2011/07/25 by Lilyana Mihalkova, Mihalkova, Lilyana, Lise Getoor +1 · 2 citations
Computer Science · Mathematics · #Algorithm #Artificial Intelligence (cs.AI) #Artificial intelligence #Bayesian Modeling and Causal Inference #Computer science #Data science #FOS: Computer and information sciences #Factor graph #Field (mathematics) #Formalism (music) #Graph #Graphical model #Inference #Information retrieval #Machine Learning (cs.LG) #Machine learning #Mathematics #Natural Language Processing Techniques #Point (geometry) #Probabilistic logic #Relational database #Statistical graphics #Statistical inference #Statistical model #Statistical relational learning #Theoretical computer science #Topic Modeling #cs.AI #cs.LG
paper · pdf · doi:10.48550/arxiv.1107.4966
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2011/07/25 · arxiv created 2011/08/26 · arxiv updated 2011/08/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
This article presents a survey of work on lifted graphical models. We review a general form for a lifted graphical model, a par-factor graph, and show how a number of existing statistical relational representations map to this formalism. We discuss inference algorithms, including lifted inference algorithms, that efficiently compute the answers to probabilistic queries. We also review work in learning lifted graphical models from data. It is our belief that the need for statistical relational models (whether it goes by that name or another) will grow in the coming decades, as we are inundated with data which is a mix of structured and unstructured, with entities and relations extracted in a noisy manner from text, and with the need to reason effectively with this data. We hope that this synthesis of ideas from many different research groups will provide an accessible starting point for new researchers in this expanding field.