Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Text

• URL: http://arxiv.org/abs/2308.02357v1
• Date: Fri, 4 Aug 2023 14:47:15 GMT
• Title: Text2KGBench: A Benchmark for Ontology-Driven Knowledge Graph Generation from Text
• Authors: Nandana Mihindukulasooriya, Sanju Tiwari, Carlos F. Enguix, Kusum Lata
• Abstract summary: Large language models (LLM) and foundation models with emergent capabilities have been shown to improve the performance of many NLP tasks. We present Text2KGBench, a benchmark to evaluate the capabilities of language models to generate Knowledge Graphs (KGs) from natural language text guided by an ontology.
• Score: 2.396908230113859
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The recent advances in large language models (LLM) and foundation models with emergent capabilities have been shown to improve the performance of many NLP tasks. LLMs and Knowledge Graphs (KG) can complement each other such that LLMs can be used for KG construction or completion while existing KGs can be used for different tasks such as making LLM outputs explainable or fact-checking in Neuro-Symbolic manner. In this paper, we present Text2KGBench, a benchmark to evaluate the capabilities of language models to generate KGs from natural language text guided by an ontology. Given an input ontology and a set of sentences, the task is to extract facts from the text while complying with the given ontology (concepts, relations, domain/range constraints) and being faithful to the input sentences. We provide two datasets (i) Wikidata-TekGen with 10 ontologies and 13,474 sentences and (ii) DBpedia-WebNLG with 19 ontologies and 4,860 sentences. We define seven evaluation metrics to measure fact extraction performance, ontology conformance, and hallucinations by LLMs. Furthermore, we provide results for two baseline models, Vicuna-13B and Alpaca-LoRA-13B using automatic prompt generation from test cases. The baseline results show that there is room for improvement using both Semantic Web and Natural Language Processing techniques.

Related papers

• Can LLMs be Good Graph Judger for Knowledge Graph Construction? [33.958327252291]
  In this paper, we propose GraphJudger, a knowledge graph construction framework to address the aforementioned challenges. We introduce three innovative modules in our method, which are entity-centric iterative text denoising, knowledge aware instruction tuning and graph judgement. Experiments conducted on two general text-graph pair datasets and one domain-specific text-graph pair dataset show superior performances compared to baseline methods.
  arXiv  Detail & Related papers  (2024-11-26T12:46:57Z)
• Graph-DPEP: Decomposed Plug and Ensemble Play for Few-Shot Document Relation Extraction with Graph-of-Thoughts Reasoning [34.85741925091139]
  Graph-DPEP framework is grounded in the reasoning behind triplet explanation thoughts presented in natural language. We develop "ensemble-play", reapplying generation on the entire type list by leveraging the reasoning thoughts embedded in a sub-graph.
  arXiv  Detail & Related papers  (2024-11-05T07:12:36Z)
• Ontology Population using LLMs [0.9894420655516563]
  Knowledge graphs (KGs) are increasingly utilized for data integration, representation, and visualization. LLMs offer promising capabilities for such tasks, excelling in natural language understanding and content generation. This study investigates LLM effectiveness for the KG population, focusing on the Enslaved.org Hub Ontology.
  arXiv  Detail & Related papers  (2024-11-03T15:39:20Z)
• Prompt-based vs. Fine-tuned LLMs Toward Causal Graph Verification [0.0]
  This work aims toward an application of natural language processing (NLP) technology for automatic verification of causal graphs using text sources. We compare the performance of two types of NLP models: (1) pre-trained language models fine-tuned for causal relation classification task and, (2) prompt-based LLMs.
  arXiv  Detail & Related papers  (2024-05-29T09:06:18Z)
• Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning [51.90524745663737]
  A key innovation is our use of explanations as features, which can be used to boost GNN performance on downstream tasks. Our method achieves state-of-the-art results on well-established TAG datasets. Our method significantly speeds up training, achieving a 2.88 times improvement over the closest baseline on ogbn-arxiv.
  arXiv  Detail & Related papers  (2023-05-31T03:18:03Z)
• LeTI: Learning to Generate from Textual Interactions [60.425769582343506]
  We explore LMs' potential to learn from textual interactions (LETI) that not only check their correctness with binary labels but also pinpoint and explain errors in their outputs through textual feedback. Our focus is the code generation task, where the model produces code based on natural language instructions. LETI iteratively fine-tunes the model, using the objective LM, on a concatenation of natural language instructions, LM-generated programs, and textual feedback.
  arXiv  Detail & Related papers  (2023-05-17T15:53:31Z)
• MURMUR: Modular Multi-Step Reasoning for Semi-Structured Data-to-Text Generation [102.20036684996248]
  We propose MURMUR, a neuro-symbolic modular approach to text generation from semi-structured data with multi-step reasoning. We conduct experiments on two data-to-text generation tasks like WebNLG and LogicNLG.
  arXiv  Detail & Related papers  (2022-12-16T17:36:23Z)
• Deep Bidirectional Language-Knowledge Graph Pretraining [159.9645181522436]
  DRAGON is a self-supervised approach to pretraining a deeply joint language-knowledge foundation model from text and KG at scale. Our model takes pairs of text segments and relevant KG subgraphs as input and bidirectionally fuses information from both modalities.
  arXiv  Detail & Related papers  (2022-10-17T18:02:52Z)
• Explaining Patterns in Data with Language Models via Interpretable Autoprompting [143.4162028260874]
  We introduce interpretable autoprompting (iPrompt), an algorithm that generates a natural-language string explaining the data. iPrompt can yield meaningful insights by accurately finding groundtruth dataset descriptions. Experiments with an fMRI dataset show the potential for iPrompt to aid in scientific discovery.
  arXiv  Detail & Related papers  (2022-10-04T18:32:14Z)
• BenchCLAMP: A Benchmark for Evaluating Language Models on Syntactic and Semantic Parsing [55.058258437125524]
  We introduce BenchCLAMP, a Benchmark to evaluate Constrained LAnguage Model Parsing. We benchmark eight language models, including two GPT-3 variants available only through an API. Our experiments show that encoder-decoder pretrained language models can achieve similar performance or surpass state-of-the-art methods for syntactic and semantic parsing when the model output is constrained to be valid.
  arXiv  Detail & Related papers  (2022-06-21T18:34:11Z)

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.