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

TL;DR

Text2KGBench is a new benchmark designed to evaluate how well language models can generate knowledge graphs from natural language text guided by ontologies, highlighting current limitations and areas for improvement.

Contribution

The paper introduces Text2KGBench, a comprehensive benchmark with datasets, evaluation metrics, and baseline results for ontology-guided KG generation from text.

Findings

Baseline models show significant room for improvement.

Evaluation metrics effectively measure fact extraction and ontology conformance.

Current LLMs exhibit hallucinations and inconsistencies in KG generation.

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.