Journal articles: 'SWRL rule'

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Bouaicha, Souad, and Zizette Boufaida. "SWRLx." International Journal of Intelligent Information Technologies 12, no. 2 (April 2016): 53–69. http://dx.doi.org/10.4018/ijiit.2016040104.

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Although OWL (Web Ontology Language) and SWRL (Semantic Web Rule Language) add considerable expressiveness to the Semantic Web, they do have expressive limitations. For some reasoning problems, it is necessary to modify existing knowledge in an ontology. This kind of problem cannot be fully resolved by OWL and SWRL, as they only support monotonic inference. In this paper, the authors propose SWRLx (Extended Semantic Web Rule Language) as an extension to the SWRL rules. The set of rules obtained with SWRLx are posted to the Jess engine using rewrite meta-rules. The reason for this combination is that it allows the inference of new knowledge and storing it in the knowledge base. The authors propose a formalism for SWRLx along with its implementation through an adaptation of different object-oriented techniques. The Jess rule engine is used to transform these techniques to the Jess model. The authors include a demonstration that demonstrates the importance of this kind of reasoning. In order to verify their proposal, they use a case study inherent to interpretation of a preventive medical check-up.

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Plinere, Darya, and Arkady Borisov. "SWRL: Rule Acquisition Using Ontology." Scientific Journal of Riga Technical University. Computer Sciences 40, no. 1 (January 1, 2009): 117–22. http://dx.doi.org/10.2478/v10143-010-0016-8.

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SWRL: Rule Acquisition Using Ontology Nowadays rule-based systems are very common. The use of ontology-based systems is becoming ever more popular, especially in addition to the rule-based one. The most widely used ontology development platform is Protégé. Protégé provides a knowledge acquisition tool, but still the main issue of the ontology-based rule system is rule acquisition. This paper presents an approach to using SWRL rules Tab, a plug-in to Protégé, for rule acquisition. SWRL rules Tab transforms conjunctive rules to Jess rules in IF…THEN form.

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Bassiliades, Nick. "A Tool for Transforming Semantic Web Rule Language to SPARQL Infererecing Notation." International Journal on Semantic Web and Information Systems 16, no. 1 (January 2020): 87–115. http://dx.doi.org/10.4018/ijswis.2020010105.

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Semantic web rule language (SWRL) combines web ontology language (OWL) ontologies with horn logic rules of the rule markup language (RuleML) family. Being supported by ontology editors, rule engines and ontology reasoners, it has become a very popular choice for developing rule-based applications on top of ontologies. However, SWRL is probably not going to become a WWW Consortium standard, prohibiting industrial acceptance. On the other hand, SPARQL Inferencing Notation (SPIN) has become a de-facto industry standard to represent SPARQL rules and constraints on semantic web models, building on the widespread acceptance of SPARQL (SPARQL Protocol and RDF Query Language). In this article, we argue that the life of existing SWRL rule-based ontology applications can be prolonged by converting them to SPIN. To this end, we have developed the SWRL2SPIN tool in Prolog that transforms SWRL rules into SPIN rules, considering the object-orientation of SPIN, i.e. linking rules to the appropriate ontology classes and optimizing them, as derived by analysing the rule conditions.

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Zhai, Zhaoyu, José-Fernán Martínez Ortega, Néstor Lucas Martínez, and Pedro Castillejo. "A Rule-Based Reasoner for Underwater Robots Using OWL and SWRL." Sensors 18, no. 10 (October 16, 2018): 3481. http://dx.doi.org/10.3390/s18103481.

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Web Ontology Language (OWL) is designed to represent varied knowledge about things and the relationships of things. It is widely used to express complex models and address information heterogeneity of specific domains, such as underwater environments and robots. With the help of OWL, heterogeneous underwater robots are able to cooperate with each other by exchanging information with the same meaning and robot operators can organize the coordination easier. However, OWL has expressivity limitations on representing general rules, especially the statement “If … Then … Else …”. Fortunately, the Semantic Web Rule Language (SWRL) has strong rule representation capabilities. In this paper, we propose a rule-based reasoner for inferring and providing query services based on OWL and SWRL. SWRL rules are directly inserted into the ontologies by several steps of model transformations instead of using a specific editor. In the verification experiments, the SWRL rules were successfully and efficiently inserted into the OWL-based ontologies, obtaining completely correct query results. This rule-based reasoner is a promising approach to increase the inference capability of ontology-based models and it achieves significant contributions when semantic queries are done.

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Jajaga, Edmond, and Lule Ahmedi. "C-SWRL: A Unique Semantic Web Framework for Reasoning Over Stream Data." International Journal of Semantic Computing 11, no. 03 (September 2017): 391–409. http://dx.doi.org/10.1142/s1793351x17400165.

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The synergy of Data Stream Management Systems and Semantic Web applications has steered towards a new paradigm known as Stream Reasoning. The Semantic Web standards for knowledge base modeling and querying, namely RDF, OWL and SPARQL, has extensively been used by the Stream Reasoning community. However, the Semantic Web rule languages, such as SWRL and RIF, have never been used in stream data applications. Instead, different non-Semantic Web rule systems have been approached. Since RIF is primarily intended for exchanging rules among systems, we focused on SWRL applications with stream data. This proves difficult following the SWRL’s open world semantics. To overcome SWRL’s expressivity issues we propose an infrastructure extension, which will enable SWRL reasoning with stream data. Namely, a query processing system, such as C-SPARQL, was layered under SWRL to support closed-world and time-aware reasoning. Moreover, OWLAPI constructs were utilized to enable non-monotonicity, while SPARQL constructs were used to enable negation as failure. Water quality monitoring was used as a validation domain of the proposed system.

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Zhang, Xian-kun, Xin-ya Gao, Qian Zhang, and Jia Jia. "Research on the Rough Extension of Ontology Description Language of SWRL." Journal of Electrical and Computer Engineering 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/5636254.

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Although ontology has strong ability to express knowledge, it is difficult to express uncertain or imprecise information using the language of ontology. In order to improve the ability to express uncertain information, this paper extends the Semantic Web Rule Language (SWRL) and gives the extension of reasoning rules. According to the rough ontology and the rules of SWRL, it updates the knowledge base. Firstly, the concept of rough ontology and the extension of rough relationship of ontology are put forward; secondly, it gives the extension method for concepts, relationships, axioms, examples, and rules of SWRL. Finally, a psychological counseling case shows that the method can well express the uncertainty of knowledge, and it is able to well express the reasoning rules.

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de Farias, Tarcisio Mendes, Ana Roxin, and Christophe Nicolle. "SWRL rule-selection methodology for ontology interoperability." Data & Knowledge Engineering 105 (September 2016): 53–72. http://dx.doi.org/10.1016/j.datak.2015.09.001.

