arXiv:2409.04056v1 Announce Type: new Abstract: 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. Manual efforts to clean up this taxonomy are time-consuming and prone to errors or subjective decisions. We present WiKC, a new version of Wikidata taxonomy cleaned automatically using a combination of Large Language Models (LLMs) and graph mining techniques. Operations on the taxonomy, such as cutting links or merging classes, are performed with the help of zero-shot prompting on an open-source LLM. 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.
The article “WiKC: Cleaning up Wikidata Taxonomy with Large Language Models and Graph Mining” addresses the challenges associated with the complex taxonomy of Wikidata, including issues of ambiguity, inaccuracy, cycles, and redundancy. Manual efforts to clean up this taxonomy are time-consuming and subjective, leading to errors. To address this, the authors introduce WiKC, a new version of the Wikidata taxonomy that is automatically cleaned using a combination of Large Language Models (LLMs) and graph mining techniques. The taxonomy operations, such as cutting links or merging classes, are performed with the assistance of zero-shot prompting on an open-source LLM. The refined taxonomy is evaluated from intrinsic and extrinsic perspectives, demonstrating its practical value in entity typing tasks.
Transforming Wikidata: Introducing WiKC’s Innovative Solution
Wikidata, known for its collaborative nature, has established itself as a valuable resource in the realm of knowledge sharing. However, its taxonomy has proven to be a complex web, plagued with recurring issues such as confusion between instances and classes, inaccuracies in taxonomic paths, cycles, and an abundance of redundant classes. The manual efforts to clean up this taxonomy are time-consuming and often lead to errors or subjective decisions. Enter WiKC – the innovative solution that revitalizes Wikidata’s taxonomy automatically, combining Large Language Models (LLMs) with graph mining techniques.
WiKC offers a revolutionary approach to tackle the challenges of Wikidata’s taxonomy. By leveraging the power of LLMs, WiKC taps into the capabilities of cutting-edge models that are trained on vast amounts of text data. These models excel at understanding language and context, making them ideal candidates for addressing the intricate taxonomy of Wikidata.
The Process of WiKC
The process behind WiKC involves a combination of LLMs and graph mining techniques. The first step is to utilize the LLMs to automatically clean up the existing taxonomy. By leveraging the language understanding abilities of LLMs, WiKC can identify and rectify instances where classes and instances have been incorrectly assigned or where inaccuracies in taxonomic paths exist.
To further enhance the taxonomy, graph mining techniques are employed. These techniques analyze the structure of the taxonomy graph, detecting cycles and redundancies. By identifying and addressing these issues, WiKC ensures a more streamlined and accurate taxonomy, free from inconsistencies that hinder the effectiveness of Wikidata.
The Power of Zero-Shot Prompting
One of the most remarkable features of WiKC is its utilization of zero-shot prompting on an open-source LLM. This powerful technique allows WiKC to perform operations on the taxonomy, such as cutting links or merging classes, without the need for explicit instructions. Instead, WiKC relies on its ability to prompt the LLM with context and receive intelligent responses, expanding its capabilities beyond the limitations of traditional methods.
With zero-shot prompting, WiKC can tackle intricate tasks within the taxonomy, making data-driven decisions and modifications. This eliminates the subjectivity and potential errors that may arise from manual efforts, providing a more reliable and efficient solution for refining Wikidata’s taxonomy.
The Evaluation and Practical Impact of WiKC
The refined taxonomy produced by WiKC undergoes rigorous evaluation from both intrinsic and extrinsic perspectives. Intrinsic evaluation focuses on assessing the quality of the taxonomy independently, taking into account factors such as consistency, accuracy, and logical structure. Extrinsic evaluation examines the practical impact of the refined taxonomy, such as its efficacy in entity typing tasks.
The results of the evaluation demonstrate the practical interest and benefits of WiKC. With a refined taxonomy, the entity typing task becomes more efficient and accurate, enhancing the overall usability and reliability of Wikidata. By addressing the intricacies of the taxonomy, WiKC empowers users to extract knowledge effectively and contribute to a more coherent and structured knowledge base.
WiKC revolutionizes the way we clean up and refine Wikidata’s taxonomy. By harnessing the power of LLMs and graph mining techniques, WiKC provides an innovative, automated solution that saves time, eliminates errors, and unlocks the true potential of Wikidata’s knowledge sharing capabilities. With WiKC, the future of Wikidata’s taxonomy is streamlined, accurate, and optimized for a seamless knowledge sharing experience.
The paper titled “WiKC: Cleaning up Wikidata Taxonomy with Large Language Models and Graph Mining” addresses the challenges associated with the complex taxonomy of Wikidata. Wikidata, being a collaborative platform, often faces issues such as ambiguity between instances and classes, inaccurate taxonomic paths, cycles, and redundancy across classes. These problems require manual efforts to clean up the taxonomy, which are time-consuming and prone to errors or subjective decisions.
To overcome these challenges, the authors propose WiKC, a new version of Wikidata taxonomy that is cleaned automatically using a combination of Large Language Models (LLMs) and graph mining techniques. LLMs have shown remarkable performance in various natural language processing tasks, and their application to cleaning up Wikidata’s taxonomy is a novel approach.
WiKC leverages the power of LLMs to perform operations on the taxonomy, such as cutting links or merging classes. This is achieved through zero-shot prompting, where the LLM is trained to respond to prompts related to taxonomy operations, despite not being explicitly trained on this specific task. By utilizing zero-shot prompting, the authors are able to make the LLM assist in the taxonomy cleaning process, reducing the reliance on manual efforts.
The quality of the refined taxonomy is evaluated from both intrinsic and extrinsic perspectives. Intrinsic evaluation involves examining the taxonomy’s internal properties, such as the absence of cycles or the reduction of redundancy. Extrinsic evaluation, on the other hand, focuses on a practical task of entity typing, where the refined taxonomy is used to classify entities. The results of the evaluation demonstrate the practical interest and effectiveness of WiKC in improving the taxonomy’s quality.
Overall, this paper presents an innovative approach to addressing the challenges associated with the complex taxonomy of Wikidata. By combining LLMs and graph mining techniques, WiKC offers an automated solution that reduces the manual effort and subjective decisions involved in cleaning up the taxonomy. The evaluation results highlight the potential of WiKC in enhancing both the intrinsic properties of the taxonomy and its practical usability in entity typing tasks. Future directions could involve further refining the methodology, exploring additional applications for the refined taxonomy, and addressing any limitations or potential biases introduced by the use of LLMs.
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