Wikidata

For those deeply involved with Wikidata, the richness of its interconnected data is both a blessing and a challenge when it comes to programmatic access. While the standard wbgetentities API endpoint is fundamental, retrieving the complete set of properties, including labels and values, for a given item often leads to a cascade of recursive API calls. For example, suppose we fetch all properties for Q42 using wbgetentities API - https://www.wikidata.org/w/api.php?action=wbgetentities&ids=Q42. In the response, if well lookup the “country of citizenship” (P27) for Q42 (Douglas Adams): the initial response only provides the target QID (Q145), necessitating further queries to resolve both P27 and Q145 into human-readable labels. [Read More]

This is a blog post version a paper titled “Question-to-Question Retrieval for Hallucination-Free Knowledge Access: An Approach for Wikipedia and Wikidata Question Answering” available at https://arxiv.org/abs/2501.11301. In the world of Large Language Models (LLMs) and question answering systems, hallucination - where models generate plausible but incorrect information - remains a significant challenge. This is particularly problematic when dealing with encyclopedic knowledge sources like Wikipedia, where accuracy is paramount. Today, I’ll discuss a novel approach that addresses this challenge through question-to-question retrieval. [Read More]