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Milanovic, Milan, Dragan Gasevic, Adrian Giurca, Gerd Wagner, Sergey Lukichev, and Vladan Devedzic. "Model transformations to bridge concrete and abstract syntax of web rule languages." Computer Science and Information Systems 6, no. 2 (2009): 47–85. http://dx.doi.org/10.2298/csis0902047m.

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This paper presents a solution to bridging the abstract and concrete syntax of a Web rule languages by using model transformations. Current specifications of Web rule languages such as Semantic Web Rule Language (SWRL) or RuleML define their abstract syntax (e.g., metamodel) and concrete syntax (e.g., XML schema) separately. Although the recent research in the area of Model-Driven Engineering (MDE) demonstrates that such a separation of two types of syntax is a good practice (due to the complexity of languages), one should also have tools that check validity of rules written in a concrete syntax with respect to the abstract syntax of the rule language. In this study, we use the REWERSE I1 Rule Markup Language (R2ML), SWRL, and Object Constraint Language (OCL), whose abstract syntax is defined by using metamodeling, while their textual concrete syntax is defined by using either XML/RDF schema or Extended Backus-Naur Form (EBNF) syntax. We bridge this gap by a bi-directional transformation defined in a model transformation language (ATLAS Transformation Language, ATL). This transformation allowed us to discover a number of issues in both web rule language metamodels and their corresponding concrete syntax, and thus make them fully compatible. This solution also enables for sharing web rules between different web rule languages.

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Dai, Hong Qin. "The Research on Intelligent Clothing Recommendation System Based on Ontology." Advanced Materials Research 175-176 (January 2011): 827–31. http://dx.doi.org/10.4028/www.scientific.net/amr.175-176.827.

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Ontology which is a description of knowledge has been applied to many fields. Some intelligent systems based on ontology have been developed. In the paper, a clothing recommendation system based on ontology is developed. The recommendation system mainly includes two parts: knowledge base and inference engine. The structural knowledge of clothing is represented by using ontology and some constraint knowledge is described by SWRL. The clothing recommendation process are carried out using JESS, a rule engine for the Java platform, by mapping OWL-based clothing knowledge and SWRL-based design rules into JESS facts and JESS rules, respectively.

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Bouihi, Bouchra, and Mohamed Bahaj. "Ontology and Rule-Based Recommender System for E-learning Applications." International Journal of Emerging Technologies in Learning (iJET) 14, no. 15 (August 1, 2019): 4. http://dx.doi.org/10.3991/ijet.v14i15.10566.

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The continuous growth of the internet has given rise to an overwhelming mass of learning materials. Which has increased the demand for a recommendation system to filter information and to deliver the learning materials that fit learners learning context. In this paper, we propose an architecture of a semantic web based recommender system. The proposed architecture is a redesigned architecture of the classical 3-tiers web application architecture with an additional semantic layer. This layer holds two semantic subsystems: an Ontology-based subsystem and SWRL (Semantic Web Rule Language) rules one. The Ontology subsystem is used as a reusable and sharable domain knowledge to model the learning content and context. The SWRL rules are used as a recommendation and filtering technique based on learning object relevance and weighting. These rules are organized into four categories: Learning History Rules (LHR), Learning Performance Rules (LPR), Learning Social Network Rules (LSNR) and Learning Pathway Rules (PR).

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Gunawan, Ridowati, and Khabib Mustofa. "Finding Knowledge from Indonesian Traditional Medicine using Semantic Web Rule Language." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 6 (December 1, 2017): 3674. http://dx.doi.org/10.11591/ijece.v7i6.pp3674-3682.

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One of the natural resources in Indonesia is a lot of plants which can be used in healing diseases. Thosekinds of plants can be used in “Jamu”. Jamu is a name given to traditional medicine in Indonesia. Usually Jamu is composed from several plants as ingredients. Particularly, some parts of the plant like the leaves, roots, or branches have different purpose in Jamu. Nowadays the knowledge about Jamu can be known by building Ontology. Ontology can be built and developed to enrich the content. Knowledge in Ontology is built by several rules using Semantic Web Rule Language (SWRL).Knowledge gained from SWRL is easily searchable so that users can double check the results obtained.

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Ghosh, Bhaswati, Partha S. Ghosh, and Iftikhar U. Sikder. "Modeling a Classification Scheme of Epileptic Seizures Using Ontology Web Language." International Journal of Computational Models and Algorithms in Medicine 1, no. 1 (January 2010): 45–60. http://dx.doi.org/10.4018/jcmam.2010072004.

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Ontology-based disease classification offers a way to rigorously assign disease types and to reuse diagnostic knowledge. However, ontology itself is not sufficient for fully representing the complex knowledge needed in classification schemes which are continuously evolving. This article describes the application of SWRL/OWL-DL to the representation of knowledge intended for proper classification of a complex neurological condition, namely epilepsy. The authors present a rigorous and expandable approach to the ontological classification of epileptic seizures based on the 1981ILAE classification. It provides a classification knowledge base that can be extended with rules that describe constraints in SWRL. Moreover, by transforming an OWL classification scheme into JESS (rule engine in Java platform) facts and by transforming SWRL constraints into JESS, logical inferences and reasoning provide a mechanism to discover new knowledge and facts. The logic representation of epileptic classification amounts to greater community understanding among practitioners, knowledge reuse and interoperability.

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BIKAKIS, ANTONIS, PAUL FODOR, ADRIAN GIURCA, and LEORA MORGENSTERN. "Introduction to the special issue on the International Web Rule Symposia 2012–2014." Theory and Practice of Logic Programming 16, no. 3 (March 7, 2016): 243–46. http://dx.doi.org/10.1017/s1471068416000028.

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The annual International Web Rule Symposium (RuleML) is an international conference on research, applications, languages, and standards for rule technologies. It has evolved from an annual series of international workshops since 2002, international conferences in 2005 and 2006, and international symposia since 2007. It is the flagship event of the Rule Markup and Modeling Initiative (RuleML, http://ruleml.org), a nonprofit umbrella organization of several technical groups from academia, industry, and government working on rule technology and its applications. RuleML is the leading conference to build bridges between academia and industry in the field of rules and its applications, especially as part of the semantic technology stack. It is devoted to rule-based programming and rule-based systems including production rules systems, logic programming rule engines, and business rules engines/business rules management systems; Semantic Web rule languages and rule standards (e.g., RuleML, SWRL, RIF, PRR, SBVR, DMN, CL, Prolog); rule-based event processing languages and technologies; and research on inference rules, transformation rules, decision rules, production rules, and ECA rules.

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Vang, Tran Anh, and Ping Yu Jiang. "Using Ontology and Rule-Based Reasoning for Supporting Automatic Process Plan for Milling Prismatic Parts." Applied Mechanics and Materials 127 (October 2011): 531–36. http://dx.doi.org/10.4028/www.scientific.net/amm.127.531.

