scholarly journals Open-World Probabilistic Databases: An Abridged Report

2017 ◽  
Author(s):  
Ismail Ilkan Ceylan ◽  
Adnan Darwiche ◽  
Guy Van den Broeck
Keyword(s):  
Large Scale ◽  
Knowledge Bases ◽  
Probabilistic Databases ◽  
Open World ◽  
Probabilistic Database ◽  
Closed World ◽  
Complexity Dichotomy ◽  
Probabilistic Knowledge ◽  
Probability Zero ◽  
Evaluation Algorithm

Large-scale probabilistic knowledge bases are becoming increasingly important in academia and industry alike. They are constantly extended with new data, powered by modern information extraction tools that associate probabilities with database tuples. In this paper, we revisit the semantics underlying such systems. In particular, the closed-world assumption of probabilistic databases, that facts not in the database have probability zero, clearly conflicts with their everyday use. To address this discrepancy, we propose an open-world probabilistic database semantics, which relaxes the probabilities of open facts to default intervals. For this open-world setting, we lift the existing data complexity dichotomy of probabilistic databases, and propose an efficient evaluation algorithm for unions of conjunctive queries. We also show that query evaluation can become harder for non-monotone queries.

scholarly journals On Constrained Open-World Probabilistic Databases

2019 ◽  
Author(s):  
Tal Friedman ◽  
Guy Van den Broeck
Keyword(s):  
Large Class ◽  
Recent Work ◽  
Large Scale ◽  
Knowledge Bases ◽  
Probabilistic Databases ◽  
Open World ◽  
Probabilistic Database ◽  
Probabilistic Knowledge ◽  
Reasonable Answers

Increasing amounts of available data have led to a heightened need for representing large-scale probabilistic knowledge bases. One approach is to use a probabilistic database, a model with strong assumptions that allow for efficiently answering many interesting queries. Recent work on open-world probabilistic databases strengthens the semantics of these probabilistic databases by discarding the assumption that any information not present in the data must be false. While intuitive, these semantics are not sufficiently precise to give reasonable answers to queries. We propose overcoming these issues by using constraints to restrict this open world. We provide an algorithm for one class of queries, and establish a basic hardness result for another. Finally, we propose an efficient and tight approximation for a large class of queries. 

scholarly journals Ontology-Mediated Query Answering over Log-Linear Probabilistic Data

2019 ◽  
Vol 33 ◽  
pp. 2711-2718 ◽  
Author(s):  
Stefan Borgwardt ◽  
İsmail İlkan Ceylan ◽  
Thomas Lukasiewicz
Keyword(s):  
Data Model ◽  
Large Scale ◽  
Probability Model ◽  
Knowledge Bases ◽  
Query Answering ◽  
Probabilistic Databases ◽  
Linear Probability Model ◽  
Probabilistic Database ◽  
Log Linear ◽  
Linear Probability

Large-scale knowledge bases are at the heart of modern information systems. Their knowledge is inherently uncertain, and hence they are often materialized as probabilistic databases. However, probabilistic database management systems typically lack the capability to incorporate implicit background knowledge and, consequently, fail to capture some intuitive query answers. Ontology-mediated query answering is a popular paradigm for encoding commonsense knowledge, which can provide more complete answers to user queries. We propose a new data model that integrates the paradigm of ontology-mediated query answering with probabilistic databases, employing a log-linear probability model. We compare our approach to existing proposals, and provide supporting computational results.

scholarly journals Recent Advances in Querying Probabilistic Knowledge Bases

2018 ◽  
Author(s):  
Stefan Borgwardt ◽  
İsmail İlkan Ceylan ◽  
Thomas Lukasiewicz
Keyword(s):  
Large Scale ◽  
Knowledge Bases ◽  
Closed Domain ◽  
Independence Assumption ◽  
Commonsense Knowledge ◽  
Closed World Assumption ◽  
Recent Advances ◽  
Closed World ◽  
Probabilistic Knowledge ◽  
Computational Properties

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 commonsense knowledge (and in parallel giving up the tuple-independence assumption), and (iii) by giving up the closed-domain assumption, while preserving some computational properties of query answering in such formalisms.

scholarly journals Well-founded operators for normal hybrid MKNF knowledge bases

2017 ◽  
Vol 17 (5-6) ◽  
pp. 889-905
Author(s):  
JIANMIN JI ◽  
FANGFANG LIU ◽  
JIA-HUAI YOU
Keyword(s):  
Artificial Intelligence ◽  
Search Engine ◽  
Knowledge Bases ◽  
Rule Based ◽  
Open World ◽  
Closed World ◽  
Ontology Languages ◽  
Inference Methods ◽  
Partial Partitions ◽  
Normal Hybrid

