Probabilistic Ontology Research Articles

A semantic knowledge-based method for home service robot to grasp an object

To help the home service robot grasp an object with the constraints from the object itself, task and environment at the same time, we propose a knowledge-based method. Firstly, we construct five ontologies (object, task, environment, constraint and probabilistic ontology) to represent the five in a unified format. The remarkable things are that the object ontology is part-based and the constraints (grasp type, grasp location, approach direction, opening width, trajectory constraint and grasp force) are throughout the whole grasp process. To reason the constraints out with the findings, we propose a collaborative reasoning mechanism. It contains ontological reasoning, rule-based reasoning and probabilistic reasoning. Secondly, we propose to use SWRL (Semantic Web Rule Language) rules to label the object’s segments with functional parts. For its textual form, this method is fast. Thirdly, JSHOP2 planner is used to make the inferred constraints as the parameters of grasp subactions. Once the subaction list is output, the corresponding executable codes are called. Finally, we use Tiago robot to grasp different objects in different task and environment in our laboratory.

Remote intervention assistance system for a person in difficulty based on probabilistic ontologies

The proposed theme of this paper is to develop a system for remote intervention assistance with a person in difficulty, which is becoming increasingly important as the world’s population ages. The goal is to provide effective long-term care to reduce hospitalizations of the elderly and alleviate the burden of care for hospitals and governments. The system requires careful consideration of costs, resources, and efficiency due to the complexity and heterogeneity of the data involved, making human analysis nearly impossible. To enable machine analysis and decision-making, the proposed system is based on an ontological approach that combines medical, social, temporary, and geographical factors, along with Semantic Web Rule Language (SWRL). The system is further enhanced by the integration of probabilistic reasoning through Probabilistic Web Ontology Language (PR-OWL), which allows for the use of ontological resources in a probabilistic environment. The paper also proposes two additional approaches in the form of Jena Application Programming Interfaces (API), including ontological enrichment based on Bayesian Network (BN) knowledge bases and structural/parametric learning based on ontological knowledge bases, as well as a probabilistic clause integrated into the ontological format. As results, the proposed approaches were tested in a simulation, and feedback was collected from users. The feedback was compared, and an increase in precision was observed from one approach to another with varying execution times. These results demonstrate the effectiveness of the proposed system for remote intervention assistance and provide a foundation for future research and development in this area.

Probabilistic knowledge infusion through symbolic features for context-aware activity recognition

In the general machine learning domain, solutions based on the integration of deep learning models with knowledge-based approaches are emerging. Indeed, such hybrid systems have the advantage of improving the recognition rate and the model’s interpretability. At the same time, they require a significantly reduced amount of labeled data to reliably train the model. However, these techniques have been poorly explored in the sensor-based Human Activity Recognition (HAR) domain. The common-sense knowledge about activity execution can potentially improve purely data-driven approaches. While a few knowledge infusion approaches have been proposed for HAR, they rely on rigid logic formalisms that do not take into account uncertainty. In this paper, we propose P-NIMBUS, a novel knowledge infusion approach for sensor-based HAR that relies on probabilistic reasoning. A probabilistic ontology is in charge of computing symbolic features that are combined with the features automatically extracted by a CNN model from raw sensor data and high-level context data. In particular, the symbolic features encode probabilistic common-sense knowledge about the activities consistent with the user’s surrounding context. These features are infused within the model before the classification layer. We experimentally evaluated P-NIMBUS on a HAR dataset of mobile devices sensor data that includes 14 different activities performed by 25 users. Our results show that P-NIMBUS outperforms state-of-the-art neuro-symbolic approaches, with the advantage of requiring a limited amount of training data to reach satisfying recognition rates (i.e., more than 80% of F1-score with only 20% of labeled data).

