Multi-Entity Bayesian Network Research Articles

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.

The Application of Interactive Humanoid Robots in the History Education of Museums Under Artificial Intelligence

The purpose is to improve the application of museum robots in museum scenes, enhance the service capabilities of robots in museums, break tourists’ boring concepts of museum environment, manual explanation, services, etc., and promote tourists’ exhibition experience. A method for sentiment analysis of humanoid robots in museums is proposed by studying the transformation of museums with the help of artificial intelligence (AI) technology, as well as the function and significance of museums in history education. First, the function of museums in history education and the role of AI in constructing intelligent museums are described. Second, on account of the multimodal sentiment analysis method of speech and emotion, a scenario model of the visitor museum is established. An uncertain reasoning method for robot service tasks based on Multi-entity Bayesian network (MEBN) is also proposed. Finally, the proposed model is validated by experiments. The results show that compared with the recognition rates of Arousal and Valence dimensions, the consistency correlation coefficient value of the Kalman filter is higher. The Consistency Correlation Coefficient (CCC) value of the Arousal dimension is 0.703, and the CCC value of the Valence dimension is 0.766. Besides, in different tour times, the proportion of services that tourists want to be provided with varies in different emotional states. From time t1 to time t2, the proportion of tourists who want to hear explanations of cultural relics dropped by 11.5%, while the proportion of tourists who want to be provided with tea service increased by 24%. This indicates that when the Kalman filter algorithm performs continuous emotion recognition of a multimodal fusion, the final emotion recognition accuracy is higher, and emotion analysis can help humanoid robots to be more intelligent and humanized. The proposed sentiment analysis based on the multimodal analysis and MEBN’s uncertainty reasoning method for robot service tasks not only broadens the practical application field of intelligent robots under human–computer interaction technology but also has important research significance for the innovative education development of museum history education.

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.

A Theoretical Foundation for Syntactico-Semantic Pattern Recognition

Conventionally syntactic pattern recognition tasks have been driven by grammars defining a syntactic structure. Syntactic Pattern recognition tasks were primarily relying on the ability of parsing algorithms to recognize the patterns in the input data. These algorithms essentially were dependent on the syntactic grammars defining the patterns. Context free grammars, a type of grammars have been particularly well studied for pattern recognition tasks to be solved by computer efficiently. Some of the key pattern recognition tasks had applications in Natural Language Processing (NLP). Though context free grammars are well suited for capturing rigid patterns and unambiguous patterns, there was a need to encapsulate the uncertainty aspects involved in some pattern recognition processes. Probabilistic context free grammars can well handle the need to capture uncertainty in the processes but not in a true sense they are able to capture the uncertainty associated with the semantic context governing the domain in which the pattern recognition processes are being attempted at. The paper formally puts forth an approach for syntactico-semantic pattern recognition. The syntactico-semantic pattern recognition attempts to capture the semantic context and the uncertainties involved thereof along with probabilistic reasoning. The approach consists of integration mapping between probabilistic context free grammar (PCFG) and Multi Entity Bayesian network (MEBN), a first-order logic for modeling probabilistic knowledge bases. Additionally, the paper outlines a modified version of the CYK parser algorithm for the defined mapping between PCFG and MEBN with a method to ensure the properness and consistency of such PCFG along with its key application, disambiguation of PP (Prepositional Phrase) attachment. The theoretical foundation proposed has been validated by a proof-of-concept implementation of the modified CYK algorithm for syntactico-semantic reasoning in Java with promising ability to disambiguate PP attachment uses cases of New York Times and Wikipedia corpus dataset samples.

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.

An ontology-based methodology for hazard identification and causation analysis

This article presents a dynamic hazard identification methodology founded on an ontology-based knowledge modeling framework coupled with probabilistic assessment. The objective is to develop an efficient and effective knowledge-based tool for process industries to screen hazards and conduct rapid risk estimation. The proposed generic model can translate an undesired process event (state of the process) into a graphical model, demonstrating potential pathways to the process event, linking causation to the transition of states. The Semantic web-based Web Ontology Language (OWL) is used to capture knowledge about unwanted process events. The resulting knowledge model is then transformed into Probabilistic-OWL (PR-OWL) based Multi-Entity Bayesian Network (MEBN). Upon queries, the MEBNs produce Situation Specific Bayesian Networks (SSBN) to identify hazards and their pathways along with probabilities. Two open-source software programs, Protégé and UnBBayes, are used. The developed model is validated against 45 industrial accidental events extracted from the U.S. Chemical Safety Board's (CSB) database. The model is further extended to conduct causality analysis.

Reasoning Non-Functional Requirements Trade-off in Self-Adaptive Systems Using Multi-Entity Bayesian Network Modeling

Reasoning Non-Functional Requirements Trade-off in Self-Adaptive Systems Using Multi-Entity Bayesian Network Modeling

Survey of Multi Entity Bayesian Network (MEBN) and its applications in probabilistic reasoning.

