85 research outputs found
Lifted Variable Elimination for Probabilistic Logic Programming
Lifted inference has been proposed for various probabilistic logical
frameworks in order to compute the probability of queries in a time that
depends on the size of the domains of the random variables rather than the
number of instances. Even if various authors have underlined its importance for
probabilistic logic programming (PLP), lifted inference has been applied up to
now only to relational languages outside of logic programming. In this paper we
adapt Generalized Counting First Order Variable Elimination (GC-FOVE) to the
problem of computing the probability of queries to probabilistic logic programs
under the distribution semantics. In particular, we extend the Prolog Factor
Language (PFL) to include two new types of factors that are needed for
representing ProbLog programs. These factors take into account the existing
causal independence relationships among random variables and are managed by the
extension to variable elimination proposed by Zhang and Poole for dealing with
convergent variables and heterogeneous factors. Two new operators are added to
GC-FOVE for treating heterogeneous factors. The resulting algorithm, called
LP for Lifted Probabilistic Logic Programming, has been implemented by
modifying the PFL implementation of GC-FOVE and tested on three benchmarks for
lifted inference. A comparison with PITA and ProbLog2 shows the potential of
the approach.Comment: To appear in Theory and Practice of Logic Programming (TPLP). arXiv
admin note: text overlap with arXiv:1402.0565 by other author
Exploiting Uncertainty for Querying Inconsistent Description Logics Knowledge Bases
The necessity to manage inconsistency in Description Logics Knowledge
Bases~(KBs) has come to the fore with the increasing importance gained by the
Semantic Web, where information comes from different sources that constantly
change their content and may contain contradictory descriptions when considered
either alone or together. Classical reasoning algorithms do not handle
inconsistent KBs, forcing the debugging of the KB in order to remove the
inconsistency. In this paper, we exploit an existing probabilistic semantics
called DISPONTE to overcome this problem and allow queries also in case of
inconsistent KBs. We implemented our approach in the reasoners TRILL and BUNDLE
and empirically tested the validity of our proposal. Moreover, we formally
compare the presented approach to that of the repair semantics, one of the most
established semantics when considering DL reasoning tasks
Probabilistic inference in SWI-Prolog
Probabilistic Logic Programming (PLP) emerged as one of the most prominent approaches to cope with real-world domains. The distribution semantics is one of most used in PLP, as it is followed by many languages, such as Independent Choice Logic, PRISM, pD, Logic Programs with Annotated Disjunctions (LPADs) and ProbLog. A possible system that allows performing inference on LPADs is PITA, which transforms the input LPAD into a Prolog program containing calls to library predicates for handling Binary Decision Diagrams (BDDs). In particular, BDDs are used to compactly encode explanations for goals and efficiently compute their probability. However, PITA needs mode-directed tabling (also called tabling with answer subsumption), which has been implemented in SWI-Prolog only recently. This paper shows how SWI-Prolog has been extended to include correct answer subsumption and how the PITA transformation has been changed to use SWI-Prolog implementation
A Framework for Reasoning on Probabilistic Description Logics
While there exist several reasoners for Description Logics, very few of them
can cope with uncertainty. BUNDLE is an inference framework that can exploit
several OWL (non-probabilistic) reasoners to perform inference over
Probabilistic Description Logics.
In this chapter, we report the latest advances implemented in BUNDLE. In
particular, BUNDLE can now interface with the reasoners of the TRILL system,
thus providing a uniform method to execute probabilistic queries using
different settings. BUNDLE can be easily extended and can be used either as a
standalone desktop application or as a library in OWL API-based applications
that need to reason over Probabilistic Description Logics.
