Automated Refinement of Wikidata Taxonomy Using Large Language Models

Główne pojęcia

An automated approach for refining the Wikidata taxonomy using a combination of Large Language Models (LLMs) and graph mining techniques, addressing issues like ambiguity, inconsistency, redundancy, and complexity.

Streszczenie

The paper presents an approach called WiKC for refining the Wikidata taxonomy, a general-purpose knowledge base maintained by a large community of contributors. Wikidata's taxonomy is known to have several challenges, including ambiguity between instances and classes, inaccuracy of taxonomic paths, presence of cycles, and high redundancy across classes.

The key steps of the approach are:

1. Taxonomy Extraction: The authors extract the actual taxonomy from Wikidata by prioritizing the instanceOf property over the subclassOf property, and handling exceptions like meta-classes. They then build an acyclic taxonomy graph, filtering out irrelevant classes.

2. Taxonomy Refinement: The authors use zero-shot prompting on an open-source LLM to analyze each link in the taxonomy and predict the semantic relation between the linked classes (subclassOf, superclassOf, equivalent, irrelevant, or none). Based on these predictions, they perform a series of operations to cut irrelevant links, resolve reversed links, reduce transitive links, merge equivalent classes, rewire links, and filter out non-informative and rare classes.

The resulting taxonomy, called WiKC, is evaluated both intrinsically and extrinsically. The intrinsic evaluation shows that WiKC is much simpler and more concise than the original Wikidata taxonomy, with a significantly lower number of classes, links, and depth. The extrinsic evaluation on an entity typing task demonstrates that WiKC consistently outperforms the original Wikidata taxonomy, especially at deeper levels of the hierarchy.

The authors discuss some limitations of their approach, such as potential issues with the LLM's responses and the need to assess the knowledge coverage of WiKC for downstream tasks. They also plan to explore the use of other open-source LLMs and investigate the trustworthiness of these models in the taxonomy refinement task.

Statystyki

Wikidata taxonomy has 4.1M classes, 4.8M links, and a depth of 20.
WiKC has 17k classes, 20k links, and a depth of 13.
Wikidata taxonomy has 35 cycles and 500k redundant links, while WiKC has none.
78% of classes in Wikidata have labels and descriptions, while 100% of classes in WiKC have them.

Cytaty

"Due to its collaborative nature, Wikidata is known to have a complex taxonomy, with recurrent issues like the ambiguity between instances and classes, the inaccuracy of some taxonomic paths, the presence of cycles, and the high level of redundancy across classes."
"We present WiKC, a new version of Wikidata taxonomy cleaned automatically using a combination of Large Language Models (LLMs) and graph mining techniques."
"The quality of the refined taxonomy is evaluated from both intrinsic and extrinsic perspectives, on a task of entity typing for the latter, showing the practical interest of WiKC."

Kluczowe wnioski z

Refining Wikidata Taxonomy using Large Language Models

by Yiwen Peng (... o arxiv.org 09-09-2024

https://arxiv.org/pdf/2409.04056.pdf

Głębsze pytania

How can the proposed approach be extended to handle other types of knowledge graphs beyond Wikidata?

The proposed approach for refining the Wikidata taxonomy using Large Language Models (LLMs) and graph mining techniques can be extended to other types of knowledge graphs (KGs) by adapting the methodology to accommodate the unique structures and semantics of different KGs. Here are several strategies for such an extension:

Custom Taxonomy Extraction: Each knowledge graph may have its own properties and relationships that define its taxonomy. The initial step would involve customizing the taxonomy extraction process to identify relevant properties (e.g., subclassOf, instanceOf) specific to the target KG. This may require domain knowledge to accurately interpret the relationships.

Domain-Specific Prompts: The zero-shot prompting mechanism used with LLMs can be tailored to reflect the specific terminology and relationships present in the new KG. By crafting prompts that resonate with the domain-specific language and context, the LLM can provide more accurate predictions regarding the semantic relationships between entities.

Evaluation Metrics: The evaluation framework for assessing the refined taxonomy should be adapted to include metrics that are relevant to the specific use cases of the new KG. For instance, if the KG is focused on biomedical data, metrics related to clinical relevance and accuracy in medical classifications would be prioritized.

