CEAR: Automatic construction of a knowledge graph of chemical entities and roles from scientific literature
- URL: http://arxiv.org/abs/2407.21708v1
- Date: Wed, 31 Jul 2024 15:56:06 GMT
- Title: CEAR: Automatic construction of a knowledge graph of chemical entities and roles from scientific literature
- Authors: Stefan Langer, Fabian Neuhaus, Andreas Nürnberger,
- Abstract summary: We propose a methodology that involves augmenting existing annotated text corpora with knowledge from Chebi and fine-tuning a large model (LLM) to recognize chemical entities and their roles in scientific text.
By combining ontological knowledge understanding capabilities of LLMs, we achieve high precision and recall rates in identifying both the chemical entities and roles in scientific literature.
- Score: 4.086092284014203
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Ontologies are formal representations of knowledge in specific domains that provide a structured framework for organizing and understanding complex information. Creating ontologies, however, is a complex and time-consuming endeavor. ChEBI is a well-known ontology in the field of chemistry, which provides a comprehensive resource for defining chemical entities and their properties. However, it covers only a small fraction of the rapidly growing knowledge in chemistry and does not provide references to the scientific literature. To address this, we propose a methodology that involves augmenting existing annotated text corpora with knowledge from Chebi and fine-tuning a large language model (LLM) to recognize chemical entities and their roles in scientific text. Our experiments demonstrate the effectiveness of our approach. By combining ontological knowledge and the language understanding capabilities of LLMs, we achieve high precision and recall rates in identifying both the chemical entities and roles in scientific literature. Furthermore, we extract them from a set of 8,000 ChemRxiv articles, and apply a second LLM to create a knowledge graph (KG) of chemical entities and roles (CEAR), which provides complementary information to ChEBI, and can help to extend it.
Related papers
- MolCap-Arena: A Comprehensive Captioning Benchmark on Language-Enhanced Molecular Property Prediction [44.27112553103388]
We present Molecule Caption Arena: the first comprehensive benchmark of large language models (LLMs)augmented molecular property prediction.
We evaluate over twenty LLMs, including both general-purpose and domain-specific molecule captioners, across diverse prediction tasks.
Our findings confirm the ability of LLM-extracted knowledge to enhance state-of-the-art molecular representations.
arXiv Detail & Related papers (2024-11-01T17:03:16Z) - ChemEval: A Comprehensive Multi-Level Chemical Evaluation for Large Language Models [62.37850540570268]
Existing benchmarks in this domain fail to adequately meet the specific requirements of chemical research professionals.
ChemEval identifies 4 crucial progressive levels in chemistry, assessing 12 dimensions of LLMs across 42 distinct chemical tasks.
Results show that while general LLMs excel in literature understanding and instruction following, they fall short in tasks demanding advanced chemical knowledge.
arXiv Detail & Related papers (2024-09-21T02:50:43Z) - ChemVLM: Exploring the Power of Multimodal Large Language Models in Chemistry Area [50.15254966969718]
We introduce textbfChemVLM, an open-source chemical multimodal large language model for chemical applications.
ChemVLM is trained on a carefully curated bilingual dataset that enhances its ability to understand both textual and visual chemical information.
We benchmark ChemVLM against a range of open-source and proprietary multimodal large language models on various tasks.
arXiv Detail & Related papers (2024-08-14T01:16:40Z) - Integrating Chemistry Knowledge in Large Language Models via Prompt Engineering [2.140221068402338]
This paper presents a study on the integration of domain-specific knowledge in prompt engineering to enhance the performance of large language models (LLMs) in scientific domains.
A benchmark dataset is curated to the intricate physical-chemical properties of small molecules, their drugability for pharmacology, alongside the functional attributes of enzymes and crystal materials.
The proposed domain-knowledge embedded prompt engineering method outperforms traditional prompt engineering strategies on various metrics.
arXiv Detail & Related papers (2024-04-22T16:55:44Z) - Scientific Large Language Models: A Survey on Biological & Chemical Domains [47.97810890521825]
Large Language Models (LLMs) have emerged as a transformative power in enhancing natural language comprehension.
The application of LLMs extends beyond conventional linguistic boundaries, encompassing specialized linguistic systems developed within various scientific disciplines.
As a burgeoning area in the community of AI for Science, scientific LLMs warrant comprehensive exploration.
arXiv Detail & Related papers (2024-01-26T05:33:34Z) - Diversifying Knowledge Enhancement of Biomedical Language Models using
Adapter Modules and Knowledge Graphs [54.223394825528665]
We develop an approach that uses lightweight adapter modules to inject structured biomedical knowledge into pre-trained language models.
We use two large KGs, the biomedical knowledge system UMLS and the novel biochemical OntoChem, with two prominent biomedical PLMs, PubMedBERT and BioLinkBERT.
We show that our methodology leads to performance improvements in several instances while keeping requirements in computing power low.
arXiv Detail & Related papers (2023-12-21T14:26:57Z) - Structured Chemistry Reasoning with Large Language Models [70.13959639460015]
Large Language Models (LLMs) excel in diverse areas, yet struggle with complex scientific reasoning, especially in chemistry.
We introduce StructChem, a simple yet effective prompting strategy that offers the desired guidance and substantially boosts the LLMs' chemical reasoning capability.
Tests across four chemistry areas -- quantum chemistry, mechanics, physical chemistry, and kinetics -- StructChem substantially enhances GPT-4's performance, with up to 30% peak improvement.
arXiv Detail & Related papers (2023-11-16T08:20:36Z) - Large Language Models, scientific knowledge and factuality: A framework to streamline human expert evaluation [0.0]
This work explores the potential of Large Language Models for dialoguing with biomedical background knowledge.
The framework involves of three evaluation steps, each assessing different aspects sequentially: fluency, prompt alignment, semantic coherence, factual knowledge, and specificity of the generated responses.
The work provides a systematic assessment on the ability of eleven state-of-the-art models LLMs, including ChatGPT, GPT-4 and Llama 2, in two prompting-based tasks.
arXiv Detail & Related papers (2023-05-28T22:46:21Z) - COVID-19 Literature Knowledge Graph Construction and Drug Repurposing
Report Generation [79.33545724934714]
We have developed a novel and comprehensive knowledge discovery framework, COVID-KG, to extract fine-grained multimedia knowledge elements from scientific literature.
Our framework also provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence.
arXiv Detail & Related papers (2020-07-01T16:03:20Z)
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.