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Recent Advances in Querying Probabilistic Knowledge Bases

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

doi:10.24963/ijcai.2018/765
dblp:conf/ijcai/BorgwardtCL18
fatcat:o43b3gn6sbgydncx55ryiqftde
*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. ...##
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Using Iterative Deepening for Probabilistic Logic Inference
[chapter]

2016
*
Lecture Notes in Computer Science
*

We present a novel approach that uses an iterative deepening algorithm

doi:10.1007/978-3-319-51676-9_14
fatcat:4zjk4pxzj5ctnduod7d4nl2hcq
*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*= ...##
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A Hybrid Approach to Inference in Probabilistic Non-Monotonic Logic Programming

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 ...

##
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MAP Inference for Probabilistic Logic Programming
[article]

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. ...

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

2011
*
Theory and Practice of Logic Programming
*

The

doi:10.1017/s147106841100010x
fatcat:uwvoqd7vpnbdhanhy53mqfj5cq
*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. ...##
###
Systems and Learning Algorithms for Probabilistic Logical Knowledge Bases

2016
*
International Conference of the Italian Association for Artificial Intelligence
*

learns new clauses of

dblp:conf/aiia/Cota16
fatcat:kt35dus56rh4dh3etl7j2hd7z4
*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 ...##
###
MCINTYRE: A Monte Carlo Algorithm for Probabilistic Logic Programming

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*. ...

##
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DeepStochLog: Neural Stochastic Logic Programming
[article]

2021
*
arXiv
*
pre-print

We show that inference and learning

arXiv:2106.12574v1
fatcat:4gllqnj2nzekdedkppg7hlsmo4
*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). ...##
###
The Most Probable Explanation for Probabilistic Logic Programs with Annotated Disjunctions
[chapter]

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*. ...

##
###
Debugging weighted ontologies

2013
*
Extended Semantic Web Conference
*

We present a reformulation of the problem as finding the

dblp:conf/esws/Stuckenschmidt13
fatcat:fnzijdmrqfaippfysisdj74lbm
*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 ...##
###
Probabilistic Semantics

2016
*
Procedia Computer Science
*

Indeed, while the progressive consolidation of Semantic Technology

doi:10.1016/j.procs.2016.05.472
fatcat:eagm6fi2vjcufnhcivmrlkur3e
*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). ...##
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Computing most probable worlds of action probabilistic logic programs: scalable estimation for 1030,000 worlds

2007
*
Annals of Mathematics and Artificial Intelligence
*

The semantics of

doi:10.1007/s10472-008-9089-2
fatcat:icbxcltowbathgdp55dfutpbve
*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] . ...##
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Constraint-Based Inference in Probabilistic Logic Programs

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. ...

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

2013
*
Lecture Notes in Computer Science
*

Over the past decade, the two research areas of

doi:10.1007/978-3-642-40683-6_1
fatcat:lofuquzqgbb4hcjtjeqydyakbe
*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. ...##
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From Statistical Relational to Neural Symbolic Artificial Intelligence: a Survey
[article]

2022
*
arXiv
*
pre-print

Neural-symbolic and statistical relational artificial intelligence both integrate frameworks for learning with

arXiv:2108.11451v2
fatcat:2vynob3s7bhsjk22pwv5e5hnta
*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. ...
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