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

2019 ◽  
Vol 9 (9) ◽  
pp. 1743 ◽  
Author(s):  
Cheol Young Park ◽  
Kathryn Blackmond Laskey
Keyword(s):  
Knowledge Representation ◽  
Bayesian Network ◽  
Situation Awareness ◽  
Software Tool ◽  
Use Cases ◽  
Relational Model ◽  
Smart Manufacturing ◽  
Knowledge Representation And Reasoning ◽  
Ontology Language ◽  
Four Levels

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.

scholarly journals Uniform knowledge representation for language processing in the B2 system

1997 ◽  
Vol 3 (2) ◽  
pp. 123-145 ◽  
Author(s):  
SUSAN W. MCROY ◽  
SYED S. ALI ◽  
SUSAN M. HALLER
Keyword(s):  
Knowledge Representation ◽  
Natural Language ◽  
Bayesian Network ◽  
Language Processing ◽  
Semantic Network ◽  
Information Source ◽  
Knowledge Representation And Reasoning ◽  
Decision Support Model ◽  
Domain Specific ◽  
Management Domain

We describe the natural language processing and knowledge representation components of B2, a collaborative system that allows medical students to practice their decision-making skills by considering a number of medical cases that differ from each other in a controlled manner. The underlying decision-support model of B2 uses a Bayesian network that captures the results of prior clinical studies of abdominal pain. B2 generates story-problems based on this model and supports natural language queries about the conclusions of the model and the reasoning behind them. B2 benefits from having a single knowledge representation and reasoning component that acts as a blackboard for intertask communication and cooperation. All knowledge is represented using a propositional semantic network formalism, thereby providing a uniform representation to all components. The natural language component is composed of a generalized augmented transition network parser/grammar and a discourse analyzer for managing the natural language interactions. The knowlege representation component supports the natural language component by providing a uniform representation of the content and structure of the interaction, at the parser, discourse, and domain levels. This uniform representation allows distinct tasks, such as dialog management, domain-specific reasoning, and meta-reasoning about the Bayesian network, to all use the same information source, without requiring mediation. This is important because there are queries, such as Why?, whose interpretation and response requires information from each of these tasks. By contrast, traditional approaches treat each subtask as a “black-box” with respect to other task components, and have a separate knowledge representation language for each. As a result, they have had much more difficulty providing useful responses.

scholarly journals Event order generation using Reference Event based qualitative Temporal (REseT) relations in Time Event Ontology

2014 ◽  
Vol 4 (1) ◽  
Author(s):  
V. Uma ◽  
G. Aghila
Keyword(s):  
Query Languages ◽  
Vital Role ◽  
Knowledge Representation And Reasoning ◽  
Interval Algebra ◽  
Event Ordering ◽  
Ontology Language ◽  
Reference Event ◽  
Event Order ◽  
Event Based ◽  
Temporal Ordering

AbstractOWL (Web Ontology Language) is the standard language for Semantic Web and is used in defining ontologies for Web. Temporal event data are ubiquitous in nature. Temporal data can be represented qualitatively using temporal relations in OWL, enabling temporal ordering of events which plays a vital role in task planners. The basic Allen’s temporal interval relations can be used to describe relations in OWL. Allen’s interval algebra is a well known formalism used to represent and reason the temporal knowledge. In this work, Allen’s interval algebra is extended by Reference Event based Temporal (REseT) relations to reduce the ambiguity in the before relation. The extended formalism is used in the representation of relations between time intervals and the viability of ordering of events in ontology is elucidated. This paper proposes a temporal knowledge representation and reasoning based event ordering system which helps in the temporal ordering of events. The advantage of this method is that it does not introduce any additional constructs in OWL and hence the existing reasoning tools and DL based query languages are capable of generating the linear order of events. The system is investigated experimentally using the COW (Correlates of War) dataset and has been evaluated using the Percent_ Similarity measure.

Agile sensor tasking for CoIST using natural language knowledge representation and reasoning

2014 ◽  
Author(s):  
David Braines ◽  
Geeth de Mel ◽  
Chris Gwilliams ◽  
Christos Parizas ◽  
Diego Pizzocaro ◽  
...  
Keyword(s):  
Knowledge Representation ◽  
Natural Language ◽  
Knowledge Representation And Reasoning ◽  
Language Knowledge ◽  
Sensor Tasking

Architecture and use cases of digital twins towards smart manufacturing

2021 ◽  
Vol 8 (3) ◽  
pp. 264
Author(s):  
Nibhrita Tiwari ◽  
Maninder Jeet Kaur ◽  
Ved Prakash Mishra
Keyword(s):  
Use Cases ◽  
Smart Manufacturing ◽  
Digital Twins

Model Libraries: Knowledge Representation and Reasoning

1990 ◽  
Vol 2 (3) ◽  
pp. 287-301 ◽  
Author(s):  
Michael V. Mannino ◽  
Betsy S. Greenberg ◽  
Sa Neung Hong
Keyword(s):  
Knowledge Representation ◽  
Knowledge Representation And Reasoning

scholarly journals Acyclicity Notions for Existential Rules and Their Application to Query Answering in Ontologies

2013 ◽  
Vol 47 ◽  
pp. 741-808 ◽  
Author(s):  
B. Cuenca Grau ◽  
I. Horrocks ◽  
M. Krötzsch ◽  
C. Kupke ◽  
D. Magka ◽  
...  
Keyword(s):  
Knowledge Representation ◽  
Sufficient Conditions ◽  
Query Answering ◽  
Conjunctive Queries ◽  
Practical Solution ◽  
Ontology Language ◽  
Lower Complexity ◽  
The Given ◽  
Theoretical Side

Answering conjunctive queries (CQs) over a set of facts extended with existential rules is a prominent problem in knowledge representation and databases. This problem can be solved using the chase algorithm, which extends the given set of facts with fresh facts in order to satisfy the rules. If the chase terminates, then CQs can be evaluated directly in the resulting set of facts. The chase, however, does not terminate necessarily, and checking whether the chase terminates on a given set of rules and facts is undecidable. Numerous acyclicity notions were proposed as sufficient conditions for chase termination. In this paper, we present two new acyclicity notions called model-faithful acyclicity (MFA) and model-summarising acyclicity (MSA). Furthermore, we investigate the landscape of the known acyclicity notions and establish a complete taxonomy of all notions known to us. Finally, we show that MFA and MSA generalise most of these notions. Existential rules are closely related to the Horn fragments of the OWL 2 ontology language; furthermore, several prominent OWL 2 reasoners implement CQ answering by using the chase to materialise all relevant facts. In order to avoid termination problems, many of these systems handle only the OWL 2 RL profile of OWL 2; furthermore, some systems go beyond OWL 2 RL, but without any termination guarantees. In this paper we also investigate whether various acyclicity notions can provide a principled and practical solution to these problems. On the theoretical side, we show that query answering for acyclic ontologies is of lower complexity than for general ontologies. On the practical side, we show that many of the commonly used OWL 2 ontologies are MSA, and that the number of facts obtained by materialisation is not too large. Our results thus suggest that principled development of materialisation-based OWL 2 reasoners is practically feasible.

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