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Journal articles on the topic 'Multimedia web ontology language'

Author: Grafiati

Published: 6 September 2023

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1

Chebotko, Artem, Yu Deng, Shiyong Lu, Farshad Fotouhi, and Anthony Aristar. "An Ontology-Based Multimedia Annotator for the Semantic Web of Language Engineering." International Journal on Semantic Web and Information Systems 1, no. 1 (January 2005): 50–67. http://dx.doi.org/10.4018/jswis.2005010104.

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Rinaldi, Antonio M. "Enabling semantic technologies using multimedia ontology." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (March 2017): 1630008. http://dx.doi.org/10.1142/s2425038416300081.

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The new vision of the Web as a global intelligent repository needs advanced knowledge structure to manage complex data and services. From this perspective, the use of formal models to represent information on the web is a suitable way to allow the cooperation of users and services. This paper describes a general ontological approach to represent knowledge using multimedia data and linguistic properties to bridge the gap between the target semantic classes and the available low-level multimedia descriptors. We choose to implement our approach in a system to edit, manage and share multimedia ontology in the WEB. The system provides tools to add multimedia objects by means of user interaction. The multimedia features are automatically extracted using algorithms based on standard MPEG-7 descriptors.

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Alhawiti, Mohamed, and Yasser Abdelhamid. "Collection of Web Multimedia Teaching Resources Using Ontology." International Journal of Information and Education Technology 6, no. 4 (2016): 269–74. http://dx.doi.org/10.7763/ijiet.2016.v6.698.

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Mannan, J. Mannar, M. Sundarambal, and S. Raghul. "SELECTION OF ONTOLOGY FOR WEB SERVICE DESCRIPTION LANGUAGE TO ONTOLOGY WEB LANGUAGE CONVERSION." Journal of Computer Science 10, no. 1 (January 1, 2014): 45–53. http://dx.doi.org/10.3844/jcssp.2014.45.53.

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Saha, Goutam Kumar. "Web ontology language (OWL) and semantic web." Ubiquity 2007, September (September 2007): 1. http://dx.doi.org/10.1145/1295289.1295290.

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Jakkilinki, R., N. Sharda, and I. Ahmad. "MUDPY ontology: a tool for multimedia project planning, design and development." Journal of Enterprise Information Management 19, no. 2 (March 1, 2006): 165–74. http://dx.doi.org/10.1108/17410390610645067.

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PurposeThis paper seeks to describe the process used to develop the multimedia design and planning pyramid (MUDPY) ontology, and the role played by the MUDPY ontology as a planning, design and development tool in multimedia projects.Design/methodology/approachThe MUDPY model was implemented for the semantic web by developing an ontology for it. This ontology facilitates defining the concepts existing in that domain, their attributes and the relationships between them.FindingsThe MUDPY ontology can guide developers through the various phases of a multimedia project in a systematic fashion by allowing them to create a project proposal, specify the functional requirements, decide on the navigational structure and create a storyboard, and thus create high‐quality projects.Research limitations/implicationsThe current system is a prototype. This MUDPY ontology can be extended by adding more classes in order to increase its functionality; for example, one can develop special plug‐in widgets to generate project reports required during multimedia project planning and design.Practical implicationsBy using the MUDPY ontology, developers can follow a systematic process for project development, and better manage the complexity of multimedia projects.Originality/valueThe paper introduces the MUDPY model and its ontology, and shows a multimedia author the pathway to manage a multitude of concepts and assets required in a multimedia project.

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Milea, V., F. Frasincar, and U. Kaymak. "tOWL: A Temporal Web Ontology Language." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 42, no. 1 (February 2012): 268–81. http://dx.doi.org/10.1109/tsmcb.2011.2162582.

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MaduraiMeenachi, N., and M. Sai Baba. "Web Ontology Language Editors for Semantic Web- A Survey." International Journal of Computer Applications 53, no. 12 (September 25, 2012): 12–16. http://dx.doi.org/10.5120/8472-2398.

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Uma, R., and K. Muneeswaran. "OMIR: Ontology-Based Multimedia Information Retrieval System for Web Usage Mining." Cybernetics and Systems 48, no. 4 (March 13, 2017): 393–414. http://dx.doi.org/10.1080/01969722.2017.1285163.

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Thirupathi, Lingala, and Venkata Nageswara Rao Padmanabhuni. "Protection Policy Implementation using Web Ontology Language." International Journal of Engineering Trends and Technology 70, no. 8 (August 31, 2022): 453–62. http://dx.doi.org/10.14445/22315381/ijett-v70i8p246.

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Khamis, Khamis Abdul Latif, Luo Zhong, and Hua Zhu Song. "Implementation and Formalization of the Digital Contents’ Data Using OWL and DL into the Reasoning Modules." Applied Mechanics and Materials 644-650 (September 2014): 1759–63. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1759.