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Nowadays, the manufacturing enterprises are in a highly dynamic environment because of the continuous changes of market. Therefore, the integration of product’s data and manufacturing data are paid much attention of researchers due to its role in improving the reliability and flexibility of manufacturing process. In this paper, we present a new approach of using ontology and rule-based reasoning to support this integration. Contributions of this research consist of establishing an architecture of semantic CAPP system (s-CAPP), using OWL (Web Ontology Language) and SWRL (Semantic Web Rule Language) to describe the information of machining parts and manufacturing equipments, presenting the definition of feasible process plan through rule-based reasoning with SWRL and JESS, and giving a case study on s-CAPP system.

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Yilmaz, C., C. Comert, and D. Yildirim. "SDQO AND SFO, ONTOLOGIES FOR SPATIAL DATA QUALITY ASSESSMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1275–80. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1275-2019.

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<p><strong>Abstract.</strong> Spatial quality assessment is based on the conformance of data to its specifications or fitness for users’ purpose. These specifications and the users’ purposes include the rules and constraints that a dataset should comply with. Assessing the compliance of data to the rules is still an active research subject and rule-based approach is the common method. For the efficient rule-based system implementation, it is desired to automate assessment process with a domain-independent and web-based approach. Reasoning capability and re-usability of semantic web components are expected to promote efficient implementation. In literature, many domains such as agriculture, music, Linked Data and geospatial domain etc. apply ontology-based methods for quality management. There is a need to model geospatial quality concepts and rules in a domain-independent way to automate the quality management process. In our model of rule formalism, we use Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL). We devise two types of ontologies. These are; the specification ontologies (SfO) and the Spatial Data Quality Ontology (SDQO). SfO is to be created by domain experts/users to define rules according to specifications. SDQO is responsible with quality assessment; it is domain independent and makes assessment based on the rules defined by any SfO for the related domain. The quality elements are domain and toposemantic consistency that assessed by SWRL. In this paper, the design considerations of the ontologies for quality assessment are explained with an example.</p>

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Jabardi, Mohammed, and Asaad Hadi. "Ontology Meter for Twitter Fake Accounts Detection." International Journal of Intelligent Engineering and Systems 14, no. 1 (February 28, 2021): 410–19. http://dx.doi.org/10.22266/ijies2021.0228.38.

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One of the most popular social media platforms, Twitter is used by millions of people to share information, broadcast tweets, and follow other users. Twitter is an open application programming interface and thus vulnerable to attack from fake accounts, which are primarily created for advertisement and marketing, defamation of an individual, consumer data acquisition, increase fake blog or website traffic, share disinformation, online fraud, and control. Fake accounts are harmful to both users and service providers, and thus recognizing and filtering out such content on social media is essential. This study presents a new approach to detect fake Twitter accounts using ontology and Semantic Web Rule Language (SWRL) rules. SWRL rules-based reasoner is utilized under predefined rules to infer whether the profile is trust or fake. This approach achieves a high detection accuracy of 97%. Furthermore, ontology classifier is an interpretable model that offers straightforward and human-interpretable decision rules.

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Aminu, Enesi F., Olaide N. Oyelade, and Ibrahim S. Shehu. "Rule Based Communication Protocol between Social Networks using Semantic Web Rule Language (SWRL)." International Journal of Modern Education and Computer Science 8, no. 2 (February 8, 2016): 22–29. http://dx.doi.org/10.5815/ijmecs.2016.02.03.

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Hwang, Jae W., and Shmuel Rotenstreich. "A Policy-Based Team Collaboration." International Journal of e-Collaboration 8, no. 1 (January 2012): 1–16. http://dx.doi.org/10.4018/jec.2012010101.

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This paper presents a policy-based coordination model for team collaboration. Team collaboration requires an agreement that utilizes a negotiation protocol to find candidate teams and to decide on a collaboration partner. The decision relies on policies that are rules governing team situations in an organization. Contexts and rules allow reasoning about team situations. The authors describe a policy-based negotiation protocol. It introduces an ontology-based whiteboard component that uses the Semantic Web technologies such as Web Ontology Language (OWL), Semantic Web Rule Language (SWRL), and Semantic Query-enhanced Web Rule Language (SQWRL). The negotiation protocol facilitates whiteboards as a computational foundation for awareness of situations and policies, and it assists with the final decision using a measure based on the combination of rule-based queries and functions.

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Zeng, Qing Liang, Zhi Hai Liu, Li Rong Wan, and Yong Hua Zhao. "Research on Modeling and Reasoning Framework of Ontology-Based Equipment Selection and Matching Expert System in Fully Mechanized Caving Face." Applied Mechanics and Materials 16-19 (October 2009): 764–68. http://dx.doi.org/10.4028/www.scientific.net/amm.16-19.764.

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In this paper, ontology modeling language and reasoning engines are studied and applied, framework model of ontology-based expert system is built up, the model tree of classes and instances of fully mechanized caving used equipments is brought forward, OWL-based equipment knowledge base is created by using protégé tool, knowledge rules of mining equipments selection and matching are analyzed, and SWRL-based selection and matching rules are designed. Finally, the rule conversion and reasoning is realized in JESS environment.

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SAMUEL, KEN, LEO OBRST, SUZETTE STOUTENBERG, KAREN FOX, PAUL FRANKLIN, ADRIAN JOHNSON, KEN LASKEY, DEBORAH NICHOLS, STEVE LOPEZ, and JASON PETERSON. "Translating OWL and semantic web rules into prolog: Moving toward description logic programs." Theory and Practice of Logic Programming 8, no. 3 (May 2008): 301–22. http://dx.doi.org/10.1017/s1471068407003249.

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AbstractWe are researching the interaction between the rule and the ontology layers of the Semantic Web, by comparing two options: 1) using OWL and its rule extension SWRL to develop an integrated ontology/rule language, and 2) layering rules on top of an ontology with RuleML and OWL. Toward this end, we are developing the SWORIER system, which enables efficient automated reasoning on ontologies and rules, by translating all of them into Prolog and adding a set of general rules that properly capture the semantics of OWL. We have also enabled the user to make dynamic changes on the fly, at run time. This work addresses several of the concerns expressed in previous work, such as negation, complementary classes, disjunctive heads, and cardinality, and it discusses alternative approaches for dealing with inconsistencies in the knowledge base. In addition, for efficiency, we implemented techniques called extensionalization, avoiding reanalysis, and code minimization.

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Jain, Sarika, Sumit Sharma, Jorrit Milan Natterbrede, and Mohamed Hamada. "Rule-Based Actionable Intelligence for Disaster Situation Management." International Journal of Knowledge and Systems Science 11, no. 3 (July 2020): 17–32. http://dx.doi.org/10.4018/ijkss.2020070102.

