A literature overview of knowledge sharing between Petri nets and ontologies

2016 ◽  
Vol 31 (3) ◽  
pp. 239-260 ◽  
Author(s):  
Haitao Cheng ◽  
Zongmin Ma
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Representation ◽  
Knowledge Sharing ◽  
Petri Nets ◽  
Petri Net ◽  
Knowledge Engineering ◽  
Future Research ◽  
Knowledge Domain ◽  
Literature Overview ◽  
Popular Knowledge

AbstractKnowledge representation is a subarea of artificial intelligence concerned with using formal symbols to represent a set of facts within a knowledge domain. Two popular knowledge representation languages, namely Petri net and ontology, are promising knowledge sharing and reusing methods in knowledge engineering. The combination of Petri net and ontology can facilitate achieving complementary advantages. Currently, many efforts have been done on knowledge sharing between Petri nets and ontologies. To investigate these issues and more importantly serve as identifying the direction of knowledge sharing between Petri nets and ontologies, in this paper we give a comprehensive literature overview of knowledge sharing between Petri net models and ontology models to satisfy the obvious need. In detail, we discuss the knowledge sharing from two aspects: the different knowledge representation approaches of ontology to represent and reason Petri net and issues of constructing Petri net from ontology. In addition, other important issues on applications and directions for future research are discussed in detail.

Ontologies and E-Learning

2005 ◽  
pp. 345-356
Author(s):  
Matteo Cristani
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Management ◽  
Natural Language Processing ◽  
Knowledge Sharing ◽  
Language Processing ◽  
Information Integration ◽  
Knowledge Engineering ◽  
Formal Ontologies ◽  
E Learning ◽  
Intelligent Information

What is an ontology? Why is this relevant to a learning environment? It is quite well-established in recent investigations on information systems that formal ontologies area crucial problem to deal with, and in fact, received a lot of attention in several different communities, such as knowledge management, knowledge engineering, natural language processing, intelligent information integration, and so on (Fensel, 2000).Ontologies have been developed in artificial intelligence to facilitate knowledge sharing and reuse. The viewpoint we adopt here is taken from the general considerations on the use of philosophical issues in artificial intelligence: “the systematic, formal, axiomatic development of the logic of all forms and modes of being” (Cocchiarella,1991). Another commonly accepted definition is that an ontology is an explicit specification of a shared conceptualization that holds in a particular context.

Ontologies and E-Learning

2011 ◽  
pp. 322-331 ◽  
Author(s):  
Matteo Cristani
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Management ◽  
Natural Language Processing ◽  
Knowledge Sharing ◽  
Language Processing ◽  
Information Integration ◽  
Knowledge Engineering ◽  
Formal Ontologies ◽  
E Learning ◽  
Intelligent Information

What is an ontology? Why is this relevant to a learning environment? It is quite well-established in recent investigations on information systems that formal ontologies area crucial problem to deal with, and in fact, received a lot of attention in several different communities, such as knowledge management, knowledge engineering, natural language processing, intelligent information integration, and so on (Fensel, 2000).Ontologies have been developed in artificial intelligence to facilitate knowledge sharing and reuse. The viewpoint we adopt here is taken from the general considerations on the use of philosophical issues in artificial intelligence: “the systematic, formal, axiomatic development of the logic of all forms and modes of being” (Cocchiarella,1991). Another commonly accepted definition is that an ontology is an explicit specification of a shared conceptualization that holds in a particular context.

Infinite Petri Nets as Models of Grids

2015 ◽  
pp. 187-204 ◽  
Author(s):  
Dmitry A. Zaitsev ◽  
Ivan D. Zaitsev ◽  
Tatiana R. Shmeleva
Keyword(s):  
Petri Nets ◽  
Petri Net ◽  
Regular Structure ◽  
Parametric Representation ◽  
Future Research ◽  
Parametric Form ◽  
Infinite Systems ◽  
Analysis Technique ◽  
Parametric Description

An overview of works, early published by the authors, has been done that explains peculiarities of composition and analysis technique developed for investigation of infinite Petri nets with regular structure which were introduced for modeling networks, clusters, and computing grids. Parametric description of Petri nets, parametric representation of infinite systems for calculation place/transition invariants, and solving them in parametric form allowed the invariance proof for infinite Petri net models. Complex deadlocks were disclosed and a possibility of the network blocking via ill-intended traffic revealed. Prospective directions for future research of infinite Petri nets were formulated.

