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Recent Advances in Querying Probabilistic Knowledge Bases

Stefan Borgwardt, İsmail İlkan Ceylan, Thomas Lukasiewicz
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
We give a survey on recent advances at the forefront of research on probabilistic knowledge bases for representing and querying large-scale automatically extracted data.  ...  We concentrate especially on increasing the semantic expressivity of formalisms for representing and querying probabilistic knowledge (i) by giving up the closed-world assumption, (ii) by allowing for  ...  There Open-World Assumption Most real-world probabilistic knowledge bases encode only a portion of the real world, and this description is, in most cases, incomplete.  ... 
doi:10.24963/ijcai.2018/765 dblp:conf/ijcai/BorgwardtCL18 fatcat:o43b3gn6sbgydncx55ryiqftde

Using Iterative Deepening for Probabilistic Logic Inference [chapter]

Theofrastos Mantadelis, Ricardo Rocha
2016 Lecture Notes in Computer Science  
We present a novel approach that uses an iterative deepening algorithm in order to perform probabilistic logic inference for ProbLog, a probabilistic extension of Prolog.  ...  Our experimental results show that our iterative deepening approach gets approximate bounded values in almost all cases and in most cases we are able to get the exact result for the same or one lower scaling  ...  The probability of a possible world (P world ) equals to the product of the probability of all probabilistic facts in L true and 1 -probability of all probabilistic facts in L f alse , i.e., P world =  ... 
doi:10.1007/978-3-319-51676-9_14 fatcat:4zjk4pxzj5ctnduod7d4nl2hcq

A Hybrid Approach to Inference in Probabilistic Non-Monotonic Logic Programming

Matthias Nickles, Alessandra Mileo
2015 International Conference on Logic Programming  
In contrast to traditional approaches to probabilistic Answer Set Programming (ASP), our framework imposes only comparatively little restrictions on probabilistic logic programs -in particular, it allows  ...  We present a probabilistic inductive logic programming framework which integrates non-monotonic reasoning, probabilistic inference and parameter learning.  ...  We build upon existing approaches in the area of probabilistic (inductive) logic programming in order to provide a new ASP-based probabilistic logic programming language and inference tool which combines  ... 
dblp:conf/iclp/NicklesM15 fatcat:s5yiq73325endpnru5lctb5rxe

MAP Inference for Probabilistic Logic Programming [article]

Elena Bellodi, Marco Alberti, Fabrizio Riguzzi, Riccardo Zese
2020 arXiv   pre-print
In Probabilistic Logic Programming (PLP) the most commonly studied inference task is to compute the marginal probability of a query given a program.  ...  evidence on other variables, and the Most Probable Explanation (MPE) task, the instance of MAP where the query variables are the complement of the evidence variables.  ...  Conclusions In this paper, we presented an algorithm to solve the Maximum-A-Posteriori (MAP) and the Most-Probable-Explanation (MPE) problems on Logic Programs with Annotated Disjunctions.  ... 
arXiv:2008.01394v3 fatcat:m6xaz5lzjjfi7icisgd6yjajfm

The PITA system: Tabling and answer subsumption for reasoning under uncertainty

FABRIZIO RIGUZZI, TERRANCE SWIFT
2011 Theory and Practice of Logic Programming  
The most common such representation is probability, and the combination of probability with logic programs has given rise to the field of Probabilistic Logic Programming (PLP), leading to languages such  ...  The complexity of computing the probability of queries to these general PLP programs is very high due to the need to combine the probabilities of explanations that may not be exclusive.  ...  Acknowledgements The authors thank Henning Christiansen for his help in validating the experimental results that use removal of non-discriminating arguments.  ... 
doi:10.1017/s147106841100010x fatcat:uwvoqd7vpnbdhanhy53mqfj5cq

Systems and Learning Algorithms for Probabilistic Logical Knowledge Bases

Giuseppe Cota
2016 International Conference of the Italian Association for Artificial Intelligence  
learns new clauses of Probabilistic Logic Programs, the latter is used in the context of Probabilistic Description Logics.  ...  The first described system is cplint on SWISH, a web application that allows the user to write Probabilistic Logic Programs and submit the computation of the probability of queries with a web browser.  ...  In the last decades several semantics where proposed to represent uncertainty, one of the most prominent approaches for representing probabilistic information in Logic Programming is the distribution semantics  ... 
dblp:conf/aiia/Cota16 fatcat:kt35dus56rh4dh3etl7j2hd7z4

MCINTYRE: A Monte Carlo Algorithm for Probabilistic Logic Programming

Fabrizio Riguzzi
2011 Italian Conference on Computational Logic  
In this paper we concentrate on the problem of approximate inference in probabilistic logic programming languages based on the distribution semantics.  ...  Probabilistic Logic Programming is receiving an increasing attention for its ability to model domains with complex and uncertain relations among entities.  ...  A program in one of these languages defines a probability distribution over normal logic programs called worlds.  ... 
dblp:conf/cilc/Riguzzi11 fatcat:rltmyhbj45c3bbowaqs6zgfiwi

DeepStochLog: Neural Stochastic Logic Programming [article]

