source: https://arxiv.org/html/2412.02035v1 by Nadeen Fathallah, Alsayed Algergawy
- Abstract
- 1 Introduction
- 2 Related Work
- 3 Methodology
- 4 Experiments and Results
- 4.1 Experiment 1: Baseline NeOn-GPT (AquaDiva)
- 4.2 Experiment 2: Count Metric-Guided Prompts (AquaDiva)
- 4.3 Experiment 3: Merging Ontologies (AquaDiva)
- 4.4 Experiment 4: Re-prompting & Advanced Role-play Prompting (Habitat)
- 4.5 Experiment 5: Reuse (Role)
- 4.6 Experiment 6: Reuse of domain-specific examples (Carbon & Nitrogen Cycling)
- 4.7 Comprehensive Ontology Performance Overview
- 5 Conclusion and Future work
- 6 Appendix A: Persona Used for Role-play Prompting
Challenges in Ontology Learning for Complex Domains using LLMs:
- Existing Large Language Models (LLMs) have limitations:
- Struggle to generate ontologies with multiple hierarchical levels
- Limited rich interconnections
- Inadequate domain adaptation
- Reasons for these challenges:
- Token constraints in LLMs
- Insufficient specialized knowledge
Addressing Challenges through NeOn-GPT Pipeline Extension:
- Enhancing generated ontologies' domain-specific reasoning and structural depth with advanced prompt engineering techniques
- Ontology reuse for improved results
Evaluation of LLMs in Complex Domains:
- Case study: AquaDiva ontology in the life science domain (AquaDiva 11https://www.aquadiva.uni-jena.de/)
- Evaluation criteria: logical consistency, completeness, scalability
Results and Conclusion:
- LLMs can be viable for ontology learning in specialized domains like life science
- Addresses limitations in model performance and scalability.
Ontology Learning
Tasks: Ontology extraction, ontology generation, or ontology acquisition.
- Automatic/semi-automatic creation of ontologies from natural language text
- Extracting domain terms and relationships between concepts
- Encoding with an ontology language for easy retrieval
Challenges in Complex Domains:
- Limited ability of Large Language Models (LLMs) to generate ontologies in highly specialized domains like life sciences
- Inherent complexity, domain-specific terminologies, and data limit logical depth required for advanced reasoning
AquaDiva Ontology as a Use Case:
- Collaborative research project across biology, geology, chemistry, and computer science
- Objective: Enhance understanding of Earth's critical zone
- Standardize data, integrate, and ensure interoperability using semantic web approaches
- AquaDiva Ontology (ADOn): Developed with 78.840 axioms, 8.892 concepts, and 245 object properties
Importance of Accurate Ontologies:
- Facilitate scientific research
- Support advanced data analysis and decision-making
- Enhance understanding of complex ecological processes
- Improve scientific communication
Limitations of Current Approaches:
- Heavy reliance on manual processes: labor-intensive, prone to human error
- Potential for efficiency enhancement with LLMs but requires rigorous evaluation
- Evaluating generated ontologies for logical soundness, domain coverage, and adaptability
Evaluation of LLMs in Complex Domains:
- Insufficient domain adaptation leads to simplified structures, shallow hierarchies, and limited subclass depth
- Vast amount of information often exceeds token limitations, resulting in incomplete outputs
Proposed Approach:
- Extension of NeOn-GPT pipeline with advanced prompt engineering techniques
- Re-prompting strategies to refine output and enhance depth and hierarchy
- Increased use of few-shot examples and advanced role-play prompting
- Domain categorization strategy to handle token limitations
- Incorporation of ontology reuse in the NeOn-GPT pipeline
Experimental Evaluation:
- Assess structural complexity, depth, and logical consistency of generated ontologies using AquaDiva ontology as a case study.
LLMs in Ontology Learning:
- LLMs enhance various ontology-related tasks: creation, enrichment, refinement (Mateiu et al.)
- Challenges: maintaining deep structure, avoiding irrelevant axioms (Mateiu et al.), fine-tuning necessary for domain-specific tasks (Babaei Giglou et al.)
- LLMs4OL framework: Term Typing, Taxonomy Discovery, Non-Taxonomic Relation Extraction (Babaei Giglou et al.)