Integration of Domain Knowledge: Incorporating domain-specific ontologies or expert knowledge can enhance the refinement process. This could involve using existing ontologies as a reference to validate the relationships predicted by the LLM, ensuring that the refined taxonomy aligns with established knowledge in the field.

Iterative Feedback Loop: Establishing a feedback mechanism where domain experts can review and provide input on the refined taxonomy can help mitigate errors and biases. This iterative process can enhance the quality and relevance of the taxonomy for specific applications.

By implementing these strategies, the approach can be effectively adapted to refine taxonomies in various knowledge graphs, enhancing their usability and accuracy across different domains.

What are the potential biases and limitations of using LLMs for taxonomy refinement, and how can they be mitigated?

Using Large Language Models (LLMs) for taxonomy refinement presents several potential biases and limitations, which can impact the quality and reliability of the refined taxonomy. Here are some key concerns and strategies for mitigation:

Bias in Training Data: LLMs are trained on vast datasets that may contain biases reflecting societal stereotypes or inaccuracies. This can lead to biased predictions in taxonomy relationships. To mitigate this, it is essential to curate training datasets that are diverse and representative of various perspectives, ensuring that the LLM is exposed to a balanced view of the concepts it will analyze.

Hallucination of Information: LLMs may generate responses that are not grounded in the input data, leading to the creation of non-existent classes or incorrect relationships. To address this, implementing a verification step where the generated relationships are cross-checked against existing knowledge or databases can help filter out hallucinations.

Inconsistency in Responses: LLMs can produce inconsistent outputs, where the explanation provided does not align with the final answer. To mitigate this, a structured prompting approach that emphasizes coherence between the explanation and the answer can be employed. Additionally, using ensemble methods where multiple LLMs provide input can help identify and resolve inconsistencies.

Lack of Domain Knowledge: LLMs may lack specific domain knowledge necessary for accurate taxonomy refinement, especially in specialized fields. Incorporating domain-specific ontologies and expert feedback into the refinement process can enhance the model's understanding and improve the accuracy of the taxonomy.

Overfitting to Specific Patterns: LLMs may overfit to patterns present in the training data, leading to a lack of generalization in novel contexts. Regularly updating the model with new data and retraining it can help maintain its relevance and adaptability to evolving knowledge.

By recognizing these biases and limitations and implementing appropriate mitigation strategies, the reliability and effectiveness of LLMs in taxonomy refinement can be significantly enhanced.

How can the knowledge coverage and usefulness of WiKC be further validated for a wider range of downstream applications?

To further validate the knowledge coverage and usefulness of WiKC for a broader range of downstream applications, several strategies can be employed:

Comprehensive Benchmarking: Conducting extensive benchmarking against existing knowledge bases and taxonomies in various domains can help assess the completeness and accuracy of WiKC. This could involve comparing the coverage of classes and relationships in WiKC with those in other well-established KGs, such as DBpedia or YAGO.

Application-Specific Evaluations: Implementing WiKC in specific downstream applications, such as entity recognition, entity linking, and semantic search, can provide practical insights into its effectiveness. Evaluating performance metrics such as precision, recall, and F1-score in these applications will help gauge the utility of WiKC in real-world scenarios.

User Studies and Feedback: Engaging with end-users and domain experts to gather feedback on the usability and relevance of WiKC in their specific contexts can provide valuable insights. User studies can help identify areas for improvement and validate the practical applicability of the refined taxonomy.

Integration with Machine Learning Models: Testing WiKC as a feature set in machine learning models for tasks like classification or recommendation can demonstrate its effectiveness. By measuring the performance improvements in these models when using WiKC compared to other taxonomies, its usefulness can be quantitatively assessed.

Longitudinal Studies: Conducting longitudinal studies to track the performance of applications utilizing WiKC over time can help assess its stability and adaptability to changing knowledge landscapes. This can provide insights into its long-term relevance and coverage.

By implementing these validation strategies, the knowledge coverage and practical usefulness of WiKC can be thoroughly assessed, ensuring its effectiveness across a wide range of applications and domains.