Thomas Winters, Giuseppe Marra, Robin Manhaeve, Luc De Raedt
2021 arXiv   pre-print
We show that inference and learning in neural stochastic logic programming scale much better than for neural probabilistic logic programs.  ...  Like graphical models, these probabilistic logic programs define a probability distribution over possible worlds, for which inference is computationally hard.  ...  Most notably, one distinguishes probabilistic from stochastic logic programs (PLPs vs SLPs).  ... 
arXiv:2106.12574v1 fatcat:4gllqnj2nzekdedkppg7hlsmo4

The Most Probable Explanation for Probabilistic Logic Programs with Annotated Disjunctions [chapter]

Dimitar Shterionov, Joris Renkens, Jonas Vlasselaer, Angelika Kimmig, Wannes Meert, Gerda Janssens
2015 Lecture Notes in Computer Science  
Probabilistic logic languages, such as ProbLog and CP-logic, are probabilistic generalizations of logic programming that allow one to model probability distributions over complex, structured domains.  ...  This encoding is tailored towards the task of computing the marginal probability of a query given evidence (MARG), but is not correct for the task of finding the most probable explanation (MPE) with important  ...  Most PLP techniques extend logic programming languages (such as Prolog) with probabilities.  ... 
doi:10.1007/978-3-319-23708-4_10 fatcat:pw2kbf6yvnavhbbzglehmor6dm

Debugging weighted ontologies

Heiner Stuckenschmidt
2013 Extended Semantic Web Conference  
We present a reformulation of the problem as finding the most probable consistent ontology according to a log-linear model and show how existing methods from probabilistic reasoning can be adapted to our  ...  We define this problem as computing a consistent subontology with a maximal sum of axiom weights.  ...  Acknowledgement The work summarized in this abstract has been joint work with Christian Meilicke, Mathias Niepert and Jan Noessner  ... 
dblp:conf/esws/Stuckenschmidt13 fatcat:fnzijdmrqfaippfysisdj74lbm

Probabilistic Semantics

Salvatore F. Pileggi
2016 Procedia Computer Science  
Indeed, while the progressive consolidation of Semantic Technology in a wide context and on a large scale is going to be a fact, the non-deterministic character of many problems and environments suggests  ...  Probabilistic extensions and their implications to the current semantic ecosystems are discussed in this paper with an implicit focus on the Web and its evolution.  ...  Acknowledgments This research is supported in part by European FP7 project 609551 SyncFree (2013-2016).  ... 
doi:10.1016/j.procs.2016.05.472 fatcat:eagm6fi2vjcufnhcivmrlkur3e

Computing most probable worlds of action probabilistic logic programs: scalable estimation for 1030,000 worlds

Samir Khuller, M. Vanina Martinez, Dana Nau, Amy Sliva, Gerardo I. Simari, V. S. Subrahmanian
2007 Annals of Mathematics and Artificial Intelligence  
The semantics of probabilistic logic programs (PLPs) is usually given through a possible worlds semantics.  ...  In such applications, worlds correspond to sets of actions these entities might take. Thus, there is a need to find the most probable world (MPW) for ap-programs.  ...  Related work Probabilistic logic programming was introduced in [16, 17] and later studied by several authors [2, 3, 9, 11, 14] .  ... 
doi:10.1007/s10472-008-9089-2 fatcat:icbxcltowbathgdp55dfutpbve

Constraint-Based Inference in Probabilistic Logic Programs

ARUN NAMPALLY, TIMOTHY ZHANG, C. R. RAMAKRISHNAN
2018 Theory and Practice of Logic Programming  
In PLP, inference is performed by summarizing the possible worlds which entail the query in a suitable data structure, and using this data structure to compute the answer probability.  ...  AbstractProbabilistic Logic Programs (PLPs) generalize traditional logic programs and allow the encoding of models combining logical structure and uncertainty.  ...  Inference in Probabilistic Logic Programs goal.  ... 
doi:10.1017/s1471068418000273 fatcat:do7j3wqo6fh67ekjuxsj4ylxke

10 Years of Probabilistic Querying – What Next? [chapter]

Martin Theobald, Luc De Raedt, Maximilian Dylla, Angelika Kimmig, Iris Miliaraki
2013 Lecture Notes in Computer Science  
Over the past decade, the two research areas of probabilistic databases and probabilistic programming have intensively studied the problem of making structured probabilistic inference scalable, but-so  ...  While probabilistic databases have focused on describing tractable query classes based on the structure of query plans and data lineage, probabilistic programming has contributed sophisticated inference  ...  PP approaches combine a logic program with probabilistic facts, and are thus closely related to PDBs that associate probabilities to tuples.  ... 
doi:10.1007/978-3-642-40683-6_1 fatcat:lofuquzqgbb4hcjtjeqydyakbe

From Statistical Relational to Neural Symbolic Artificial Intelligence: a Survey [article]

Giuseppe Marra and Sebastijan Dumančić and Robin Manhaeve and Luc De Raedt
2022 arXiv   pre-print
Neural-symbolic and statistical relational artificial intelligence both integrate frameworks for learning with logical reasoning.  ...  Probabilistic logic uses weighted logic programs or theories to define probability distributions over the possible worlds, i.e. p(ω).  ...  Probabilistic logic programs are essentially definite clause programs where every fact is annotated with the probability that it is True. This then results in a possible world semantics.  ... 
arXiv:2108.11451v2 fatcat:2vynob3s7bhsjk22pwv5e5hnta
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