Probabilistic reasoning for diagnosis prediction of Coronavirus disease based on probabilistic ontology

The novel Coronavirus has been declared a pandemic by the World Health Organization (WHO). Predicting the diagnosis of COVID-19 is essential for disease cure and control. The paper?s main aim is to predict the COVID-19 diagnosis using probabilistic ontologies to address the randomness and incompleteness of knowledge. Our approach begins with constructing the entities, attributes, and relationships of COVID-19 ontology, by extracting symptoms and risk factors. The probabilistic components of COVID-19 ontology are developed by creating a Multi-Entity Bayesian Network, then determining its components, with the different nodes, as probability distribution linked to various nodes. We use probabilistic inference for predicting COVID-19 diagnosis, using the Situation-Specific Bayesian Network (SSBN). To validate the solution, an experimental study is conducted on real cases, comparing the results of existing machine learning methods, our solution presents an encouraging result and, therefore enables fast medical assistance.

Reasoning about location of robot-operated object based on probabilistic ontologies

Background: A robot needs to acquire the location of objects when performing daily tasks. Compared to industrial robots, a service robot faces a more complex and unstructured working environment where the location of the target object is usually uncertain. For example, due to diversity in personal habits and seasons, apples may be located in refrigerators, tables, or other places in the living environment. Methods: We propose a novel method for semantic localization of the robot-operated object based on probabilistic ontologies (PR-OWL) and multi-entity Bayesian networks (MEBN). The probabilistic web ontology language is used to describe and model the highly uncertain knowledge about the storage location of objects in the human household environment. Furthermore, the target location is inferred based on the multi-entity Bayesian network. Results: The proposed method is capable to adapting to environmental changes and achieves reliable probability estimation of object location. Experiments on simulated robotic tasks verify the effectiveness of the method. Conclusions: We show that applying PR-OWL combined with MEBN to locate the target object for the robot is feasible， which can improve the cognitive and self-adaptive ability of the robot.

Remote Intervention Assistance System for a Person in Difficulty Based on Probabilistic Ontologies

Remote Intervention Assistance System for a Person in Difficulty Based on Probabilistic Ontologies

Proactive and reactive context reasoning architecture for smart web services

The web of things (WoT) uses web technologies to connect embedded objects to each other and to deliver services to stakeholders. The context of these interactions (situation) is a key source of information which can be sometimes uncertain. In this paper, we focus on the development of intelligent web services. The main requirements for intelligent service are to deal with context diversity, semantic context representation and the capacity to reason with uncertain information. From this perspective, we propose a framework for intelligent services to deal with various contexts, to reactively respond to real-time situations and proactively predict future situations. For the semantic representation of context, we use PR-OWL, a probabilistic ontology based on multi-entity Bayesian networks. PR-OWL is flexible enough to represent complex and uncertain contexts. We validate our framework with an intelligent plant watering use case to show its reasoning capabilities.

ProCAVIAR: Hybrid Data-Driven and Probabilistic Knowledge-Based Activity Recognition

The recognition of physical activities using sensors on mobile devices has been mainly addressed with supervised and semi-supervised learning. The state-of-the-art methods are mainly based on the analysis of the user's movement patterns that emerge from inertial sensors data. While the literature on this topic is quite mature, existing approaches are still not adequate to discriminate activities characterized by similar physical movements. The context that surrounds the user (e.g., semantic location) could be used as additional information to significantly extend the set of recognizable activities. Since collecting a comprehensive training set with activities performed in every possible context condition is too costly, if possible at all, existing works proposed knowledge-based reasoning over ontological representation of context data to refine the predictions obtained from machine learning. A problem with this approach is the rigidity of the underlying logic formalism that cannot capture the intrinsic uncertainty of the relationships between activities and context. In this work, we propose a novel activity recognition method that combines semi-supervised learning and probabilistic ontological reasoning. We model the relationships between activities and context as a combination of soft and hard ontological axioms. For each activity, we use a probabilistic ontology to compute its compatibility with the current context conditions. The output of probabilistic semantic reasoning is combined with the output of a machine learning classifier based on inertial sensor data to obtain the most likely activity performed by the user. The evaluation of our system on a dataset with 13 types of activities performed by 26 subjects shows that our probabilistic framework outperforms both a pure machine learning approach and previous hybrid approaches based on classic ontological reasoning.