- Reduces human effort but requires manual validation due to variability in LLM output and prompt sensitivity (Kommineni et al., Saeedizade and Blomqvist)
- Shallow hierarchy generation, token limitations, insufficient domain adaptation are common limitations of off-the-shelf LLMs (Mai et al.)
Improvements:
- Employ re-prompting techniques and keyword categorization strategy to manage token constraints (this work)
- Leverage ontology reuse: incorporate existing ontological structures to guide LLM generation of more detailed hierarchies and relationships (this work)
- Ensures consistency with established domain knowledge while allowing for comprehensive ontology generation in specialized domains like AquaDiva ontology.
NeOn-GPT Pipeline for Ontology Learning: Extensions for Complex Domains
Background:
- Built on previous work with NeOn methodology framework [^11]
- Translates structured process into prompts for pre-trained LLMs
- Effective ontology generation in popular domains but not specialized ones
Motivation:
- Extend pipeline to handle more complex and specialized domains, such as life sciences
- Enhancements enable deeper understanding of domain-specific knowledge
Methodology:
- Specification of Ontology Requirements:
- Define purpose, scope, target group, functional requirements using chain-of-thought (CoT) prompting
- Integrate domain descriptions and keywords into CoT prompt
- Refine role-play persona for more contextually relevant outputs
- Reuse of Ontological Knowledge Resources:
- Identify critical limitations in LLM's ability to generate ontological structures that meet predefined criteria
- Introduce reuse of structural information (count metrics) from gold standard AquaDiva ontology to improve overall structure and alignment with predefined metrics
- Ontology Conceptualization:
- Extract entities and relationships through few-shot prompting
- Tailor process to AquaDiva ontology using domain-specific examples
Enhancements:
- Reuse (Role):
- Manually extract examples from Environment Ontology (ENVO) for assessment of hierarchical depth, interoperability, and relevance within broader ontological ecosystem
- Structural Improvements:
- Prompt LLM to target predefined counts for various ontology components
- Introduce refined prompt to increase subclass count and improve hierarchy depth and interconnectivity
Benefits:
- Effectively generates ontologies in complex domains, significantly advancing ontology learning for niche areas.
Evaluating LLM's Performance with AquaDiva Ontologies:
- Experiments to assess impact of pipeline updates on generating complex life science ontologies
- Focusing on AquaDiva, a domain integrating hydrogeology, microbial ecology, etc. (78.840 axioms, 8.892 concepts, 245 object properties) [^12]
- Experiments conducted using GPT-4o [^17]
- Results discussed to illustrate improvements achieved through updated pipeline
- Code base accessible at: https://github.com/NadeenAhmad/NeOn-GPTAquaDivaOntology
Preparing for Experiments:
- Evaluating LLM performance using AquaDiva ontologies before and after pipeline updates
- Focus on complex life science domains, specifically AquaDiva
- Experiments conducted with GPT-4o [^17]
- Results discussed to demonstrate improvements from updated pipeline
- Code base available at: https://github.com/NadeenAhmad/NeOn-GPTAquaDivaOntology
Assessing LLM Performance:
- Evaluating Logical Consistency and Structural Depth of Generated Ontologies
- Using AquaDiva ontologies as a test domain (complex life science domain)
- Experiments conducted with GPT-4o [^17]
- Results presented to illustrate improvements from updated pipeline.
Experiment 1: Baseline NeOn-GPT (AquaDiva)
- Evaluated LLM performance without enhancements: Applied original pipeline, included domain-specific keywords to compensate for lack of relevant training data
- Results:
- Captured key concepts like 'aquifers' and 'microbial communities' but:
- Ontology remained overly simplistic with sparse hierarchy
- Lacked complexity needed for advanced ecological modeling
- Metrics and class hierarchy:
- 176 classes (significantly fewer than gold standard)
- 44 object properties (omission of crucial relationships and subclass hierarchies)
- Absence of equivalent and disjoint classes, reduced logical axioms (323 vs. 16,303 in the gold standard)
- Captured key concepts like 'aquifers' and 'microbial communities' but:
- Generated ontology:
- Included important concepts like 'Aquifer' and its subclasses, environmental factors
- Lacked relational depth to describe interactions between entities, impacting ability to model microbial interactions within environments
- Limited representation of complex ecological relationships and taxonomic structures due to:
- Reduced number of individuals (13)
- Data properties (26)
- Impact on utility: Simplified logical framework made it difficult to support detailed ecological queries, significantly reducing its utility for in-depth reasoning about environmental and biological phenomena.
