Theophile Cantelo has created Foudinge, a knowledge graph connecting restaurants and chefs. Leveraging Large Language Models (LLMs), Foudinge extracts information from various online sources like blogs, guides, and social media to establish relationships between culinary professionals and the establishments they've worked at or own. This allows for complex queries, such as finding all restaurants where a specific chef has worked, discovering connections between different chefs through shared work experiences, and exploring the culinary lineage within the restaurant industry. Currently focused on French gastronomy, the project aims to expand its scope geographically and improve data accuracy through community contributions and additional data sources.
Théophile Cantelobre has introduced "Foudinge," a novel knowledge graph specifically focused on the culinary world, encompassing restaurants and chefs. This project leverages the power of Large Language Models (LLMs) to construct and populate the graph with information extracted from diverse online sources. Cantelobre details the process of building Foudinge, highlighting the challenges and solutions encountered along the way.
Initially, the project aimed to be a comprehensive database of French gastronomy, but it quickly evolved into a more generalized platform capable of representing culinary knowledge globally. The core of Foudinge lies in its ability to identify and link entities such as restaurants and chefs, establishing relationships between them like "Chef X works at Restaurant Y." This linking process is automated using LLMs, which analyze textual data from sources like restaurant websites, blogs, news articles, and social media platforms. This automated approach allows Foudinge to scale rapidly and incorporate information from a vast range of online resources.
The construction of Foudinge involved several key steps. First, an initial dataset was compiled, encompassing various data points related to restaurants and chefs. This data was then processed using LLMs to extract relevant information and transform it into a structured format suitable for a knowledge graph. The LLMs were instrumental in identifying and disambiguating entities, ensuring that the same chef or restaurant is represented consistently across different sources. Furthermore, the LLMs helped to infer relationships between entities based on the contextual information available in the source material.
Cantelobre acknowledges the inherent challenges of working with LLMs, such as potential biases in the training data and occasional inaccuracies in the generated output. To mitigate these challenges, Foudinge incorporates a validation process involving both automated checks and manual review. This iterative refinement process ensures the accuracy and reliability of the knowledge graph.
The long-term vision for Foudinge is to become a valuable resource for culinary enthusiasts, professionals, and researchers. Its structured data and interconnectedness allow for complex queries and analyses, enabling users to explore the culinary landscape in novel ways. For instance, one could trace the career trajectory of a chef, identify restaurants with similar culinary styles, or investigate the influence of specific chefs on regional cuisines. Cantelobre envisions Foudinge as a dynamic and evolving platform, continuously incorporating new information and expanding its coverage of the culinary world. He invites feedback and contributions from the community to further enhance the project and maximize its potential.
Summary of Comments ( 16 )
https://news.ycombinator.com/item?id=43242818
Hacker News users generally expressed skepticism about the value proposition of the presented knowledge graph of restaurants and chefs. Several commenters questioned the accuracy and completeness of the data, especially given its reliance on LLMs. Some doubted the usefulness of connecting chefs to restaurants without further context, like the time period they worked there. Others pointed out the existing prevalence of this information on platforms like Wikipedia and guide sites, questioning the need for a new platform. The lack of a clear use case beyond basic information retrieval was a recurring theme, with some suggesting potential applications like tracking career progression or identifying emerging culinary trends, but ultimately finding the current implementation insufficient. A few commenters appreciated the technical effort, but overall the reception was lukewarm, focused on the need for demonstrable practical application and improved data quality.
The Hacker News post titled "Show HN: Knowledge graph of restaurants and chefs, built using LLMs" generated a moderate amount of discussion, with a focus on the practical application and potential limitations of the project.
Several commenters expressed interest in the project's potential, particularly regarding its use for restaurant recommendations. One commenter highlighted the difficulty of finding good restaurants in unfamiliar cities and suggested the knowledge graph could be helpful in this scenario, particularly if it allowed users to filter by cuisine type and other specific criteria. They also inquired about the possibility of incorporating user reviews or ratings into the system.
Another user echoed this sentiment, pointing out that existing restaurant recommendation platforms often rely on outdated or inaccurate information. They envisioned the project as a valuable tool for both diners and restaurant owners, providing a centralized and up-to-date resource for restaurant information.
However, some commenters expressed concerns about the project's reliance on LLMs. One commenter pointed out the potential for hallucinations and inaccuracies in LLM-generated data, emphasizing the importance of thorough verification and fact-checking. They also questioned the long-term viability of relying solely on LLMs for data collection and maintenance, suggesting that a more robust approach might involve incorporating human input and curation.
The creator of the project engaged with the commenters, acknowledging the challenges of LLM-based data generation and outlining plans to address these concerns. They mentioned plans to implement a feedback mechanism to flag inaccurate information and explore methods for verifying the accuracy of LLM-generated data. They also discussed potential future features, such as incorporating user reviews, dietary information, and real-time menu updates.
A recurring theme in the comments was the need for a practical application or interface for the knowledge graph. Commenters suggested various use cases, including a dedicated search engine for restaurants, a mobile app for on-the-go recommendations, and integration with existing restaurant platforms.
Finally, one commenter raised a broader point about the ethical implications of using LLMs to scrape data from the web, questioning the potential impact on website owners and the overall ecosystem of online information. This sparked a brief discussion about the responsible use of LLMs and the importance of respecting website terms of service. While not directly related to the project itself, this comment highlighted the broader ethical considerations surrounding LLM-driven data collection.