Fuzzy Probabilistic Ontology Approach

In spite of the undeniable success of the ontologies, where they have been widely applied successfully to represent the knowledge in lots of real-world problems, they cannot represent and reason with uncertain knowledge which inherently appears in most domains. To cope with this issue, this article presents a new approach for dealing with rich-uncertainty domains. In fact, it is mainly based on integrating hybrid models which combine both fuzzy logic and Bayesian networks. On the other hand, the Fuzzy multi-entity Bayesian network (FzMEBN) proposed as a hybrid model which enhances the classical multi-entity Bayesian network using fuzzy logic, it can be used to represent and reason with probabilistic and vague knowledge simultaneously. Thus, as a language belongs to the proposed approach, this study proposes a promising solution to overcome the weakness of the Probabilistic Ontology Web Language (PR-OWL) based on FzMEBN to allow dealing with vague and probabilistic knowledge in ontologies. The proposed extension is evaluated with a case study in the medical field (diabetes diseases).

MEBN-RM: A Mapping between Multi-Entity Bayesian Network and Relational Model

Multi-Entity Bayesian Network (MEBN) is a knowledge representation formalism combining Bayesian Networks (BNs) with First-Order Logic (FOL). MEBN has sufficient expressive power for general-purpose knowledge representation and reasoning, and is the logical basis of Probabilistic Web Ontology Language (PR-OWL), a representation language for probabilistic ontologies. Developing an MEBN model to support a given application is a challenge, requiring definition of entities, relationships, random variables, conditional dependence relationships, and probability distributions. When available, data can be invaluable both to improve performance and to streamline development. By far the most common format for available data is the relational database (RDB). Relational databases describe and organize data according to the Relational Model (RM). Developing an MEBN model from data stored in an RDB therefore requires mapping between the two formalisms. This paper presents MEBN-RM, a set of mapping rules between key elements of MEBN and RM. We identify links between the two languages (RM and MEBN) and define four levels of mapping from elements of RM to elements of MEBN. These definitions are implemented in the MEBN-RM algorithm, which converts a relational schema in RM to a partial MEBN model. Through this research, the software has been released as an MEBN-RM open-source software tool. The method is illustrated through two example use cases using MEBN-RM to develop MEBN models: a Critical Infrastructure Defense System and a Smart Manufacturing System. Both systems are proof-of-concept systems used for situation awareness, where data coming from various sensors are stored in RDBs and converted into MEBN models through the MEBN-RM algorithm. In these use cases, we evaluate the performance of the MEBN-RM algorithm in terms of mapping speed and quality to show its efficiency in MEBN modeling.

A semantic approach for situation-aware ubiquitous learner support

This paper thoroughly introduces a semantic web approach for supporting decision making in ubiquitous learning environment. Probabilistic ontology will be proposed to reduce inaccuracy, randomness, and incompleteness and will support automated reasoning to support the learner choice of the learning intention, strategy, media and resource according to the predicted learning situation. The multi-entity Bayesian networks (MEBNs) was used for modelling the knowledge and analysing the content in context-aware ubiquitous learning. In fact, MEBNs offer a rigorous framework for knowledge representing and reasoning with probabilistic inference. Finally, a case study has been presented confirming the effectiveness of the proposed model.

A semantic approach for situation-aware ubiquitous learner support

This paper thoroughly introduces a semantic web approach for supporting decision making in ubiquitous learning environment. Probabilistic ontology will be proposed to reduce inaccuracy, randomness, and incompleteness and will support automated reasoning to support the learner choice of the learning intention, strategy, media and resource according to the predicted learning situation. The multi-entity Bayesian networks (MEBNs) was used for modelling the knowledge and analysing the content in context-aware ubiquitous learning. In fact, MEBNs offer a rigorous framework for knowledge representing and reasoning with probabilistic inference. Finally, a case study has been presented confirming the effectiveness of the proposed model.