AbstractHybrid MKNF knowledge bases have been considered one of the dominant approaches to combining open world ontology languages with closed world rule-based languages. Currently, the only known inference methods are based on the approach of guess-and-verify, while most modern SAT/ASP solvers are built under the DPLL architecture. The central impediment here is that it is not clear what constitutes a constraint propagator, a key component employed in any DPLL-based solver. In this paper, we address this problem by formulating the notion of unfounded sets for non-disjunctive hybrid MKNF knowledge bases, based on which we propose and study two new well-founded operators. We show that by employing a well-founded operator as a constraint propagator, a sound and complete DPLL search engine can be readily defined. We compare our approach with the operator based on the alternating fixpoint construction by Knorr et al. (2011. Artificial Intelligence 175, 9, 1528–1554) and show that, when applied to arbitrary partial partitions, the new well-founded operators not only propagate more truth values but also circumvent the non-converging behavior of the latter. In addition, we study the possibility of simplifying a given hybrid MKNF knowledge base by employing a well-founded operator and show that, out of the two operators proposed in this paper, the weaker one can be applied for this purpose and the stronger one cannot. These observations are useful in implementing a grounder for hybrid MKNF knowledge bases, which can be applied before the computation of MKNF models.

scholarly journals Unfounded Sets for Disjunctive Hybrid MKNF Knowledge Bases

2021 ◽  
Author(s):  
Spencer Killen ◽  
Jia-Huai You
Keyword(s):  
Original Work ◽  
Answer Set Programming ◽  
Constraint Propagation ◽  
Knowledge Bases ◽  
Logic Programs ◽  
Complexity Bounds ◽  
Open World ◽  
Closed World ◽  
Disjunctive Logic Programs ◽  
Lower Complexity

Combining the closed-world reasoning of answer set programming (ASP) with the open-world reasoning of ontologies broadens the space of applications of reasoners. Disjunctive hybrid MKNF knowledge bases succinctly extend ASP and in some cases without increasing the complexity of reasoning tasks. However, in many cases, solver development is lagging behind. As the result, the only known method of solving disjunctive hybrid MKNF knowledge bases is based on guess-and-verify, as formulated by Motik and Rosati in their original work. A main obstacle is understanding how constraint propagation may be performed by a solver, which, in the context of ASP, centers around the computation of \textit{unfounded atoms}, the atoms that are false given a partial interpretation. In this work, we build towards improving solvers for hybrid MKNF knowledge bases with disjunctive rules: We formalize a notion of unfounded sets for these knowledge bases, identify lower complexity bounds, and demonstrate how we might integrate these developments into a DPLL-based solver. We discuss challenges introduced by ontologies that are not present in the development of solvers for disjunctive logic programs, which warrant some deviations from traditional definitions of unfounded sets. We compare our work with prior definitions of unfounded sets.

Personalization and rule strategies in data-intensive intelligent context-aware systems

2015 ◽  
Vol 30 (2) ◽  
pp. 140-156 ◽  
Author(s):  
Philip T. Moore ◽  
Hai V. Pham
Keyword(s):  
Large Scale ◽  
Ad Hoc ◽  
Contextual Information ◽  
Context Aware ◽  
Context Processing ◽  
Data Intensive ◽  
Open World ◽  
Closed World ◽  
Rule Based Approach ◽  
The Relationship

AbstractThe concept of personalization in its many forms has gained traction driven by the demands of computer-mediated interactions generally implemented in large-scale distributed systems and ad hoc wireless networks. Personalization requires the identification and selection of entities based on a defined profile (a context); an entity has been defined as a person, place, or physical or computational object. Context employs contextual information that combines to describe an entities current state. Historically, the range of contextual information utilized (in context-aware systems) has been limited to identity, location, and proximate data; there has, however, been advances in the range of data and information addressed. As such, context can be highly dynamic with inherent complexity. In addition, context-aware systems must accommodate constraint satisfaction and preference compliance.This article addresses personalization and context with consideration of the domains and systems to which context has been applied and the nature of the contextual data. The developments in computing and service provision are addressed with consideration of the relationship between the evolving computing landscape and context. There is a discussion around rule strategies and conditional relationships in decision support. Logic systems are addressed with an overview of the open world assumption versus the closed world assumption and the relationship with the Semantic Web. The event-driven rule-based approach, which forms the basis upon which intelligent context processing can be realized, is presented with an evaluation and proof-of-concept. The issues and challenges identified in the research are considered with potential solutions and research directions; alternative approaches to context processing are discussed. The article closes with conclusions and open research questions.

