Alexandria: Unsupervised High-Precision Knowledge Base Construction using a Probabilistic Program
Creating a knowledge base that is accurate, up-to-date and complete remains a significant challenge despite substantial efforts in automated knowledge base construction. In this paper, we present Alexandria -- a system for unsupervised, high-precision knowledge base construction.Alexandria uses a probabilistic program to define a process of converting knowledge base facts into unstructured text. Using probabilistic inference, we can invert this program and so retrieve facts, schemas and entities from web text.The use of a probabilistic program allows uncertainty in the text to be propagated through to the retrieved facts, which increases accuracy and helps merge facts from multiple sources.Because Alexandria does not require labelled training data, knowledge bases can be constructed with the minimum of manual input.We demonstrate this by constructing a high precision knowledge base for people from a single seed fact.
This paper presents a system for unsupervised, high-precision knowledge base construction using a probabilistic program to define a process of converting knowledge base facts into unstructured text.Overview about existing knowledge base that is constructed with a probabilistic model, with the knowledge base construction approach evaluated against other knowledge base approaches YAGO2, NELL, Knowledge Vault, and DeepDive.This paper uses a probabilistic program describing the process by which facts describing entities can be realised in text and large number of web pages, to learn to perform fact extraction about people using a single seed fact.