Experiment 2: AquaDiva Ontology Generation (Count Metric-Guided Prompts)
- Revised prompt pipeline from Experiment 1 to incorporate explicit count metrics
- Incorporated AquaDiva gold standard metrics: classes (8,892), object properties (245)
- Emphasized subclass count of n-1 (where n is the total number of classes) to address shallow hierarchies in Experiment 1
Results:
- More interconnected structure with increased density and layered hierarchy
- Increased concepts and relationships, aligning more closely to domain complexity
- Significant improvements over initial version
- Notable increase in classes (342) and axioms (795) compared to Experiment 1
- Improved hierarchy with more subclass levels, e.g., "HydroChemistry" -> "SubClassOf" -> "Geological Chemistry" -> "SubClassOf" -> "Earth Science"
- Discrepancies in object property count (8 vs. expected 245) due to:
- GPT-4's output limit (4096 tokens) restricting content generation
- LLM's mathematical limitations, particularly in precise counting tasks
- Redundancy with overlapping object properties, e.g., "interact with" and "interacts with," requiring further refinement.
Experiment 3: Merging Ontologies (AquaDiva)
- Improvements in key metrics:
- Total axiom count increased to 1,479
- Object property count rose to 50
- Captured broader set of relationships and axioms, resulting in more comprehensive ontology
- Limitations persist:
- Class count (500) below gold standard AquaDiva ontology
- Discrepancies in data and annotation properties
- Object property count (245) still falls short of expected value
- Progress made in logical consistency:
- 713 logical axioms
- 114 SubClassOf axioms
- Improved structure for defining relationships and hierarchical taxonomies
- Number of disjoint classes (109) lagging, impacting ability to differentiate categories for accurate environmental modeling.
Experiment 4: Generating an Ontology for Habitat Category (AquaDiva)
Approach: Instructed LLM to categorize AquaDiva keywords into 22 categories instead of generating the entire ontology due to output token constraints.
Goals:
- Improve quality and precision by providing richer domain-specific context
- Increase number of few-shot examples for better guidance
- Refine role-play persona as an expert aquatic ecologist
- Apply re-prompting for iterative refinement
Results:
- Ontology metrics: 630 axioms, 275 logical axioms, 75 classes
- Progress in object property count (47) but still lacking in several areas
- Incomplete class relationships (single DisjointClasses axiom, 3 EquivalentClasses axioms)
- Insufficient SubClassOf axioms (44) for detailed hierarchical structure
- Lack of comprehensive disjointness and equivalence axioms.
Experiment 5: Role Ontology Generation (Role)
Strengths:
- Axiom count: 969 axioms
- Class count: 118 classes
- Subclass count: 86 subclasses (significant increase from Experiment 4)
- Represents complex relationships within the Role domain
- Suitable for supporting advanced reasoning tasks
- Includes 57 individual instances
Improvements:
- Enhanced subclass hierarchy through reuse of ENVO example
- More layered and detailed ontology structure
Limitations:
- Logical consistency: Needs improvement with only 17 EquivalentClasses axioms
- Underdeveloped in terms of DisjointClass distinctions, with only 10 axioms
- Broadness of some classes (e.g., "Biological Role", "Chemical Role") may dilute focus and reduce utility within AquaDiva ontology.
Experiment 6: Carbon and Nitrogen Cycling Domain
Ontology Generation:
- Building on lessons from previous experiments
- Reuse of existing ontological resources improves terminology generation
- Increased number of classes and subclasses from 4.4 to 4.5
- Selected Carbon and Nitrogen Cycling domain for evaluation
Improvements:
- Continued using advanced role-play persona from Experiments 4 & 5
- Detailed description with domain-specific keywords to guide model's understanding
- Increased number of few-shot examples tailored to Carbon and Nitrogen Cycling domain
- Syntax and consistency restrictions at all stages for logical consistency
Reuse of Existing Ontological Resources:
- Targeted reuse approach, using specific components from ENVO
- Clearer structure, ensuring accurate hierarchical depth and detailed relationships
Results:
- Significant improvements in capturing complex biochemical processes
- Key entities like "Carbon Fixation" and "Nitrogen Transformation" accurately modeled
- 157 classes, 63 object properties, enabling detailed interactions
- Hierarchical depth enhanced with 130 SubClassOf axioms from ENVO
- 1,169 axioms, 455 of which are logical, for more detailed process representations
- Limited ability to fully capture equivalent biochemical processes and distinctions between exclusive pathways.