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Managing natural disasters is a social responsibility as they might cause a gloomy impact on human life. Efficient and timely alert systems for public and actionable recommendations for decision makers may well decrease the number of casualties. Web semantics strengthen the description of web resources for exploiting them better and making them more meaningful for both human and machine. In this work, the authors propose a semantic rule-based approach for disaster situation management (DSM) to reach the next level of decision-making power and its architecture for providing actionable intelligence in the domain of the earthquake. The system itself is based on a data pre-processing layer, a computation layer, and the middle layer relies on an extensive rule base of experts' advice stored over time and a disaster ontology along with its inherent semantics. The rule-based reasoning approach uses this knowledge base in combination with the expert rule base, written in SWRL rules, to infer recommendations for the response to an earthquake.

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Li, Yan Ling, Yi Duo Liang, and Jun Zhai. "Fuzzy Knowledge Representation Based on Fuzzy Linguistic Variable Ontology and SWRL on the Semantic Web." Applied Mechanics and Materials 58-60 (June 2011): 1707–11. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1707.

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Ontology is adopted as a standard for knowledge representation on the Semantic Web, and Ontology Web Language (OWL) is used to add structure and meaning to web applications. In order to share and resue the fuzzy knowledge on the Semantic Web, we propose the fuzzy linguistic variables ontology (FLVO), which utilizes ontology to represent formally the fuzzy linguistic variables and defines the semantic relationships between fuzzy concepts. Then fuzzy rules are described in Semantic Web Rule Language (SWRL) on the basis of FLVO model. Taking a sample case for students’ performance in physics for example, the fuzzy rule management system is built by using the tool protégé and SWRLTab, which shows that this research enables distributed fuzzy applications on the Semantic Web.

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Li, Wan, Bi Hua Zhou, Qi Zhang, Ya Peng Fu, and Tao Wang. "Research on Ontology Knowledge Based System of Meteorological and Hydrological Support." Applied Mechanics and Materials 373-375 (August 2013): 1027–30. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1027.

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According to knowledge sharing&reusing problem and system extensibility&portability problem in meteorological and hydrological support, the knowledge concepts, attributes, instances, hierarchy and relationships were clarified; the ontological knowledge base was established by OWL ontology language and SWRL rule language. With the help of Racer, Protégé, Jess, and class positioning algorithm, class testing algorithm, rule testing algorithm, the meteorological and hydrological support prototype system was implemented by Eclipse, ProtegeInEclipse plug-in and Jena. The system includes the server, the client and the processing controller. It provides artificial intelligence techniques means for knowledge maintenance and usage in meteorological and hydrological field.

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Yang, Qing, Chen Zuo, Xingxing Liu, Zhichao Yang, and Hui Zhou. "Risk Response for Municipal Solid Waste Crisis Using Ontology-Based Reasoning." International Journal of Environmental Research and Public Health 17, no. 9 (May 9, 2020): 3312. http://dx.doi.org/10.3390/ijerph17093312.

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Many cities in the world are besieged by municipal solid waste (MSW). MSW not only pollutes the ecological environment but can even induce a series of public safety crises. Risk response for MSW needs novel changes. This paper innovatively adopts the ideas and methods of semantic web ontology to build an ontology-based reasoning system for MSW risk response. Through the integration of crisis information and case resources in the field of MSW, combined with the reasoning ability of Semantic Web Rule Language (SWRL), a system of rule reasoning for risk transformation is constructed. Knowledge extraction and integration of MSW risk response can effectively excavate semantic correlation of crisis information along with key transformation points in the process of crisis evolution through rule reasoning. The results show that rule reasoning of transformation can effectively improve intelligent decision-making regarding MSW risk response.

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Cantone, Domenico, Marianna Nicolosi-Asmundo, and Daniele Francesco Santamaria. "An Improved Set-based Reasoner for the Description Logic 𝒟ℒD4,×†." Fundamenta Informaticae 178, no. 4 (February 9, 2021): 315–46. http://dx.doi.org/10.3233/fi-2021-2009.

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We present a KE-tableau-based implementation of a reasoner for a decidable fragment of (stratified) set theory expressing the description logic 𝒟ℒ〈4LQSR,×〉(D) (𝒟ℒD4,×, for short). Our application solves the main TBox and ABox reasoning problems for 𝒟ℒD4,×. In particular, it solves the consistency and the classification problems for 𝒟ℒD4,×-knowledge bases represented in set-theoretic terms, and a generalization of the Conjunctive Query Answering problem in which conjunctive queries with variables of three sorts are admitted. The reasoner, which extends and improves a previous version, is implemented in C++. It supports 𝒟ℒD4,×-knowledge bases serialized in the OWL/XML format and it admits also rules expressed in SWRL (Semantic Web Rule Language).

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Sedbrook, Tod, and Richard I. Newmark. "Automating REA Policy Level Specifications with Semantic Web Technologies." Journal of Information Systems 22, no. 2 (September 1, 2008): 249–77. http://dx.doi.org/10.2308/jis.2008.22.2.249.

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ABSTRACT: Enterprise modelers require tools and techniques that consistently represent and logically apply domain knowledge. Current modeling approaches rely on entity relationship or unified modeling diagrams to represent semantic descriptions of business exchanges. However, it remains difficult to transform the implicit metadata, ontologies, and logic embedded in diagrams into a coherent form that can be interpreted by machines and delivered across the web. This study explores the uniting of machine processing capabilities of semantic web technologies with resource event agent (REA) enterprise ontologies to model complex multienterprise partnerships. Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL) were used to model REA policies for a distributed e-commerce partnership selling nearly new vehicles. We combine a specialized REA application ontology with semantic technologies to direct multienterprise collaborations. We present a prototype that encodes the ontology's concepts within OWL and SWRL and explore these machine-readable representations within the context of a case study.

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Liu, Zhi Hai, Qing Liang Zeng, Cheng Long Wang, and Yu Shan Li. "Research of Equipment Selection and Matching Expert System in Fully Mechanized Caving Face Based on Ontology." Key Engineering Materials 419-420 (October 2009): 117–20. http://dx.doi.org/10.4028/www.scientific.net/kem.419-420.117.

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The technologies of expert system, ontology modeling and reasoning are studied and applied in this paper. A framework of ontology-based expert system for equipment selection and matching in fully mechanized caving face is built up. The OWL(Web Ontology Language) based equipment knowledge base is set up, and the redundant test is made in the protégé environment. The equipment selection and matching rules are built up by using SWRL(Semantic Web Rule Language). By using JAVA language, the output of equipment selection expert system is optimized based on the theory of Analytic Hierarchy Process. At last, the functions of the expert system are achieved under JESS(Java Expert System Shell) environment. Practical application shows that the equipments selected and matched by ontology-based expert system are more realistic.