The reasoning performance heavily depends on the reasoner and method used to
compute the probability. We provide a comparison of the different reasoning
settings on several datasets
Probabilistic inductive constraint logic
AbstractProbabilistic logical models deal effectively with uncertain relations and entities typical of many real world domains. In the field of probabilistic logic programming usually the aim is to learn these kinds of models to predict specific atoms or predicates of the domain, called target atoms/predicates. However, it might also be useful to learn classifiers for interpretations as a whole: to this end, we consider the models produced by the inductive constraint logic system, represented by sets of integrity constraints, and we propose a probabilistic version of them. Each integrity constraint is annotated with a probability, and the resulting probabilistic logical constraint model assigns a probability of being positive to interpretations. To learn both the structure and the parameters of such probabilistic models we propose the system PASCAL for "probabilistic inductive constraint logic". Parameter learning can be performed using gradient descent or L-BFGS. PASCAL has been tested on 11 datasets and compared with a few statistical relational systems and a system that builds relational decision trees (TILDE): we demonstrate that this system achieves better or comparable results in terms of area under the precisionârecall and receiver operating characteristic curves, in a comparable execution time
a history of probabilistic inductive logic programming
The field of Probabilistic Logic Programming (PLP) has seen significant advances in the last 20âyears, with many proposals for languages that combine probability with logic programming. Since the start, the problem of learning probabilistic logic programs has been the focus of much attention. Learning these programs represents a whole subfield of Inductive Logic Programming (ILP). In Probabilistic ILP (PILP), two problems are considered: learning the parameters of a program given the structure (the rules) and learning both the structure and the parameters. Usually, structure learning systems use parameter learning as a subroutine. In this article, we present an overview of PILP and discuss the main results
Machine learning from real data: A mental health registry case study
Imbalanced datasets can impair the learning performance of many Machine Learning techniques. Nevertheless, many real-world datasets, especially in the healthcare field, are inherently imbalanced. For instance, in the medical domain, the classes representing a specific disease are typically the minority of the total cases. This challenge justifies the substantial research effort spent in the past decades to tackle data imbalance at the data and algorithm levels. In this paper, we describe the strategies we used to deal with an imbalanced classification task on data extracted from a database generated from the Electronic Health Records of the Mental Health Service of the Ferrara Province, Italy. In particular, we applied balancing techniques to the original data, such as random undersampling and oversampling, and Synthetic Minority Oversampling Technique for Nominal and Continuous (SMOTE-NC). In order to assess the effectiveness of the balancing techniques on the classification task at hand, we applied different Machine Learning algorithms. We employed cost-sensitive learning as well and compared its results with those of the balancing methods. Furthermore, a feature selection analysis was conducted to investigate the relevance of each feature. Results show that balancing can help find the best setting to accomplish classification tasks. Since real-world imbalanced datasets are increasingly becoming the core of scientific research, further studies are needed to improve already existing techniqu
Recommended from our members
Emergency Surgery in the Elderly: Could Laparoscopy Be Useful in Frailty? A Single-Center Prospective 2-Year Follow-Up in 120 Consecutive Patients
Background: the general population is aging across the world. Therefore, even surgical interventions in the elderlyâin particular those involving emergency surgical admissionsâare becoming more frequent. The elderly population is often frail (in multiple physiological systems, this is often defined as age-related cumulative decline). This study involved a 2-year follow-up evaluation of frail elderly patients treated with urgent surgical intervention at Santa Maria Regina della Misericordia Hospital, General Surgery Department, in Adria (Italy). Method: a prospective, single-center, 2-year follow-up study of 120 patients >65 years old, treated at our department for surgical abdominal emergencies. We considered co-morbidities (ASAâAmerican Society of Anesthesiologists Physical Status Classification Systemâscore), type of surgery (laparoscopy, laparotomy or converted), frailty score, mortality, and complications at 30 days and at 2 years. Conclusions: 70 (58.4%) patients had laparoscopy, 49 (40.8) had laparotomy, and in 1 (0.8%) case, surgery was converted from laparoscopy to laparotomy. Mortality strictly depends on the type of surgery (laparotomy vs. laparoscopy), complications during recovery, and a lower Fried frailty criteria score, on average. The long-term follow-up can be a useful tool to highlight a safer surgical approach, such as laparoscopy, in frail elderly patients. We consider the laparoscopic approach feasible in emergency situations, with similar or better outcomes than laparotomy, especially in frail elderly patients.</jats:p
Diversity and ethics in trauma and acute care surgery teams: results from an international survey
Background Investigating the context of trauma and acute care surgery, the article aims at understanding the factors that can enhance some ethical aspects, namely the importance of patient consent, the perceptiveness of the ethical role of the trauma leader, and the perceived importance of ethics as an educational subject. Methods The article employs an international questionnaire promoted by the World Society of Emergency Surgery. Results Through the analysis of 402 fully filled questionnaires by surgeons from 72 different countries, the three main ethical topics are investigated through the lens of gender, membership of an academic or non-academic institution, an official trauma team, and a diverse group. In general terms, results highlight greater attention paid by surgeons belonging to academic institutions, official trauma teams, and diverse groups. Conclusions Our results underline that some organizational factors (e.g., the fact that the team belongs to a university context or is more diverse) might lead to the development of a higher sensibility on ethical matters. Embracing cultural diversity forces trauma teams to deal with different mindsets. Organizations should, therefore, consider those elements in defining their organizational procedures. Level of evidence Trauma and acute care teams work under tremendous pressure and complex circumstances, with their members needing to make ethical decisions quickly. The international survey allowed to shed light on how team assembly decisions might represent an opportunity to coordinate team member actions and increase performance
- âŠ