Overview
The research background of this paper is the performance improvement of large language models (LLM) and ontology-based knowledge graphs (KG) in natural language processing (NLP) tasks. There are some problems with past methods, and the method proposed in this paper solves these problems and encourages new methods by generating KG from text and following the given ontology. This paper proposes Text2KGBench, a benchmark for evaluating the ability of language models to generate KGs from natural language text based on ontologies. The method uses two datasets and seven evaluation metrics to evaluate extraction performance, ontology compliance, and the "illusion" of LLM. Results for two benchmark models are also provided. This method achieves certain performance on the task of generating KG from text. Benchmark results show further improvements in performance using Semantic Web and Natural Language Processing techniques, supporting the paper's goals.
Discussion of important issues
1. The introduction mentioned that recent advances in large language models (LLM) and basic models have improved the performance of natural language processing (NLP) tasks, but how do these models complement each other with knowledge graphs (KG) so that LLM can be used for Construction or completion of KGs, while existing KGs can be used to interpret LLM outputs or fact-check in a neural-symbolic manner?
○ LLM and KG can complement each other, and LLM can generate KG from natural language text by guiding ontology. LLM can be used to extract facts from text and ensure that these facts conform to a given ontology (including concepts, relationships, domain/scope constraints), while being faithful to the input sentences. By combining LLM with KG, the performance of natural language processing tasks can be improved and interpretive output and fact verification can be achieved.
2. The article mentions the benchmark tool Text2KGBench, whose main purpose is to evaluate the language model's ability to generate KG from natural language text. Please tell me, what data sets and evaluation indicators does Text2KGBench provide for testing and evaluating the performance of language models?
○ Text2KGBench provides two datasets: Wikidata-TekGen (containing 10 ontologies and 13,474 sentences) and DBpedia-WebNLG (containing 19 ontologies and 4,860 sentences). To evaluate the performance of language models, Text2KGBench defines seven evaluation metrics, including fact extraction performance, ontology consistency, and fictional situations of LLM.
3. In this article, in order to provide the results of the baseline model, the author used Vicuna-13B and Alpaca-LoRA-13B for experiments. Generate test cases based on automatic prompts. Does the baseline result show that there is still room for improvement using Semantic Web and natural language processing technology?
○ Baseline results show that after experiments using two baseline models, Vicuna-13B and Alpaca-LoRA-13B, there is still room for improvement using Semantic Web and natural language processing technologies. This means that current technologies have not fully exploited the potential of the Semantic Web and natural language processing, and there is room for improvement in the task of generating KGs from language models.
4. The article mentioned the application scope of knowledge graph (KG), including question and answer, recommendation, semantic search and interpretability advanced analysis, etc. When building a knowledge graph, when the data exists in unstructured text format and cannot be constructed using crowdsourcing methods, what natural language processing technologies can be used to build the knowledge graph?
○ When data exists in the form of unstructured text and cannot be constructed using crowdsourcing methods, natural language processing technology can be used to build a knowledge graph. These technologies include named entity recognition (NER), relationship extraction, open information extraction, entity linking and relationship linking, etc. By applying these natural language processing techniques to text data, structured knowledge can be extracted from it and a knowledge graph can be constructed.
5. In the Semantic Web community, there is growing interest in methods of building knowledge graphs using natural language processing (NLP) technology. What kind of work has been done or researched in this area so far? If so, please give an example.
○ Currently, the Semantic Web community has conducted some work and research on methods of using natural language processing technology to build knowledge graphs. For example, there have been some academic seminars covering this field, such as Text2KG and NLP4KGC. These workshops aim to promote research on the combination of Semantic Web and NLP, and explore methods and techniques for using NLP technology to build knowledge graphs from text. These works and studies demonstrate the growing interest of the Semantic Web community in building knowledge graphs using NLP methods.
Paper link: https://arxiv.org/abs/2308.02357.pdf