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RODRÍGUEZ, DANIEL, ELENA GARCÍA, SALVADOR SÁNCHEZ, and CARLOS RODRÍGUEZ-SOLANO NUZZI. "DEFINING SOFTWARE PROCESS MODEL CONSTRAINTS WITH RULES USING OWL AND SWRL." International Journal of Software Engineering and Knowledge Engineering 20, no. 04 (June 2010): 533–48. http://dx.doi.org/10.1142/s0218194010004876.

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The Software & Systems Process Engineering meta-model (SPEM) allows the modelling of software processes using OMG (Object Management Group) standards such as the MOF (Meta-Object Facility) and UML (Unified Modelling Language) making it possible to represent software processes using tools compliant with UML. Process definition encompasses both the static and dynamic structure of roles, tasks and work products together with imposed constraints on those elements. However, the latter requires support for constraint enforcement that is not always directly available in SPEM. Such constraint-checking behaviour could be used to detect possible mismatches between process definitions and the actual processes being carried out in the course of a project. This paper approaches the modelling of such constraints using the SWRL (Semantic Web Rule Language), which is a W3C recommendation. To do so, we need to first represent generic processes modelled with SPEM using an underlying ontology based on the OWL (Ontology Web Language) representation together with data derived from actual projects.

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Kim, Kyoung-Yun, Seongah Chin, Ohbyung Kwon, and R. Darin Ellis. "Ontology-based modeling and integration of morphological characteristics of assembly joints for network-based collaborative assembly design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 23, no. 1 (December 16, 2008): 71–88. http://dx.doi.org/10.1017/s0890060409000110.

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AbstractThis paper presents our research on developing an ontology-based framework that can represent morphological characteristics related to assembly joints. Joints within the physical structure of an assembly are inevitable because of the limitations of component geometries and the associated, required engineering properties. Consequently, a framework is needed that can capture and propagate assembly design and joint information in a robust assembly model throughout the entire product development processes. The framework and model are based on an understanding of the morphological characteristics of an assembly and its different physical effects. The morphological characteristics are consequences of the principal physical processes and of the design intentions. Therefore, the morphological characteristics should be carefully represented while considering the geometry and topology of assembly joints. In this research, assembly joint topology is defined by a mereotopology, which is a region-based theory for the parts and associated concepts. This formal ontology can differentiate often ambiguous assembly and joining relations. Furthermore, the mereotopological definitions for assembly joints are implemented in Semantic Web Rule Language (SWRL) rules and Web Ontology Language triples. This process provides universality to the mereotopological definitions. Two geometrically and topologically similar joint pairs are presented to describe how the assembly joints can be defined in mereotopology and be transformed into SWRL rules. Web3D is also employed to support network-enabled sharing of assembly geometry. Finally, the proposed modeling framework is demonstrated using a real fixture assembly. This case study demonstrates the usability of the proposed framework for network-based design collaboration.

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Tan, Peng, Feng He, and Chen Min Yan. "Research on Consistency Verification of Semantic Business Process Model Based on SWRL." Advanced Materials Research 756-759 (September 2013): 1595–99. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1595.

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At the moment Semantic Business Process Model (SBPM) build on the semantic annotation, the specification of process activity, the control flow between activities and business functions of process. However, the correctness of SBPM is based on technical knowledge and experiences of the model designer, which is apparently not feasible for complex and dynamic business processes. Therefore, the goal of this paper is present a formalization description which was understood by business experts and model designers, establishing a new method to verify the consistency between SBPM and requirement of enterprise. Our techniques are based on the SWRL and Business rule.

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Cao, Qiushi, Ahmed Samet, Cecilia Zanni-Merk, François de Bertrand de Beuvron, and Christoph Reich. "Combining chronicle mining and semantics for predictive maintenance in manufacturing processes." Semantic Web 11, no. 6 (October 29, 2020): 927–48. http://dx.doi.org/10.3233/sw-200406.

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Within manufacturing processes, faults and failures may cause severe economic loss. With the vision of Industry 4.0, artificial intelligence techniques such as data mining play a crucial role in automatic fault and failure prediction. However, due to the heterogeneous nature of industrial data, data mining results normally lack both machine and human-understandable representation and interpretation of knowledge. This may cause the semantic gap issue, which stands for the incoherence between the knowledge extracted from industrial data and the interpretation of the knowledge from a user. To address this issue, ontology-based approaches have been used to bridge the semantic gap between data mining results and users. However, only a few existing ontology-based approaches provide satisfactory knowledge modeling and representation for all the essential concepts in predictive maintenance. Moreover, most of the existing research works merely focus on the classification of operating conditions of machines, while lacking the extraction of specific temporal information of failure occurrence. This brings obstacles for users to perform maintenance actions with the consideration of temporal constraints. To tackle these challenges, in this paper we introduce a novel hybrid approach to facilitate predictive maintenance tasks in manufacturing processes. The proposed approach is a combination of data mining and semantics, within which chronicle mining is used to predict the future failures of the monitored industrial machinery, and a Manufacturing Predictive Maintenance Ontology (MPMO) with its rule-based extension is used to predict temporal constraints of failures and to represent the predictive results formally. As a result, Semantic Web Rule Language (SWRL) rules are constructed for predicting the occurrence time of machinery failures in the future. The proposed rules provide explicit knowledge representation and semantic enrichment of failure prediction results, thus easing the understanding of the inferred knowledge. A case study on a semi-conductor manufacturing process is used to demonstrate our approach in detail. The evaluation of results shows that the MPMO ontology is free of bad practices in the structural, functional, and usability-profiling dimensions. The constructed SWRL rules posses more than 80% of True Positive Rate, Precision, and F-measure, which shows promising performance in failure prediction.

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Thomas, Manoj A., Richard T. Redmond, and Victoria Y. Yoon. "Using Ontological Reasoning for an Adaptive E-Commerce Experience." International Journal of Intelligent Information Technologies 5, no. 4 (October 2009): 41–52. http://dx.doi.org/10.4018/jiit.2009080703.

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As e-commerce applications proliferates the Web, the authors are often overwhelmed by the task of sifting through the copious volumes of information. Since the nature of foraging for information in such digital spaces can be characterized as the interaction between internal task representation and the external problem domain, the authors look at how expert systems can be used to reduce complexity of the task. They describe a conceptual framework to analyze user interactions based on mental representations. They also detail an expert system implementation using the ontology language OWL to express the semantics of the representations and the rule language SWRL to define the rule base for contextual reasoning. The chapter illustrates how an expert system can be used to guide users in an e-commerce setting by orchestrating a cognitive fit between the task environment and the task solution.

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Liang, Yi Fang, Yan Ru Zhong, Mei Fa Huang, and Cong Wen Zeng. "Ontology-Based Assembly Design Representation for Model Reuse." Applied Mechanics and Materials 775 (July 2015): 484–90. http://dx.doi.org/10.4028/www.scientific.net/amm.775.484.

