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 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. 

Sibling Models, Categorical Outcomes, and the Intra-Class Correlation

2021 ◽  
Author(s):  
Richard Breen ◽  
John Ermisch
Keyword(s):  
Random Effects ◽  
Probability Model ◽  
Intraclass Correlation ◽  
Linear Probability Model ◽  
Intra Class Correlation ◽  
Sibling Differences ◽  
Sibling Models ◽  
Alternative Measures ◽  
Linear Probability ◽  
Parametric Estimate

Abstract In sibling models with categorical outcomes the question arises of how best to calculate the intraclass correlation, ICC. We show that, for this purpose, the random effects linear probability model is preferable to a random effects non-linear probability model, such as a logit or probit. This is because, for a binary outcome, the ICC derived from a random effects linear probability model is a non-parametric estimate of the ICC, equivalent to a statistic called Cohen’s κ. Furthermore, because κ can be calculated when the outcome has more than two categories, we can use the random effects linear probability model to compute a single ICC in cases with more than two outcome categories. Lastly, ICCs are often compared between groups to show the degree to which sibling differences vary between groups: we show that when the outcome is categorical these comparisons are invalid. We suggest alternative measures for this purpose.

Charged with Instability: Are Paternal Arrests and Criminal Charges Enough to Shake the Marital Stability of American Couples?

2020 ◽  
Vol 41 (12) ◽  
pp. 2423-2447
Author(s):  
Antonius D. Skipper ◽  
Douglas S. Bates ◽  
Zachary D. Blizard ◽  
Richard G. Moye
Keyword(s):  
Justice System ◽  
Probability Model ◽  
Well Being ◽  
Marital Stability ◽  
Fragile Families ◽  
Linear Probability Model ◽  
Paternal Incarceration ◽  
Criminal Charges ◽  
Linear Probability ◽  
The Relationship

With the growing rate of divorce, increasing efforts are being made to identify the factors that contribute to relationship dissolution for many American couples. One commonly noted, and particularly concerning, factor toward relationship instability is the incarceration of husbands and fathers. Although paternal incarceration and familial stability have been studied, little is known about the relationship between criminal charges and divorce. The current study utilized data from the Fragile Families and Child Wellbeing Study to understand the effect of paternal criminal charges on divorce for 725 families. Utilizing a logistic regression and two-stage least squares linear probability model, results show that, even without incarceration, being charged with a crime as a husband significantly increases the likelihood that a couple will get divorced. These findings have significant implications for understanding how encounters with the criminal justice system affect familial well-being and stability.

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