Comparative Analysis of Generated Ontologies
Evaluation Metrics:
- Number of entities in LLM-generated ontologies that match entities in gold standard ontologies
- Concept similarity: average similarity score for matched concepts with gold standard ontology
Results with AquaDiva Ontology:
| Experiment | Matched Entities | Average Similarity Score |
|---|---|---|
| 1 (Baseline) | 17 | 0.896 |
| 2 (Count Metric-Guided Prompts) | 66 | 0.894 |
| 3 (Merging Ontologies) | 80 | 0.874 |
| 4 (Re-prompting & Roleplay Prompting) | 16 | 0.898 |
| 5 (Reuse) | 56 | 0.905 |
| 6 (Reuse of domain-specific examples) | 65 | 0.859 |
Results with ENVO Ontology:
| Experiment | Matched Entities | Average Similarity Score |
|---|---|---|
| 1 (Baseline) | 8 | 0.877 |
| 2 (Count Metric-Guided Prompts) | 57 | 0.969 |
| 3 (Merging Ontologies) | 60 | 0.885 |
| 4 (Re-prompting & Roleplay Prompting) | 13 | 0.800 |
| 5 (Reuse) | 54 | 0.886 |
| 6 (Reuse of domain-specific examples) | 51 | 0.884 |
Findings:
- Generated ontologies do not fully capture breadth and depth of domain knowledge as gold standard ontologies
- Aligned entities demonstrate high similarity scores, approaching or exceeding 0.85
- Number of matched entities increases across experiments, indicating improvements in prompt engineering techniques and pipeline refinements.
- LLM-based approaches show potential for complex ontology generation tasks.
Approach to Enhance Ontology Learning in Complex Domains:
- Extends NeOn-GPT pipeline for deep and well-structured ontologies in complex domains like life sciences
- Addresses limitations of Language Models (LLMs) in generating complex hierarchies and token constraints
- Leverages advanced prompt engineering, ontology reuse, and iterative refinement
Challenges:
- Shallow hierarchies: addressed with careful prompt design and curated examples for reuse
- Token constraints: not specified in the provided text how they are tackled
Case Study: AquaDiva
- Complex domain requiring additional contextual information in prompts and carefully curated examples
- Quality improvement through manual efforts and expert input
Future Work:
- Explore automating the process using Retrieval-Augmented Generation (RAG)
- Integrate external domain-specific resources dynamically to reduce manual intervention
- Evaluate complete AquaDiva ontology, focusing on refining consistency in relationships and capturing intricacies of specialized domains.
Acknowledgements:
- Funding by Deutsche Forschungsgemeinschaft (DFG) as part of CRC 1076 AquaDiva (Projectnumber 218627073).
Expert Aquatic Ecologist and Knowledge Engineer
Background:
- PhD in Ecology
- Additional training in data science and semantic technologies
- Extensive experience in field research and computational modeling of aquatic ecosystems
Specialties:
- Understanding biological, chemical, and physical characteristics of water bodies
- Developing ecological ontologies for scientific research and environmental management
- Identifying essential entities and relationships within the ecological domain (e.g., key species, roles, conditions, processes)
- Applying tools like Turtle to create well-defined ontologies representing complex ecological data in a structured format
- Meticulous and user-centric approach to ontology creation, ensuring interoperability, data sharing, and reuse among stakeholders
Expertise:
- Deep domain knowledge of aquatic ecology
- Enhancing understanding and application of ecological data through detailed explanations of concepts and interconnections
- Bridging the gap between raw data and actionable knowledge by developing comprehensive ontological frameworks for advanced analysis and decision-making in aquatic ecology
AquaDiva Domain:
- Studying groundwater ecosystems
- Integrating hydrogeology, microbial ecology, geochemistry, karst systems, and environmental science.