Open AccessDynamic Domain Sizes in Temporal Probabilistic Relational Models
Author(s) -
Nils Finke,
Tanya Braun,
Marcel Gehrke,
Ralf Möller
Publication year - 2021
Publication title -
proceedings of the ... international florida artificial intelligence research society conference
Language(s) - English
Resource type - Journals
eISSN - 2334-0762
pISSN - 2334-0754
DOI - 10.32473/flairs.v34i1.128466
Subject(s) - computer science , probabilistic logic , relational model , relational database , set (abstract data type) , semantics (computer science) , representation (politics) , theoretical computer science , domain (mathematical analysis) , data mining , artificial intelligence , programming language , mathematics , mathematical analysis , politics , political science , law
Probabilistic dynamic relational models (PDRMs) allow for an expressive, yet sparse and efficient representation of uncertain temporal (dynamic) and relational information with a fixed (static) set of domain objects (entities). While for different points in time, information about objects may differ, the set of objects under consideration is the same for all time points in standard PDRMs. Motivated by examples from a logistics application, in this paper we extend the theory of PDRMs with dynamically changing sets of domain objects. The paper introduces the semantics of so-called PD2RMs and analyses model management as well as query answering problems and algorithms.