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There are different methods and techniques for representing ontology domain, and each domain is bound with one suitable language that can support the ideology and methods based on the way we choose to represent them, which also depends upon the nature of the data we intends to use. So as we move on, the big question is that “how can we represent data based on events’ entities or multimedia data using object ontology, for what extends does the two common ontology languages OWL and RDF/RDF's support multimedia data? ” and how do we infer the media ontology. Those questions are reasonable and acceptable. In this paper, we introduce the basic principles to construct and express media data using ontology techniques, we also examine the capability of OWL in standardizing the rules and restriction based on the media ontology data and finally, we introduces a reasoning module as a solution for the OWL model to support multimedia data during the query and inferencing of media ontology

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Khvorenkov, D. A. "Process Ontology Web-editor." Intellekt. Sist. Proizv. 18, no. 3 (November 17, 2020): 72. http://dx.doi.org/10.22213/2410-9304-2020-3-72-76.

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Онтологии и базы знаний онтологического типа являются непременным компонентом современных систем обработки и анализа информации. В статье рассматривается актуальность использования баз знаний, некоторые перспективные направления применения баз знаний в информационных системах. Рассматриваются некоторые существующие редакторы баз знаний, характерные для них проблемы с пользовательским интерфейсом и причины возникновения этих проблем. Предлагается решение некоторых из этих проблем, основанное на расширении возможностей редактора по построению моделей со сложными связями и на ограничении функциональности, мало используемой рядовыми пользователями. Предлагается программная система для создания OWL (Ontology Web Language) – баз знаний онтологического типа, ориентированных на накопление знаний о процессах – работах и ресурсах. Система предназначена для создания машиночитаемых терминов и определений предметной области, которые необходимы для автоматизации процессов извлечения знаний из данных. С другой стороны, такая система будет полезна специалистам как иерархически организованная справочная система, где корректность задаваемых определений контролируется машиной. Система характеризуется простым и наглядным интерфейсом, призванным задействовать навыки, получаемые экспертами предметных областей в ходе профессиональной деятельности.

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Annamalai, Muthukkaruppan, and Zulhizam Sanip. "NATURAL LANGUAGE SUPPORT FOR COMPETENCY EVALUATION OF WEB-ONTOLOGIES." Journal of IT in Asia 3, no. 1 (April 20, 2016): 59–84. http://dx.doi.org/10.33736/jita.34.2010.

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Ontology is a key element of the Semantic web that facilitates a common understanding of the structure of knowledge in a domain, and they can back applications such as semantic annotation, consistency checking, search and retrieval. The increasing use of the Semantic web has escalated the demand for competent ontologies. One way to check on the competency of an ontology is by use of competency questions to ascertain whether the ontological commitments are adequate to support its purpose of design. Thus, it is useful to have a competency evaluation supporting tool to assist an ontology engineer to perform this analytic task during the development of an ontology. At present, such support is noticeably weak. It led us to construct a semi-automated competency evaluation supporting tool for the popular Protégé ontology editor. The tool, supported by a natural language program interface to the editor, is currently equipped with simple functions, but has demonstrated its potential as a dependable tool for performing the competency evaluation of web ontologies more effectively, in less time and with reduced effort.

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Jean, Stéphane, Yamine Aït-Ameur, and Guy Pierra. "OntoQL: An Alternative to Semantic Web Query Languages." International Journal of Semantic Computing 09, no. 01 (March 2015): 105–37. http://dx.doi.org/10.1142/s1793351x1550004x.

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Ontologies are used in several application domains for representing knowledge. The defined approaches differ according to the type of addressed ontology (conceptual or linguistic) and to the used ontology model (e.g. OWL or PLIB). Several languages have been proposed to manipulate ontologies and their instances, especially in the Semantic Web domain. However these languages are often specific to a given ontology model, they focus on conceptual ontologies and they are not compatible with database exploitation languages. We address these three problems in this paper by proposing the OntoQL language. This language has three main original characteristics: (1) OntoQL is based on a core ontology model composed of the shared constructors of ontology models. This core ontology model can be extended by the language itself, (2) OntoQL queries can be expressed with different natural languages features using the linguistic layer of an ontology, and (3) OntoQL is fully compatible with SQL enabling a smooth integration between SQL queries of classical database applications and ontological queries. As a theoretical validation of this language, we present the algebra of operators that sets up its formal semantics. On the operational side, we describe the implementation of OntoQL on the OntoDB database and we illustrate the interest of this language by reporting several applications where this language has been extensively used and proved powerful.

