Wikidata

Integrating Knowledge Graphs (KGs) with Large Language Models (LLMs) is a well-explored research field. KGs are vast, structured databases storing factual associations as graph edges. KGs can help LLMs for tasks like question answering, drawing on the structured, often up-to-date information within KGs, thereby mitigating the risk of hallucination. For instance, an LLM that can query Wikidata—a prominent KG project—instead of solely depending on its training data becomes significantly more reliable and useful. [Read More]

Wikidata is a rich knowledge graph, but its raw data format can be challenging for both humans and AI to process effectively. This blog post explores how I addressed these challenges by creating qrender, a tool for rendering Wikidata items in more human-readable and AI-friendly formats. In my previous article about qjson, I explained the importance of retrieving all information about a Wikidata Item. I write qjsonas an easy API to fetch all such information in one API call instead of multiple SPARQL queries or API calls. [Read More]

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]