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Assembly is an important part in production and development of complex products. Flexible assembly retrieval is one of the key issues to find the reusable model. The main objective of this paper is to extend the model of ontology-based assembly design for model reuse. Firstly, we propose a representation of assembly structural data including topological structure, assembly semantics, and geometrical information. Secondly, we enrich assembly design (AsD) ontology for knowledge captured and shared in Web Ontology Language 2 Description Logic (OWL 2 DL) and Semantic Web Rule Language (SWRL). And then, we define the matching strategies and similarity assessment for two matched models. Finally, we illustrate the validity of assembly design representation for model reuse through experiments.

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Zhang, Xia, Youchao Sun, and Yanjun Zhang. "Ontology modelling of intelligent HCI in aircraft cockpit." Aircraft Engineering and Aerospace Technology 93, no. 5 (June 10, 2021): 794–808. http://dx.doi.org/10.1108/aeat-11-2020-0255.

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Purpose Semantic modelling is an essential prerequisite for designing the intelligent human–computer interaction in future aircraft cockpit. The purpose of this paper is to outline an ontology-based solution to this issue. Design/methodology/approach The scenario elements are defined considering the cognitive behaviours, system functions, interaction behaviours and interaction situation. The knowledge model consists of a five-tuple array including concepts, relations, functions, axioms and instances. Using the theory of belief-desire-intention, the meta-model of cognitive behaviours is established. The meta-model of system functions is formed under the architecture of sub-functions. Supported by information flows, the meta-model of interaction behaviours is presented. Based on the socio-technical characteristics, the meta-model of interaction situation is proposed. The knowledge representation and reasoning process is visualized with the semantic web rule language (SWRL) on the Protégé platform. Finally, verification and evaluation are carried out to assess the rationality and quality of the ontology model. Application scenarios of the proposed modelling method are also illustrated. Findings Verification results show that the knowledge reasoning based on SWRL rules can further enrich the knowledge base in terms of instance attributes and thereby improve the adaptability and learning ability of the ontology model in different simulations. Evaluation results show that the ontology model has a good quality with high cohesion and low coupling. Practical implications The approach presented in this paper can be applied to model complex human–machine–environment systems, from a semantics-driven perspective, especially for designing future cockpits. Originality/value Different from the traditional approaches, the method proposed in this paper tries to deal with the socio-technical modelling issues concerning multidimensional information semantics. Meanwhile, the constructed model has the ability of autonomous reasoning to adapt to complex situations.

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Sun, Xiaolei, Yu Zhang, and Jing Chen. "High-Level Smart Decision Making of a Robot Based on Ontology in a Search and Rescue Scenario." Future Internet 11, no. 11 (October 31, 2019): 230. http://dx.doi.org/10.3390/fi11110230.

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The search and rescue (SAR) scenario is complex and uncertain where a robot needs to understand the scenario to make smart decisions. Aiming at the knowledge representation (KR) in the field of SAR, this paper builds an ontology model that enables a robot to understand how to make smart decisions. The ontology is divided into three parts, namely entity ontology, environment ontology, and task ontology. Web Ontology Language (OWL) is adopted to represent these three types of ontology. Through ontology and Semantic Web Rule Language (SWRL) rules, the robot infers the tasks to be performed according to the environment state and at the same time obtains the semantic information of the victims. Then, the paper proposes an ontology-based algorithm for task planning to get a sequence of atomic actions so as to complete the high-level inferred task. In addition, an indoor experiment was designed and built for the SAR scenario using a real robot platform—TurtleBot3. The correctness and usability of the ontology and the proposed methods are verified by experiments.

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Zhao, Yuanhong, and Qingping Yang. "Development of an ontology-based semantic building post-occupancy evaluation framework." International Journal of Metrology and Quality Engineering 12 (2021): 19. http://dx.doi.org/10.1051/ijmqe/2021019.

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Post-occupancy evaluation (POE) is a systematic method to evaluate the actual building performance against the theoretical design intents after the building has been occupied for some time, to understand how the building is performing and to capture lessons learned. The POE offers an opportunity to investigate the buildings' actual performance based upon the occupants' satisfaction levels in the aspects of building overall design, indoor environmental quality, thermal comfort, etc. However, as the key part of POE, occupant satisfaction assessment (OSA) is a missing link in the building performance evaluation (BPE) domain, and there is not a systematic evaluation method for the OSA. Moreover, it is time-consuming and error-prone to conduct the OSA manually. This paper presents from the end-user's satisfaction perspective a semantic post-occupancy evaluation ontology (POEontology) to facilitate the occupant satisfaction assessment of buildings, with the ultimate aim of optimizing building operation guidelines, and improving occupants' use experience quality and well-being. An ontology-based knowledge model has been developed to capture the fragmented knowledge of building use satisfaction assessment in the POE domain, with the benchmarking evaluation rules encoded in Semantic Web Rule Language (SWRL) to enable automatic rule-based rating and reasoning. This ontology model also enables the effective OSA-related knowledge retrieving and sharing, and promotes its implementation in the POE domain. A field study has been conducted based upon the Building Use Study (BUS) methodology to validate the proposed ontology framework.

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Xu, Da, Mohamed Hedi Karray, and Bernard Archimède. "A semantic-based decision support platform to assist products’ eco-labeling process." Industrial Management & Data Systems 117, no. 7 (August 14, 2017): 1340–61. http://dx.doi.org/10.1108/imds-09-2016-0405.

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Purpose With the rising concern of safety, health and environmental performance, eco-labeled product and service are becoming more and more popular. However, the long and complex process of eco-labeling sometimes demotivates manufacturers and service providers to be certificated. The purpose of this paper is to propose a decision support platform aiming at further improvement and acceleration of the eco-labeling process in order to democratize a broader application and certification of eco-labels, also to consolidate the credibility and validity of eco-labels. Design/methodology/approach This decision support platform is based on a comprehensive knowledge base composed of various domain ontologies that are constructed according to an official eco-label criteria documentation. Findings Through standard Resource Description Framework and Web Ontology Language ontology query interface, the assets of the decision support platform will stimulate domain knowledge sharing and can be applied into other applications. A case study of laundry detergent eco-labeling process is also presented in this paper. Originality/value The authors present a reasoning methodology based on inference with Semantic Web Rule Language (SWRL) rules which allows decision making with explanation.

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Zhang, Daxin, Jinyue Zhang, Jianing Guo, and Haiming Xiong. "A Semantic and Social Approach for Real-Time Green Building Rating in BIM-Based Design." Sustainability 11, no. 14 (July 22, 2019): 3973. http://dx.doi.org/10.3390/su11143973.

