1,720,977 research outputs found
Optimizing a tableau reasoner and its implementation in Prolog
No full textOne of the foremost reasoning services for knowledge bases is finding all the justifications for a query. This is useful for debugging purpose and for coping with uncertainty. Among Description Logics (DLs) reasoners, the tableau algorithm is one of the most used. However, in order to collect the justifications, the reasoners must manage the non-determinism of the tableau method. For these reasons, a Prolog implementation can facilitate the management of such non-determinism. The TRILL framework contains three probabilistic reasoners written in Prolog: TRILL, TRILLP and TORNADO. Since they are all part of the same framework, the choice about which to use can be done easily via the framework settings. Each one of them uses different approaches for probabilistic inference and handles different DLs flavors. Our previous work showed that they can sometimes achieve better results than state-of-the-art (non-)probabilistic reasoners. In this paper we present two optimizations that improve the performances of the TRILL reasoners. The first one consists into identifying the fragment of the KB that allows to perform inference without losing the completeness. The second one modifies which tableau rule to apply and their order of application, in order to reduce the number of operations. Experimental results show the effectiveness of the introduced optimizations
THERE: Toward an easy and reliable tool for automatic cephalometric analysis
No full textBackground: Cephalometric analysis in orthodontics is a meticulous process requiring high precision in identifying anatomical landmarks on lateral cephalometric radiographs. Manual analysis by clinicians remains the standard, as even slight deviations in landmark placement can lead to incorrect diagnoses. Despite advancements in AI, automatically localizing these landmarks remains challenging, with few systems meeting the exacting standards of European orthodontic practice. Methods: We developed THERETransKey, a model designed for the automatic and accurate localization of anatomical landmarks in cephalometric analysis. To evaluate its practical utility, we integrated the model into THERE (auTomatic HElpeR for cEphalometry), an open-access, user-friendly AI-based tool for cephalometric tracing. Unique in its strict adherence to the guidelines of the European Board of Orthodontics, THERE provides immediate online access and continuously collects user data to improve its performance. The tool has been validated by users with different levels of experience in Orthodontics using a PSSUQ-based questionnaire. Results: THERETransKey has shown enhanced accuracy with respect the previous models underlying THERE, as confirmed by its integration into daily clinical workflows at the University of Ferrara. Moreover, user feedback collected through the administered questionnaire confirms THERE's improved usability. Finally, its regular use enables the generation of a clinician-validated dataset during everyday practice. Conclusions: By effectively supporting clinicians, THERE not only enhances cephalometric analysis but also contributes to building a robust dataset for future research. This initiative promotes a more generalized approach to automatic landmark detection across diverse radiographic equipment
A web application for reasoning on probabilistic description logics knowledge bases
No full textThe aim of the Semantic Web is making information and resources from the Web automatically processable by machines. Usually, the uncertainty characterizing much of this information is addressed by means of a probabilistic semantics. Following the vision of a “Probabilistic Semantic Web”, a plethora of probabilistic semantics have been proposed: some of them change the syntax and/or the semantics itself of the knowledge representation language, others allow one to annotate axioms of a knowledge base with a probability value. Among the latter, the DISPONTE semantics exploits probabilistic annotations to extend query answering with the capability of returning the probability of a query being true in a domain. In order to promote the adoption of Probabilistic Semantic Web we first developed BUNDLE, a framework that can exploit different underlying (probabilistic and non-probabilistic) reasoners to perform probabilistic inference under the DISPONTE semantics. In this paper we present a web application for BUNDLE, to show how DISPONTE is easily usable even in already established applications and systems. It allows users to query a DISPONTE knowledge base written or uploaded directly in the application interface by using just a web browser, without the need to install any software on their machine. It is accessible on the web at https://bundle.ml.unife.it/ and also provides some examples for familiarizing with the application. The results of a usability evaluation involving human participants are also reported, showing the relevance and the practical impact of the tool and possible ways for improvement
MAP Inference for Probabilistic Logic Programming
No full textIn Probabilistic Logic Programming (PLP) the most commonly studied inference task is to compute the marginal probability of a query given a program. In this paper, we consider two other important tasks in the PLP setting: The Maximum-A-Posteriori (MAP) inference task, which determines the most likely values for a subset of the random variables given 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. We present a novel algorithm, included in the PITA reasoner, which tackles these tasks by representing each problem as a Binary Decision Diagram and applying a dynamic programming procedure on it. We compare our algorithm with the version of ProbLog that admits annotated disjunctions and can perform MAP and MPE inference. Experiments on several synthetic datasets show that PITA outperforms ProbLog in many cases