Indoor Localisation through Probabilistic Ontologies

For elderly people that are living alone in their homes there is a need to permanently monitor them. One of this aspect consist in knowing their indoor position and motion behavioural status, in real time. One possibility for indoor positioning of an user consists in understanding the images provided by supervising cameras. In this case the main aspect is represented by recognition of objects from these images. Thus, object recognition plays an essential part in understanding the environment and adding meaning to it. This paper presents a method for indoor localisation based on identifying the user’s context. The user’s context is computed based on object recognition and using a probabilistic ontology. The key element is represented by the probabilistic ontology that describes objects, scenes and relations between them. This ontology contains probabilistic relations that are learned using a large database. Results show that given a set of object detectors with high detection rate and low false positive rate, the system can recognize the user’s context with high accuracy.

Integration of Context Information through Probabilistic Ontological Knowledge into Image Classification

The use of ontological knowledge to improve classification results is a promising line of research. The availability of a probabilistic ontology raises the possibility of combining the probabilities coming from the ontology with the ones produced by a multi-class classifier that detects particular objects in an image. This combination not only provides the relations existing between the different segments, but can also improve the classification accuracy. In fact, it is known that the contextual information can often give information that suggests the correct class. This paper proposes a possible model that implements this integration, and the experimental assessment shows the effectiveness of the integration, especially when the classifier’s accuracy is relatively low. To assess the performance of the proposed model, we designed and implemented a simulated classifier that allows a priori decisions of its performance with sufficient precision.

A New Method for Building Probabilistic Ontology (Prob-Ont)

During the past years, ontologies are widely used for representing knowledge of complex domains. Despite that the ontologies (classical ontologies) have become standard for representing knowledge; however, they are not able to represent and reason with uncertainty which is one of the characteristics of the world that must be handled. Probabilistic Ontologies have come to remedy this defect. This paper is part of this framework in which the authors have proposed a new method of probabilistic ontology construction, named Prob-Ont, by integrating uncertainty to elements of OWL ontology (especially to instances and/or relations). As a case study, the authors have constructed a probabilistic ontology for the domain of scientific documentation system (dblp).

Reactive, proactive, and extensible situation-awareness in ambient assisted living

Considering that the world population is aging, health-support issues are in evidence, and many dangerous situations concerning users in their living environment may arise. This paper presents an approach to allow Ambient Assisted Living (AAL) systems to identify and predict situations that may endanger users in their living environment. Elderly people generally have difficulties to perform daily activities as using some electronic device, cooking, dressing among others. This paper offers an alternative to acquire comprehension of a former person's behavioral providing this knowledge when cognitive impairments will occur. This is a significant improvement to traditional expert systems applied to AAL as the handling of the knowledge base is adapted to the failing users’ behavior. We consider the user profile dynamics where new and different situations not initially planned in the development of the systems may occur. This approach was developed considering the principles of reactive and proactive behavior and the extensibility capacity of the system. Systems developed following the above-described principles can react to the current situations and anticipate proactively a dangerous situation eliminating it or controlling its impact. The approach is evaluated and verified by a case study where unwanted, dangerous situations were created and subsequently detected. The case study is based on a simulated smart house fulfilled with sensors and appliances. An application was build based on the developed approach, and it was capable of choosing the most appropriate actions to solve the unwanted situations. The main contributions of the present work are the identification and prediction of situations in smart houses, an approach developed with the principles of reactive and proactive behavior validated with a case study and supported by a probabilistic ontology.

Probabilistic ontology based activity recognition in smart homes using Markov Logic Network

Designing an activity recognition system that models various activities of an occupant is the fundamental task in creating a smart home. Activity Recognition (AR) modeling, has witnessed a comprehensive range of research, that focuses independently on probabilistic approaches and on ontology based models as well. The research presented in this paper introduces an innovative approach in AR system design that integrates probabilistic inference with the represented domain ontology. Data obtained from sensors are uncertain in nature and mapping uncertainty over ontology will not yield good accuracy in the context of AR. The proposed system augments ontology based activity recognition with probabilistic reasoning through Markov Logic Network (MLN) which is a statistical relational learning approach. The proposed system utilizes the model theoretic semantic property of description logic, to convert the represented ontology activity model to its corresponding first order rules. MLN is constructed by learning weighted first order rules that enable probabilistic reasoning within a knowledge representation framework. The experiments based on datasets obtained from smart home prototypes illustrate the effectiveness of integrating probabilistic reasoning over domain ontology and the result analysis shows enhanced recognition accuracy in comparison with existing approaches.