scholarly journals A goal-directed implementation of query answering for hybrid MKNF knowledge bases

2013 ◽  
Vol 14 (2) ◽  
pp. 239-264 ◽  
Author(s):  
ANA SOFIA GOMES ◽  
JOSÉ JÚLIO ALFERES ◽  
TERRANCE SWIFT
Keyword(s):  
Knowledge Bases ◽  
Loosely Coupled ◽  
Open World ◽  
Closed World ◽  
Practical Usefulness ◽  
Driven System ◽  
Hybrid Knowledge ◽  
Tightly Coupled ◽  
Description Framework

AbstractOntologies and rules are usually loosely coupled in knowledge representation formalisms. In fact, ontologies use open-world reasoning, while the leading semantics for rules use non-monotonic, closed-world reasoning. One exception is the tightly coupled framework of Minimal Knowledge and Negation as Failure (MKNF), which allows statements about individuals to be jointly derived via entailment from ontology and inferences from rules. Nonetheless, the practical usefulness of MKNF has not always been clear, although recent work has formalized a general resolution-based method for querying MKNF when rules are taken to have the well-founded semantics, and the ontology is modeled by a general oracle. That work leaves open what algorithms should be used to relate the entailments of the ontology and the inferences of rules. In this paper we provide such algorithms, and describe the implementation of a query-driven system, CDF-Rules, for hybrid knowledge bases combining both (non-monotonic) rules under the well-founded semantics and a (monotonic) ontology, represented by the Coherent Description Framework Type-1 ($\mathcal{ALCQ}$) theory.

scholarly journals Most Probable Explanations for Probabilistic Database Queries

2017 ◽  
Author(s):  
İsmail İlkan Ceylan ◽  
Stefan Borgwardt ◽  
Thomas Lukasiewicz
Keyword(s):  
Graphical Models ◽  
Large Scale ◽  
Complexity Analysis ◽  
Relevant Information ◽  
Knowledge Bases ◽  
Query Languages ◽  
Query Evaluation ◽  
Database Queries ◽  
Probabilistic Database ◽  
Probabilistic Query

Forming the foundations of large-scale knowledge bases, probabilistic databases have been widely studied in the literature. In particular, probabilistic query evaluation has been investigated intensively as a central inference mechanism. However, despite its power, query evaluation alone cannot extract all the relevant information encompassed in large-scale knowledge bases. To exploit this potential, we study two inference tasks; namely finding the most probable database and the most probable hypothesis for a given query. As natural counterparts of most probable explanations (MPE) and maximum a posteriori hypotheses (MAP) in probabilistic graphical models, they can be used in a variety of applications that involve prediction or diagnosis tasks. We investigate these problems relative to a variety of query languages, ranging from conjunctive queries to ontology-mediated queries, and provide a detailed complexity analysis.

scholarly journals Yield Prediction for Tomato Greenhouse Using EFuNN

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Kefaya Qaddoum ◽  
E. L. Hines ◽  
D. D. Iliescu
Keyword(s):  
Large Scale ◽  
Management Practices ◽  
Intelligent System ◽  
Vapour Pressure Deficit ◽  
Knowledge Bases ◽  
Yield Prediction ◽  
Tomato Yield ◽  
Fuzzy Neural ◽  
Rule Sets ◽  
Human Operators

In the area of greenhouse operation, yield prediction still relies heavily on human expertise. This paper proposes an automatic tomato yield predictor to assist the human operators in anticipating more effectively weekly fluctuations and avoid problems of both overdemand and overproduction if the yield cannot be predicted accurately. The parameters used by the predictor consist of environmental variables inside the greenhouse, namely, temperature, CO2, vapour pressure deficit (VPD), and radiation, as well as past yield. Greenhouse environment data and crop records from a large scale commercial operation, Wight Salads Group (WSG) in the Isle of Wight, United Kingdom, collected during the period 2004 to 2008, were used to model tomato yield using an Intelligent System called “Evolving Fuzzy Neural Network” (EFuNN). Our results show that the EFuNN model predicted weekly fluctuations of the yield with an average accuracy of 90%. The contribution suggests that the multiple EFUNNs can be mapped to respective task-oriented rule-sets giving rise to adaptive knowledge bases that could assist growers in the control of tomato supplies and more generally could inform the decision making concerning overall crop management practices.

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