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While many countries have developed green building rating systems (GBRSs) to promote the concept of green buildings, it is difficult for designers to achieve better sustainability in the design process when using the real-time green building rating score as a reference. This paper proposes an intelligent green building rating (iGBR) framework supported by a semantic and social approach to realize real-time rating in building design. The framework features four components: (1) An ontology that is used to encapsulate the knowledge of green building rating, (2) score calculation rules that are encoded in Semantic Web Rule Language (SWRL), (3) Autodesk Forge, which is employed as a building information modeling (BIM)–based design platform to synchronize design models from different professions in the cloud, and (4) a group chat tool to connect all project participants in a social communication environment to effectively exchange data/information required for score calculation. A prototype iGBR system is developed based on the Evaluation Standard for Green Building of China (ESGBC) to verify the framework, so that a total of 95 articles can be assessed automatically in the real-time approach.

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Cantone, Domenico, Marianna Nicolosi-Asmundo, and Daniele Francesco Santamaria. "A Set-theoretic Approach to Reasoning Services for the Description Logic 𝒟 ℒ D 4,×." Fundamenta Informaticae 176, no. 3-4 (December 18, 2020): 349–84. http://dx.doi.org/10.3233/fi-2020-1977.

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In this paper we consider the most common TBox and ABox reasoning services for the description logic 𝒟ℒ〈4LQSR,x〉(D) ( 𝒟 ℒ D 4,× , for short) and prove their decidability via a reduction to the satisfiability problem for the set-theoretic fragment 4LQSR. 𝒟 ℒ D 4,× is a very expressive description logic. It combines the high scalability and efficiency of rule languages such as the SemanticWeb Rule Language (SWRL) with the expressivity of description logics. In fact, among other features, it supports Boolean operations on concepts and roles, role constructs such as the product of concepts and role chains on the left-hand side of inclusion axioms, role properties such as transitivity, symmetry, reflexivity, and irreflexivity, and data types. We further provide a KE-tableau-based procedure that allows one to reason on the main TBox and ABox reasoning tasks for the description logic 𝒟 ℒ D 4,× . Our algorithm is based on a variant of the KE-tableau system for sets of universally quantified clauses, where the KE-elimination rule is generalized in such a way as to incorporate the γ-rule. The novel system, called KEγ-tableau, turns out to be an improvement of the system introduced in [1] and of standard first-order KE-tableaux [2]. Suitable benchmark test sets executed on C++ implementations of the three mentioned systems show that in several cases the performances of the KEγ-tableau-based reasoner are up to about 400% better than the ones of the other two systems.

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Khosrojerdi, Farhad, Stéphane Gagnon, and Raul Valverde. "Proposing an Ontology Model for Planning Photovoltaic Systems." Machine Learning and Knowledge Extraction 3, no. 3 (July 31, 2021): 582–600. http://dx.doi.org/10.3390/make3030030.

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The performance of a photovoltaic (PV) system is negatively affected when operating under shading conditions. Maximum power point tracking (MPPT) systems are used to overcome this hurdle. Designing an efficient MPPT-based controller requires knowledge about power conversion in PV systems. However, it is difficult for nontechnical solar energy consumers to define different parameters of the controller and deal with distinct sources of data related to the planning. Semantic Web technologies enable us to improve knowledge representation, sharing, and reusing of relevant information generated by various sources. In this work, we propose a knowledge-based model representing key concepts associated with an MPPT-based controller. The model is featured with Semantic Web Rule Language (SWRL), allowing the system planner to extract information about power reductions caused by snow and several airborne particles. The proposed ontology, named MPPT-On, is validated through a case study designed by the System Advisor Model (SAM). It acts as a decision support system and facilitate the process of planning PV projects for non-technical practitioners. Moreover, the presented rule-based system can be reused and shared among the solar energy community to adjust the power estimations reported by PV planning tools especially for snowy months and polluted environments.

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Ajami, Hicham, and Hamid Mcheick. "Ontology-Based Model to Support Ubiquitous Healthcare Systems for COPD Patients." Electronics 7, no. 12 (December 2, 2018): 371. http://dx.doi.org/10.3390/electronics7120371.

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Over the past 30 years, information technology has gradually transformed the way health care is provisioned for patients. Chronic Obstructive Pulmonary Disease (COPD) is an incurable malady that threatens the lives of millions around the world. The huge amount of medical information in terms of complex interdependence between progression of health problems and various other factors makes the representation of data more challenging. This study investigated how formal semantic standards could be used for building an ontology knowledge repository to provide ubiquitous healthcare and medical recommendations for COPD patient to reduce preventable harm. The novel contribution of the suggested framework resides in the patient-centered monitoring approach, as we work to create dynamic adaptive protection services according to the current context of patient. This work executes a sequential modular approach consisting of patient, disease, location, devices, activities, environment and services to deliver personalized real-time medical care for COPD patients. The main benefits of this project are: (1) adhering to dynamic safe boundaries for the vital signs, which may vary depending on multiple factors; (2) assessing environmental risk factors; and (3) evaluating the patient’s daily activities through scheduled events to avoid potentially dangerous situations. This solution implements an interrelated set of ontologies with a logical base of Semantic Web Rule Language (SWRL) rules derived from the medical guidelines and expert pneumologists to handle all contextual situations.

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Huang, Yi, May Yuan, Yehua Sheng, Xiangqiang Min, and Yuwei Cao. "Using Geographic Ontologies and Geo-Characterization to Represent Geographic Scenarios." ISPRS International Journal of Geo-Information 8, no. 12 (December 10, 2019): 566. http://dx.doi.org/10.3390/ijgi8120566.

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Traditional Geographic Information Systems (GIS) represent the environment under reductionist thinking, which disaggregates a geographic environment into independent geographic themes. The reductionist approach makes the spatiotemporal characteristics of geo-features explicit, but neglects the holistic nature of the environment, such as the hierarchical structure and interactions among environmental elements. To fill this gap, we integrate the concept geographic scenario with the fundamental principles of General System Theory to realize the environmental complexity in GIS. With the integration, a geographic scenario constitutes a hierarchy of spatiotemporal frameworks for organizing environmental elements and subserving the exploration of their relationships. Furthermore, we propose geo-characterization with ontological commitments to both static and dynamic properties of a geographic scenario and prescribe spatial, temporal, semantic, interactive, and causal relationships among environmental elements. We have tested the utility of the proposed representation in OWL and the associated reasoning process in Semantic Web Rule Language (SWRL) rules in a case study in Nanjing, China. The case study represents Nanjing and the Nanjing presidential palace to demonstrate the connections among environmental elements in different scenarios and the support for information queries, evolution process simulation, and semantic inferences. The proposed representation encodes geographic knowledge of the environment, makes the interactions among environmental elements explicit, supports geographic process simulation, opens opportunities for deep knowledge mining, and grounds a foundation for GeoAI to discover geographic complexity and dynamics beyond the support of conventional theme-centric inquiries in GIS.