Uncertainty modeling process for semantic technology

The ubiquity of uncertainty across application domains generates a need for principled support for uncertainty management in semantically aware systems. A probabilistic ontology provides constructs for representing uncertainty in domain ontologies. While the literature has been growing on formalisms for representing uncertainty in ontologies, there remains little guidance in the knowledge engineering literature for how to design probabilistic ontologies. To address the gap, this paper presents the Uncertainty Modeling Process for Semantic Technology (UMP-ST), a new methodology for modeling probabilistic ontologies. To explain how the methodology works and to verify that it can be applied to different scenarios, this paper describes step-by-step the construction of a proof-of-concept probabilistic ontology. The resulting domain model can be used to support identification of fraud in public procurements in Brazil. While the case study illustrates the development of a probabilistic ontology in the PR-OWL probabilistic ontology language, the methodology is applicable to any ontology formalism that properly integrates uncertainty with domain semantics.

Knowledge-oriented semantics modelling towards uncertainty reasoning.

Distributed reasoning in M2M leverages the expressive power of ontology to enable semantic interoperability between heterogeneous systems of connected devices. Ontology, however, lacks the built-in, principled support to effectively handle the uncertainty inherent in M2M application domains. Thus, efficient reasoning can be achieved by integrating the inferential reasoning power of probabilistic representations with the first-order expressiveness of ontology. But there remains a gap with current probabilistic ontologies since state-of-the-art provides no compatible representation for simultaneous handling of discrete and continuous quantities in ontology. This requirement is paramount, especially in smart homes, where continuous quantities cannot be avoided, and simply mapping continuous information to discrete states through quantization can cause a great deal of information loss. In this paper, we propose a hybrid probabilistic ontology that can simultaneously handle distributions over discrete and continuous quantities in ontology. We call this new framework HyProb-Ontology, and it specifies distributions over properties of classes, which serve as templates for instances of classes to inherit as well as overwrite some aspects. Since there cannot be restriction on the dependency topology of models that HyProb-Ontology can induce across different domains, we can achieve a unified Ground Hybrid Probabilistic Model by conditional Gaussian fuzzification of the distributions of the continuous variables in ontology. From the results of our experiments, this unified model can achieve exact inference with better performance over classical Bayesian networks.

A web system for reasoning with probabilistic OWL

We present the web application Tableau Reasoner for descrIption Logics in proLog on SWI-Prolog for SHaring TRILL on SWISH which allows the user to write probabilistic description logic DL theories and compute the probability of queries with just a web browser. Various probabilistic extensions of DLs have been proposed in the recent past, because uncertainty is a fundamental component of the Semantic Web. We consider probabilistic DL theories following our distribution semantics for probabilistic ontologies DISPONTE semantics. Axioms of a DISPONTE knowledge base can be annotated with a probability, and the probability of queries can be computed with inference algorithms. TRILL is a probabilistic reasoner for DISPONTE knowledge base that is implemented in Prolog and exploits its backtracking facilities for handling the non-determinism of the tableau algorithm. TRILL on SWISH is based on SWISH, a recently proposed web framework for logic programming, based on various features and packages of SWI-Prolog e.g., a web server and a library for creating remote Prolog engines and posing queries to them. TRILL on SWISH also allows users to cooperate in writing a probabilistic DL theory. It is free, open, and accessible on the Web at the url: http://trill.lamping.unife.it; it includes a number of examples that cover a wide range of domains and provide interesting Probabilistic Semantic Web applications. By building a web-based system, we allow users to experiment with probabilistic DLs without the need to install a complex software stack. In this way, we aim to reach out to a wider audience and popularize the Probabilistic Semantic Web. Copyright © 2016 John Wiley & Sons, Ltd.