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Shrivastav, Shikhar, Sandeep Kumar, and Kuldeep Kumar. "Towards an ontology based framework for searching multimedia contents on the web." Multimedia Tools and Applications 76, no. 18 (January 18, 2017): 18657–86. http://dx.doi.org/10.1007/s11042-017-4350-5.

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Selvam, Sheba, Ramadoss Balakrishnan, and Balasundaram Sadhu Ramakrishnan. "Ontology With Hybrid Clustering Approach for Improving the Retrieval Relevancy in Social Event Detection." International Journal on Semantic Web and Information Systems 14, no. 4 (October 2018): 33–56. http://dx.doi.org/10.4018/ijswis.2018100102.

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Progression in digital technology and the fame of social media sites such as Facebook, YouTube, Flickr etc., necessitate sharing memories. This results in a colossal amount of multimedia content such as text, audio, photographs and video on the web. Retrieving photographs exclusively from web in the large collection is a challenging task. One way to retrieve photographs is by identifying them as events. The automatic organization of a multimedia collection into groups of items, where each group corresponds to a distinct event is described as Social Event Detection (SED). Contextual information, present for each photograph in social media adds semantics to the photographs. For semantic based retrieval, ontology based approaches yield good retrieval results, by reducing the number of false positives. So, the proposed approach moves with domain ontology construction followed by a hybrid clustering approach. Compared to the existing single-pass incremental clustering algorithm, the proposed approach ensures a good f-measure of 0.8608.

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Yang, Yuan. "Discussion on the Software Engineering Data Modeling in OWL." Applied Mechanics and Materials 416-417 (September 2013): 1512–15. http://dx.doi.org/10.4028/www.scientific.net/amm.416-417.1512.

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Web ontology language, which is discussed usually by people, is abbreviated as OWL. Actually, it specifically refers to the computer Web ontology language, namely a type of computer machine language. OWL is one of the very important components composing semantic Web technology. It is an ontology language that is recently offered by W3C especially for Web language, and its ontology working group makes a description to OWL through a series of documents. OWL is applicable to the description and modeling on the semantic aspects of complex data, and it can establish a flexible semantic model when a lot of complicated structures and rich semantic data are often derived in the development process of software system. All software engineering data systems are managed in modes. OWL, as an ontology language, has been widely valued in the IT industry. However, the development of OWL has been seriously restricted because it is difficult to understand.

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Kurgaev, A. F. "New definition of the web ontology language OWL2." Reports of the National Academy of Sciences of Ukraine, no. 3 (March 18, 2018): 22–35. http://dx.doi.org/10.15407/dopovidi2018.03.022.

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Yang, Dong, Rui Miao, Hongwei Wu, and Yiting Zhou. "Product configuration knowledge modeling using ontology web language." Expert Systems with Applications 36, no. 3 (April 2009): 4399–411. http://dx.doi.org/10.1016/j.eswa.2008.05.026.

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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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Ruindungan, Dirko G. S., and Christopel H. Simanjuntak. "Implementasi Semantic Web Rule Language dalam Pemberian Rekomendasi Nutrisi Berbasis Ontologi." CCIT Journal 12, no. 2 (August 19, 2019): 207–17. http://dx.doi.org/10.33050/ccit.v12i2.691.

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The recommendations or guidelines about nutrition are available from a various distinct source on the internet. On the other hand, nutritional information needed by each person is different according to physical condition or personal preferences of each individual. This becomes a bit complicated because every information provider on the internet has a different understanding in giving foodstuff references to certain nutrients. In this study, an ontology in nutrition domain knowledge was used. The ontology represents explicit specification of pregnancy nutrition domain knowledge. The ontology constructed consists of three basic concepts that is Person, Maternal Condition and PrenNutriFood. To support the provision of nutritional recommendation, three definitions were added to ontology that is determining energy estimates per day, determining the percentage of daily value (DV) of food ingredients and determining the claims of nutrient content in foodstuff. In this study, we implemented the Semantic Web Rule Language to formalize those definitions. Inference from each rule is generated through Pellet as an inference engine. Ontology has been successfully managed with rules and finally produce new knowledge containing the recommendations. The results of inference indicate the expansion of knowledge in ontology

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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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Shanmurthy, Prakash, Poongodi Thangamuthu, Balamurugan Balusamy, and Seifedine Kadry. "Augmentation of contextual knowledge based on domain dominant words for IoT applications interoperability." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 1 (July 1, 2022): 504. http://dx.doi.org/10.11591/ijeecs.v27.i1.pp504-512.