scholarly journals The Combination of Petri Nets and Queueing Theory

2019 ◽  
Vol 9 (1S5) ◽  
pp. 293-294
Keyword(s):  
Petri Nets ◽  
Waiting Time ◽  
Petri Net ◽  
Queueing Theory ◽  
Time Delays ◽  
Queueing Network ◽  
Literature Survey ◽  
Future Research ◽  
Implementation Cost ◽  
Stochastic Petri Net

In this paper, We reviews that most of the articles under the topics petri net (PN), stochastic petri net (SPN), collared petri net (CPN), timed petri net (TPN) and queueing theory(queueing network) were created models and methods independently. After review we came to a conclusion, that the combination of petri net and queueing theory types is used to reduce the implementation cost on the applications. This literature survey shows the outline of recent works done using petri net(SPN,CPN,TPN) and queueing theory models in different applications. The main aim of the queueing theory is to reduce the waiting time, delays in travels, cost. So future research and extension to further the fruitful applications reducing delays using the combination of petri net (SPN,CPN,TPN) and queueing theory (queueing network) are discussed in this article.

scholarly journals Technical, Hard and Explainable Question Answering (THE-QA)

2019 ◽  
Author(s):  
Shailaja Sampat
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Knowledge Representation ◽  
Question Answering ◽  
State Of The Art ◽  
Future Research ◽  
Semantic Parsing ◽  
Learning Models ◽  
Knowledge Types ◽  
Language And Vision

The ability of an agent to rationally answer questions about a given task is the key measure of its intelligence. While we have obtained phenomenal performance over various language and vision tasks separately, 'Technical, Hard and Explainable Question Answering' (THE-QA) is a new challenging corpus which addresses them jointly. THE-QA is a question answering task involving diagram understanding and reading comprehension. We plan to establish benchmarks over this new corpus using deep learning models guided by knowledge representation methods. The proposed approach will envisage detailed semantic parsing of technical figures and text, which is robust against diverse formats. It will be aided by knowledge acquisition and reasoning module that categorizes different knowledge types, identify sources to acquire that knowledge and perform reasoning to answer the questions correctly. THE-QA data will present a strong challenge to the community for future research and will bridge the gap between state-of-the-art Artificial Intelligence (AI) and 'Human-level' AI.

scholarly journals Neurosymbolic Knowledge Representation for Explainable and Trustworthy AI

2020 ◽  
Author(s):  
Paola Di Maio
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Knowledge Representation ◽  
Large Scale ◽  
System Level ◽  
Future Research ◽  
Knowledge Gaps ◽  
Educational Programmes ◽  
Systemic Problems ◽  
Research And Education

AI research and implementations are growing, and so are the risks associated with AI (Artificial Intelligence) developments, especially when it’s difficult to understand exactly what they do and how they work, both at a localized level, and at deployment, in particular when distributed and on a large scale. Governments are pouring massive funding to promote AI research and education, yet research results and claims, as well as the effectiveness of educational programmes, can be difficult to evaluate given the limited reproducibility of computations based on ML (machine learning) and poor explainability, which in turn limits the accountability of the systems and can cause cascading systemic problems and challenges including poor reproducibility, reliability, and overall lack of trustworthiness. This paper addresses some of the issues in Knowledge Representation for AI at system level, identifies a number of knowledge gaps and epistemological challenges as root causes of risks and challenges for AI, and proposes that neurosymbolic and hybrid KR approaches can serve as mechanisms to address some of the challenges. The paper concludes with a postulate and points to related and future research

Research on Construction of Database Domain Ontology

2013 ◽  
Vol 717 ◽  
pp. 736-741 ◽  
Author(s):  
Xue Jiang ◽  
Ying Liu ◽  
Ying Zhang ◽  
Zhen Fang ◽  
De Peng Dang
Keyword(s):  
Artificial Intelligence ◽  
Semantic Web ◽  
Knowledge Sharing ◽  
Domain Knowledge ◽  
Knowledge Engineering ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Domain Ontology ◽  
Tutoring System ◽  
Knowledge Learning

