OntoChat: A Framework for Conversational Ontology Engineering

Kernkonzepte

LLMs can assist and facilitate OE activities through a conversational approach, reducing complexity and accelerating tasks.

Zusammenfassung

Ontology engineering faces challenges in multi-party interactions, leading to systematic ambiguities. OntoChat introduces a conversational framework leveraging LLMs for requirement elicitation, analysis, and testing. Users interact with a conversational agent to create user stories and extract competency questions. The framework aims to streamline traditional ontology engineering activities by providing computational support. Evaluation results show positive reception among domain experts and ontology engineers, highlighting the potential of LLMs in enhancing efficiency and collaboration in ontology engineering.

Statistiken

Participants: N = 23
Positive responses for demanding OE tasks: Collection of ontology requirements (86.4%), Extraction of CQs (81.8%), Analysis of requirements (77.3%), Ontology testing (81.8%)
Correct predictions in preliminary ontology testing: True positives 25, True negatives 24

Zitate

Wichtige Erkenntnisse aus

OntoChat

by Bohu... um arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.05921.pdf

Tiefere Fragen

How can the use of LLMs impact the scalability of OntoChat in handling larger projects

LLMs can significantly impact the scalability of OntoChat in handling larger projects by automating and streamlining various ontology engineering tasks. With LLMs, OntoChat can efficiently process a large volume of textual data, such as user stories and competency questions, enabling faster requirement elicitation and analysis. The language understanding capabilities of LLMs allow for more accurate extraction of information from stakeholders and domain experts, reducing manual effort and time constraints. Additionally, LLMs can assist in clustering competency questions, identifying patterns, and organizing requirements effectively in complex projects with numerous stakeholders.

What are the potential limitations or biases that could arise from relying heavily on computational support in ontology engineering

While relying heavily on computational support in ontology engineering offers many benefits, there are potential limitations and biases to consider. One limitation is the risk of over-reliance on automated processes leading to a lack of human oversight or critical thinking. Biases may arise from the training data used to develop the LLM models, potentially introducing skewed interpretations or inaccuracies in requirement extraction or analysis. Furthermore, there could be challenges in interpreting nuanced domain-specific concepts accurately without human intervention. It's essential to balance computational support with human expertise to mitigate these limitations.

How might the integration of real-time feedback mechanisms enhance the effectiveness of OntoChat's conversational approach

The integration of real-time feedback mechanisms can enhance the effectiveness of OntoChat's conversational approach by providing immediate insights into user interactions and improving system performance iteratively. Real-time feedback allows users to correct misunderstandings or provide additional context during conversations with the system, leading to more accurate results. By incorporating feedback loops into OntoChat's workflow, developers can continuously refine the model based on user input, ensuring that it adapts dynamically to evolving requirements and preferences. This iterative process enhances user satisfaction and overall usability while optimizing ontology engineering outcomes through constant improvement based on real-world usage scenarios.