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Tumnark, Piyaporn, Paulo Cardoso, Jorge Cabral, and Filipe Conceição. "An Ontology to Integrate Multiple Knowledge Domains of Training-Dietary-Competition in Weightlifting: A Nutritional Approach." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 12, no. 2 (March 5, 2019): 140–52. http://dx.doi.org/10.37936/ecti-cit.2018122.135896.

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This study is a part of weightlifting “Training-Dietary-Competition” (TDC) cycle ontology. The main objective of TDC-cycle is to build a knowledge framework for Olympic weightlifting, bringing together related fields such as training methodology, weightlifting biomechanics, and nutrition while modelling the synergy among them. In so doing, terminology, semantics, and used concepts are unified among athletes, coaches, nutritionists, and researchers to partially obviate the problem of unclear results and paucity of information. The uniqueness of this ontology is its ability to solve the knowledge sharing problem in which the knowledge owned by these experts in each field are not captures, classified or integrated into an information system for decision-making. The whole weightlifting TDC-cycle is semantically modelled by conceiving, designing, and integrating domain and task ontologies with the latter devising reasoning capability toward an automated and tailored weightlifting TDC-cycle. However, this study will focus mainly on the nutrition domain. The intended application of this part of ontology is to provide a useful decision-making platform for a sport nutritionist who gathers and integrate relevant scientific information, equation, and tools necessary when providing nutritional services. The system is constructed by using Web Ontology Language (OWL), Semantic Web Rule Language (SWRL), and Semantic Query-Enhanced Web Rule Language (SQWRL). The use of weightlifting TDC-cycle ontology can be helpful for nutritionists to create a well-planned nutrition program for athletes (especially, in the process of nutrition monitoring to identify energy imbalance in athletes) by reducing time consumption and calculation errors.

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Novacek, Jan, Alexander Viehl, Oliver Bringmann, and Wolfgang Rosenstiel. "Reasoning-Supported Robustness Validation of Automotive E/E Components." International Journal of Semantic Computing 11, no. 04 (December 2017): 473–96. http://dx.doi.org/10.1142/s1793351x17400190.

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This article presents an ontology-supported approach to tackle the complexity of the Robustness Validation (RV) process of automotive electrical/electronic (E/E) components. The approach uses formalized knowledge from the RV process and stress, operating, and load profiles, so-called Mission Profiles (MPs). In contrast to the error-prone industrially established manual procedure, we show how component characteristics are formalized in OWL in order to form the foundation of an efficient automated analysis selection and decision support during the RV process. Additionally, a rule-based transformation of component characteristics upon propagation via SWRL is described. The proposed approach is based on the idea of mapping MPs to an OWL representation in order to allow to execute semantic queries against MP data to improve their integration into the RV process. The resulting ontology-supported application framework has been applied to an industrial use-case from automotive power electronics. A generalization of the approach is described and demonstrated by applying it to stress test selection within the AEC Q100 standard. We present experimental results showing that the RV process can be significantly improved in terms of reduced design time and increased exhaustiveness by automating the analyses selection step and the provisioning of all the relevant data to be used.

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Xu, Zhao, Xuerong Wang, Wentao Zhou, and Jingfeng Yuan. "Study on the Evaluation Method of Green Construction Based on Ontology and BIM." Advances in Civil Engineering 2019 (October 1, 2019): 1–20. http://dx.doi.org/10.1155/2019/5650463.

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Many problems and issues affect the green construction process. Inaccurate assessment is one of the important factors for green construction. The objective of this study is to propose a BIM and ontology-based approach that enables the evaluation information of green construction to be inferred from a knowledge base in order to achieve scheme optimization. To automate the inference, this study established the BIM ontology that consists of BIM shared ontology and BIM construction ontology. First, IFC extension is adopted in green construction assessment system to generate attribute sets. IFC-based parameters stored in BIM models are mapped to OWL and will be used as the data source of the evaluation indicators. Then, BIM-shared ontology and BIM construction ontology are defined. Protégé is employed to simulate the evaluation indicator system. Last, utilizing BIM knowledge base, through the construction of SWRL rule language and the Drools inference, the scores of evaluation indicators could be obtained. The experimental results demonstrated that BIM knowledge base for the evaluation of green construction could realize the sharing, maintenance, and acquisition of knowledge among different participants of the project and improve the management of green construction. The proposed ontological inference of evaluation item enables an automated search of the aspects needing improvements in green construction and assist project managers in using BIM data more easily and effectively.

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Lee, Wan-Gon, Sung-Hyuk Bang, and Young-Tack Park. "Large Scale Incremental Reasoning using SWRL Rules in a Distributed Framework." Journal of KIISE 44, no. 4 (April 15, 2017): 383–91. http://dx.doi.org/10.5626/jok.2017.44.4.383.

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Mei, Jing, and Harold Boley. "Interpreting SWRL Rules in RDF Graphs." Electronic Notes in Theoretical Computer Science 151, no. 2 (May 2006): 53–69. http://dx.doi.org/10.1016/j.entcs.2005.07.036.

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Zhou, Jie, Qing Guo Shen, Yu Dong Xu, and Zhen Wei Yu. "A Method of Policy Conflict Detection Based on SWRL Rules." Advanced Materials Research 546-547 (July 2012): 955–60. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.955.

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Policy-based management technology which is used for managing enterprise-wide networks and distributed systems is getting more and more attention. Policy conflict detection and solution is a difficulty in this field. In this paper, we present a novel method of policy conflict detection based on SWRL rules. First, we construct a policy ontology model, and represent the policies with Web Ontology Language (OWL). Then the SWRL rules used to detect conflict is presented. Finally, an example is shown to illustrate how to use our method to detection the conflicts in the managed system, the feasibility and the validity of our proposed method are proved out.

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Wei, Jun Ying, and Pei Si Zhong. "The Semantic Retrieval Model of Manufacturing Resource Based on Rules and Similarity." Applied Mechanics and Materials 130-134 (October 2011): 483–86. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.483.

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Based on the common similarity algorithm between ontology concepts, this paper makes use of semantic weighted distance and introduces such factors as node density, concept attribute and concept information content, and presents an improved semantic similarity algorithm to make the measuring semantic similarity more accurate. Combined the similarity algorithm with SWRL, this paper establishes a semantic retrieval model of manufacturing resource based on rules and similarity , which applies the rules of SWRL to carry on conditional matching in the ontology concepts of manufacturing resource in order to retrieve more eligible results.

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Lezcano, Leonardo, Miguel-Angel Sicilia, and Carlos Rodríguez-Solano. "Integrating reasoning and clinical archetypes using OWL ontologies and SWRL rules." Journal of Biomedical Informatics 44, no. 2 (April 2011): 343–53. http://dx.doi.org/10.1016/j.jbi.2010.11.005.

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Journal articles on the topic 'SWRL rule'

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