Bayesian networks have been at the core of the research pertaining to probabilistic reasoning. Several machine learning algorithms and techniques highly rely on Bayesian networks for their reasoning capabilities. Since several decades Bayesian networks proved to be the essential tool in the hands of researchers working in artificial intelligence domain. Yet Bayesian networks do have certain limitations [1], Bayesian networks need extensions to be more expressive and functional for probabilistic reasoning needs of various domains. Multi Entity Bayesian Networks (MEBN) is a theory combining expressivity of first-order logic principles and probabilistic reasoning of Bayesian networks. The paper thoroughly introduces the MEBN theory with an example modeling for a problem description and discusses various other works which included MEBN theory as a core of their research. The study in this paper concludes with the current highlights and challenges in MEBN and future developments.

SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots.

In order to facilitate cooperation between underwater robots, it is a must for robots to exchange information with unambiguous meaning. However, heterogeneity, existing in information pertaining to different robots, is a major obstruction. Therefore, this paper presents a networked ontology, named the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) ontology, to address information heterogeneity and enable robots to have the same understanding of exchanged information. The SWARMs ontology uses a core ontology to interrelate a set of domain-specific ontologies, including the mission and planning, the robotic vehicle, the communication and networking, and the environment recognition and sensing ontology. In addition, the SWARMs ontology utilizes ontology constructs defined in the PR-OWL ontology to annotate context uncertainty based on the Multi-Entity Bayesian Network (MEBN) theory. Thus, the SWARMs ontology can provide both a formal specification for information that is necessarily exchanged between robots and a command and control entity, and also support for uncertainty reasoning. A scenario on chemical pollution monitoring is described and used to showcase how the SWARMs ontology can be instantiated, be extended, represent context uncertainty, and support uncertainty reasoning.

Towards a Hybrid Approach to Context Reasoning for Underwater Robots

Ontologies have been widely used to facilitate semantic interoperability and serve as a common information model in many applications or domains. The Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) project, aiming to facilitate coordination and cooperation between heterogeneous underwater vehicles, also adopts ontologies to formalize information that is necessarily exchanged between vehicles. However, how to derive more useful contexts based on ontologies still remains a challenge. In particular, the extreme nature of the underwater environment introduces uncertainties in context data, thus imposing more difficulties in context reasoning. None of the existing context reasoning methods could individually deal with all intricacies in the underwater robot field. To this end, this paper presents the first proposal applying a hybrid context reasoning mechanism that includes ontological, rule-based, and Multi-Entity Bayesian Network (MEBN) reasoning methods to reason about contexts and their uncertainties in the underwater robot field. The theoretical foundation of applying this reasoning mechanism in underwater robots is given by a case study on the oil spill monitoring. The simulated reasoning results are useful for further decision-making by operators or robots and they show that the consolidation of different reasoning methods is a promising approach for context reasoning in underwater robots.

Bayesian Network for Uncertainty Representation in Semantic Web: A Survey

Bayesian network is a probabilistic model to represent uncertainty available in knowledge base and using it tremendous works have been done to prove its relevance in uncertainty representation and reasoning using Bayesian inference. Probability can be used to represent uncertainty like prediction information, situational awareness, data and knowledge fusion etc in knowledge base to implement various real life situations. Various approaches based on description logic, object oriented, entity relational, and first order logic have been tried to represent uncertainty successfully. One of them is Multi-Entity Bayesian Network (MEBN) logic to represent probabilistic information and performing knowledge fusion in ontology, which is realized using PR-OWL (Probabilistic Web Ontology Language). This paper aims at giving an overall view, the work carried out so far to represent uncertainty with the help of Bayesian Network in semantic web and a list of works done using MEBN/PR-OWL for knowledge fusion or the representation of uncertainty in semantic web.

Risk Prediction for Production of an Enterprise

Despite all preventive measures, there is so much possibility of risks in any project development as well as in enterprise ma nagement. There is no any standard mechanism or methodology available to assess the risks in any project or production management. Using some precautionary steps, the manager can only avoid the risks as much as he can. To address this issue, this paper presents a probabilistic risk assessment model for the production of an enterprise. For this, Multi-Entity Bayesian Network (MEBN) has been used to represent the requirements for production management as well as to assess the risks adherence in production management, where MEBN combines expressivity of first-order logic and probabilistic feature of Bayesian network. Bayesian network provides the feature to represent the probabilistic uncertainty and reasoning about probabilistic knowledge base, which is used here to represent the probable risks behind each causes of a risk. The proposed probabilistic model is discussed with the help of a case study, which is used to predict risks inherent in the production of an enterprise, which depends upon various measures like labour availability, power backup, transport availability etc.

Research on Online Monitoring and Analyzing of Interactive Behavior of Distributed Software

The paper pays close attention to scenario and relationships between behavior and behavioral effects of distributed software at running time, presents a novel online monitoring and analyzing method for software behavior. Dynamic AOP monitor ing technology is adopted to monitor interactive events related with business logic which are produced by the third party entities; Scenario-sensitive method is used to model complicated Interactive Behavior s (IB s ) among these entities. By fusing real-time self-experience and pervious experience based on knowledge, the creditability of interactive entities is computed automatically. Multi-Entity Bayesian Network (MEBN) tool is adopted to construct reusable domain “knowledge fragment”. If current scenario is similar to pervious one, then pervious one is reused; if there is no similar scenario, evidences gained from monitoring and pervious experience are fused to construct behavior model for this scenario. The combination of large and small knowledge reuse improves analysis efficiency of IB s . Above method is used to “Trusted Purchasing Network” that we develop, deceitful or fraudulent behaviors in trade process are online monitored and analyzed.