Leveraging Large Language Models for Ontology and Knowledge Graph Construction

Kernekoncepter

Large Language Models (LLMs) offer a semi-automatic approach to constructing Knowledge Graphs (KGs) by reducing human effort and time in the process.

Resumé

The content explores how Large Language Models (LLMs) can automate the construction of Knowledge Graphs (KGs) by formulating competency questions, developing ontologies, constructing KGs, and evaluating results. By leveraging open-source LLMs, the study showcases a semi-automated pipeline for creating KGs on deep learning methodologies from scholarly publications in the biodiversity domain.

The conventional process of building Ontologies and Knowledge Graphs (KGs) relies heavily on human experts. Large Language Models (LLMs) have gained popularity for automating aspects of this process. The study demonstrates a pipeline involving competency questions, ontology development, KG construction using LLMs with minimal human involvement.

Research has shown that LLMs can revolutionize knowledge engineering and natural language processing tasks. The study focuses on minimizing human effort in ontology and KG construction processes by integrating LLM capabilities. By utilizing open-source LLM models, the research aims to streamline the creation of KGs from scholarly publications.

Statistik

The DLProv Ontology consists of 45 classes and 41 relationships with 365 axioms.
Two key outputs evaluated were generated CQ answers and automatically extracted KG concepts.
A total of 40 competency questions were generated during the study.
There were 42 disagreements between human annotators and the LLM Judge out of 200 evaluated CQ answers.

Citater

"Large Language Models exhibit remarkable capabilities in understanding and generating human-like text."
"LLMs have revolutionized knowledge engineering and NLP."
"Our proposed pipeline shows the potential of LLMs acting as assistants to human experts."

Vigtigste indsigter udtrukket fra

From human experts to machines

by Vams... kl. arxiv.org 03-14-2024

https://arxiv.org/pdf/2403.08345.pdf

Dybere Forespørgsler

How can prompt sensitivity impact the variability in Large Language Model outputs?

Prompt sensitivity refers to how sensitive a Large Language Model (LLM) is to the input prompts provided by users. In the context of ontology and knowledge graph construction, prompt sensitivity plays a crucial role in determining the quality and consistency of LLM-generated outputs.
When an LLM is prompted with different variations or orders of prompts, it can significantly impact the variability in its outputs. Minor alterations in prompt design can lead to substantial changes in the generated content, including answers to competency questions, ontology structures, and knowledge graph entities.
For example, slight modifications in wording or structure within a prompt may cause the LLM to focus on different aspects of the input data or generate responses that vary significantly from one iteration to another. This sensitivity can result in inconsistencies across multiple runs of the pipeline using similar data inputs but different prompts.
To mitigate this variability caused by prompt sensitivity, researchers need to carefully engineer their prompts by providing clear instructions and examples that guide the LLM towards generating relevant and accurate content consistently. By refining and optimizing prompts based on trial-and-error methods and incorporating contextual information where necessary, users can reduce fluctuations in output quality due to prompt sensitiveness.

What are the limitations associated with using open-source LLM models for ontology construction?

While open-source Large Language Models (LLMs) offer transparency, cost-effectiveness, model control, and usage flexibility for various applications like ontology construction, they also come with certain limitations that need consideration:

Hallucination: Open-source LLMs may generate text that includes fabricated information not present in the input data. This hallucinated content could introduce inaccuracies into ontologies constructed based on such outputs.

Lack of Critical Thinking: These models lack true understanding or critical thinking abilities; they rely solely on statistical patterns learned during training without genuine comprehension of concepts.

Outdated Retrieval: The information retrieved by open-source LLMs might be outdated as these models are trained on historical datasets which may not reflect current knowledge accurately.

Prompt Sensitivity: As discussed earlier regarding variability impacts due to prompt sensitivity issues when using open-source LLMs for ontology construction.

Semantic Understanding Limitations: While proficient at natural language processing tasks like text generation or completion, open-source LLMs may struggle with deep semantic understanding required for complex domain-specific ontologies.

Addressing these limitations requires careful validation processes involving human experts who understand both domain-specific nuances and potential errors introduced by automated systems.

How can automated pipelines like these impact future developments in knowledge graph engineering?

Automated pipelines leveraging Large Language Models (LLMs) have significant implications for advancing knowledge graph engineering practices:

Efficiency Gains: Automated pipelines reduce manual effort involved in constructing ontologies and populating knowledge graphs through semi-automatic processes facilitated by AI technologies.

Scalability: With automation tools powered by LLMs handling repetitive tasks efficiently at scale enables organizations to manage larger volumes of data more effectively.

3Quality Improvement: By automating parts of ontology creation process ensures consistency while reducing human error rates leading higher-quality knowledge graphs.
4Rapid Prototyping: Automated pipelines allow rapid prototyping enabling quick iterations testing new ideas improving overall development speed
5Human-in-the-Loop Collaboration: While automation streamlines processes having humans validate results ensures accuracy relevance maintaining high standards
6Innovation Catalyst: Automation frees up time resources allowing teams focus innovation exploration new techniques methodologies enhancing overall progress
By embracing automated pipelines driven by advanced AI technologies like large language models organizations stand benefit from increased efficiency improved accuracy accelerated innovation cycles ultimately leading enhanced capabilities within Knowledge Graph Engineering field