Domain ontology, which is widely used in knowledge engineering, artificial intelligence and semantic Web domain, describes concepts of the entities and mutual relationships between them. However, there is no existing research on construction of database domain ontology. Domain ontology of database will help to optimize associative knowledge learning in teaching and develop the intelligent tutoring system. It can also maximize domain knowledge sharing and reusing. In this paper, database domain ontology is constructed, and then we achieve the visualization. Finally we also implement the persistent storage of ontology and query.

scholarly journals Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy

Molecules ◽  
2021 ◽  
Vol 26 (4) ◽  
pp. 1022
Author(s):  
Hoang T. Nguyen ◽  
Kate T. Q. Nguyen ◽  
Tu C. Le ◽  
Guomin Zhang
Keyword(s):  
Artificial Intelligence ◽  
Flame Retardancy ◽  
Material Science ◽  
Construction Materials ◽  
Engineering Problem ◽  
Future Research ◽  
Fire Performance ◽  
Structural Components ◽  
Comprehensive Overview ◽  
Fire Engineering

The evaluation and interpretation of the behavior of construction materials under fire conditions have been complicated. Over the last few years, artificial intelligence (AI) has emerged as a reliable method to tackle this engineering problem. This review summarizes existing studies that applied AI to predict the fire performance of different construction materials (e.g., concrete, steel, timber, and composites). The prediction of the flame retardancy of some structural components such as beams, columns, slabs, and connections by utilizing AI-based models is also discussed. The end of this review offers insights on the advantages, existing challenges, and recommendations for the development of AI techniques used to evaluate the fire performance of construction materials and their flame retardancy. This review offers a comprehensive overview to researchers in the fields of fire engineering and material science, and it encourages them to explore and consider the use of AI in future research projects.

scholarly journals Artificial intelligence in hospitals: providing a status quo of ethical considerations in academia to guide future research

AI & Society ◽  
2021 ◽  
Author(s):  
Milad Mirbabaie ◽  
Lennart Hofeditz ◽  
Nicholas R. J. Frick ◽  
Stefan Stieglitz
Keyword(s):  
Artificial Intelligence ◽  
Citation Network ◽  
Status Quo ◽  
Biomedical Ethics ◽  
Future Research ◽  
Ethical Considerations ◽  
Ethical Discourse ◽  
The Status ◽  
Clinical Environments ◽  
Research Questions

AbstractThe application of artificial intelligence (AI) in hospitals yields many advantages but also confronts healthcare with ethical questions and challenges. While various disciplines have conducted specific research on the ethical considerations of AI in hospitals, the literature still requires a holistic overview. By conducting a systematic discourse approach highlighted by expert interviews with healthcare specialists, we identified the status quo of interdisciplinary research in academia on ethical considerations and dimensions of AI in hospitals. We found 15 fundamental manuscripts by constructing a citation network for the ethical discourse, and we extracted actionable principles and their relationships. We provide an agenda to guide academia, framed under the principles of biomedical ethics. We provide an understanding of the current ethical discourse of AI in clinical environments, identify where further research is pressingly needed, and discuss additional research questions that should be addressed. We also guide practitioners to acknowledge AI-related benefits in hospitals and to understand the related ethical concerns.

Constrained Petri Nets. Part II: Generalizations and extensions

1983 ◽  
Vol 6 (3-4) ◽  
pp. 333-374
Author(s):  
H.J.M. Goeman ◽  
L.P.J. Groenewegen ◽  
H.C.M. Kleijn ◽  
G. Rozenberg
Keyword(s):  
Petri Nets ◽  
Petri Net ◽  
Theoretical Framework ◽  
General Constraints ◽  
Context Free ◽  
Boolean Expressions

This paper continues the investigation froll1 [Goeman et al.] concerning the use of sets of places of a Petri net as additional (to input places) constraints for granting concession. Now interpretations of more general constraints are considered and expressed as Boolean expressions. This gives rise to various classes of constrained Petri nets. These are compared in the language theoretical framework introduced in [Goeman et al.]. An upperbound for the language defining power is found in the class of context-free programmed languages.

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