to understanding the inter-consistency among LLMs and lays the foundation for developing future collaboration methods.
  arXiv  Detail & Related papers  (2023-05-19T11:15:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.