Neural Network Techniques for Detecting Intra-Domestic Water Leaks of Different Magnitude
No full textNowadays, water leak control at different levels is a necessary tool for sustainable water resource management. Research shows that more than one third of the world's drinking water is lost during its transfer to users, and that leakages on users' properties vary between 2 and 13% of total residential water demand, are very frequent and difficult to detect. Thanks to the advances in Internet of Things solutions for smart metering devices, it is possible to gather household water consumption information with high spatial and temporal resolution and to analyse them. This article applies several supervised Machine Learning (ML) techniques for the automatic detection of leakages of different magnitudes - even smaller than the meter sensitivity - in pipes within the dwelling, by using data collected by smart meters installed at the connection of users to the distribution network in an Italian town. The results obtained are compared with the performance of an 'empirical algorithm' previously presented by the authors, able to automatically identify leakages by checking if the hourly flow rate is never zero during the whole day, but not able to distinguish the size of the leakages. Experimental results over about 40,500 records show that ML techniques significantly improve the detection performance both in discriminating between presence and absence of leakages and in discriminating different-size leakages
Predicting the impact of public events and mobility in Smart Cities
Full text linkThe ubiquitous presence of smartphones and the ever-expanding Internet of Things are generating a treasure trove of data on human movement. We harness the power of Artificial Intelligence to extract knowledge within this data, in particular for predicting people flows and density in a Smart City. This predictive ability holds immense potential for a multitude of applications, from optimising people flow to streamlining event planning, while offering a powerful tool for pre-emptive identification of situations that may lead to crowd disasters. In this paper, we tackle two crucial aspects of people mobility using data from public events and an Italian mobile phone network: to predict both event attendance and future crowd density in specific areas. The event details (location, time etc.) are automatically gathered and stored in a structured format. Next, we handle these problems are treated in a “supervised learning” setting, and various state-of-art Machine Learning techniques are tested to find the best model for each task. The obtained models will be encapsulated into a Policy Support System contributing to foster planning actions of mobility services
Abduction with probabilistic logic programming under the distribution semantics
No full textIn Probabilistic Abductive Logic Programming we are given a probabilistic logic program, a set of abducible facts, and a set of constraints. Inference in probabilistic abductive logic programs aims to find a subset of the abducible facts that is compatible with the constraints and that maximizes the joint probability of the query and the constraints. In this paper, we extend the PITA reasoner with an algorithm to perform abduction on probabilistic abductive logic programs exploiting Binary Decision Diagrams. Tests on several synthetic datasets show the effectiveness of our approach
Nonground Abductive Logic Programming with Probabilistic Integrity Constraints
No full textUncertain information is being taken into account in an increasing number of application fields. In the meantime, abduction has been proved a powerful tool for handling hypothetical reasoning and incomplete knowledge. Probabilistic logical models are a suitable framework to handle uncertain information, and in the last decade many probabilistic logical languages have been proposed, as well as inference and learning systems for them. In the realm of Abductive Logic Programming (ALP), a variety of proof procedures have been defined as well. In this paper, we consider a richer logic language, coping with probabilistic abduction with variables. In particular, we consider an ALP program enriched with integrity constraints à la IFF, possibly annotated with a probability value. We first present the overall abductive language and its semantics according to the Distribution Semantics. We then introduce a proof procedure, obtained by extending one previously presented, and prove its soundness and completeness
A semantics for probabilistic hybrid knowledge bases with function symbols
No full textHybrid Knowledge Bases (HKBs) successfully integrate Logic Programming (LP) and Description Logics (DL) under the Minimal Knowledge with Negation as Failure semantics. Both world closure assumptions (open and closed) can be used in the same HKB, a feature required in many domains, such as the legal and health-care ones. In previous work, we proposed (function-free) Probabilistic HKBs, whose semantics applied Sato's distribution semantics approach to the well-founded HKB semantics proposed by Knorr et al. and Lyu and You. This semantics relied on the fact that the grounding of a function-free Probabilistic HKB (PHKB) is finite. In this article, we extend the PHKB language to allow function symbols, obtaining PHKBFS. Because the grounding of a PHKBFS can be infinite, we propose a novel semantics which does not require the PHKBFS's grounding to be finite. We show that the proposed semantics extends the previously proposed semantics and that, for a large class of PHKBFS, every query can be assigned a probability
Exploiting Uncertainty for Querying Inconsistent Description Logics Knowledge Bases
Full text linkThe 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
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