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Semantic web technology is adapted to the internet of things (IoT) for web - based applications to globally connect the services. Web ontology language (OWL) domain ontology is a powerful machine - readable language for domain knowledge representation. The developer stored the IoT application relevant ontology in a repository or catalogue. Hence, IoT application - related ontology files are available for reus e, but many of the IoT application - relevant ontology files are publicly not available or inaccessible. The proposed idea is to extract the contextual knowledge of IoT applications that contain inaccessible ontology files. The context - wise specific domain I oT applications are not obtainable, hence respective ontology - based research papers are identified and their frequent terms are computed. The selected contextual dominant frequent terms from the transport domain are passed into the skip - gram flavour of wor d2vector modelled n atural language processing ( NLP ) corpus which produces most similar terms. The domain experts select the appropriate terms to annotate in OWL ontology for contextual knowledge augmentation. Finally, 1422 contextual terms were generated b ased on dominant terms of selected IoT applications.

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RUCKHAUS, EDNA, EDUARDO RUIZ, and MARÍA-ESTHER VIDAL. "Query evaluation and optimization in the semantic web." Theory and Practice of Logic Programming 8, no. 3 (May 2008): 393–409. http://dx.doi.org/10.1017/s1471068407003225.

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AbstractWe address the problem of answering Web ontology queries efficiently. An ontology is formalized as adeductive ontology base(DOB), a deductive database that comprises the ontology's inference axioms and facts. A cost-based query optimization technique for DOB is presented. A hybrid cost model is proposed to estimate the cost and cardinality of basic and inferred facts. Cardinality and cost of inferred facts are estimated using an adaptive sampling technique, while techniques of traditional relational cost models are used for estimating the cost of basic facts and conjunctive ontology queries. Finally, we implement a dynamic-programming optimization algorithm to identify query evaluation plans that minimize the number of intermediate inferred facts. We modeled a subset of the Web ontology language Lite as a DOB and performed an experimental study to analyze the predictive capacity of our cost model and the benefits of the query optimization technique. Our study has been conducted over synthetic and real-world Web ontology language ontologies and shows that the techniques are accurate and improve query performance.

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LI, YUNJIA, MIKE WALD, and GARY WILLS. "APPLYING LINKED DATA IN MULTIMEDIA ANNOTATIONS." International Journal of Semantic Computing 06, no. 03 (September 2012): 289–313. http://dx.doi.org/10.1142/s1793351x12400090.

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Current multimedia applications in Web 2.0 have generated large repositories for multimedia resources and annotations, so there is an urgent requirement to interlink annotations of these resources across different repositories to achieve better indexing and searching. To solve this problem, many researchers have been trying to apply semantic Web technologies to media fragments and annotations. Linked data has brought forward a promising way to expose, index and search media fragments and annotations which used to be isolated in different applications. This paper discusses in depth three key research problems when applying linked data principles in multimedia annotations: choosing URIs for media fragments, dereferencing media fragments and ontology alignment. An architecture is designed based on the possible solutions of the research problems. The key idea of the architecture is that it should act like an extra layer built on top of existing applications when publishing linked data. A demo is built as an implementation of the architecture to show that media fragments can be published and linked to various datasets in the linked data cloud. In the future, some algorithms should be designed to make full use of the interlinked media fragments and annotations for indexing and searching.

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Liu, Chih Hao, and Jason Jen Yen Chen. "Dynamic, Customized Workflow Using BDI Agent and Semantic Web Service." Applied Mechanics and Materials 135-136 (October 2011): 477–83. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.477.

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As the Web gradually evolves into the semantic web, the World Wide Web consortium (W3C) recommends that web ontology language (OWL) be used to encode semantic information content over the Web. Semantic web is an essential infrastructure to enhance Web to obtain better integration of information and intelligent use of web resources. Moreover, a web service is annotated by web ontology language for service (OWL-S) to form a semantic web service that, however, is a static description. The OWL-S based semantic web services thus are reactively invoked by users. How to dynamically coordinate, composite, or discover the services is an important issue.

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Sinaga, Arnaldo Marulitua, Rini Juliana Sipahutar, and Dian Ira Putri Hutasoit. "Penerapan Ontology Web Language pada Domain Ulos Batak Toba." Jurnal Teknologi Informasi dan Ilmu Komputer 5, no. 4 (October 1, 2018): 493. http://dx.doi.org/10.25126/jtiik.201854903.

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<p class="Abstrak">Indonesia adalah salah satu negara di dunia yang kaya akan keanekaragaman budaya. Keanekaragaman budaya ini diakibatkan banyaknya suku di Indonesia. Setiap suku memiliki kekhasan masing-masing termasuk kekhasan dalam kain tradisional. Salah satunya adalah ulos, kain tradisional suku Batak Toba. Ulos merupakan simbol sakral dalam adat istiadat suku Batak Toba. Ulos terdiri dari berbagai macam jenis, motif, warna, makna hingga fungsi. Namun sangat disayangkan bahwa informasi mengenai ulos belum terdokumentasi dengan baik. Informasi didapatkan secara turun temurun dari satu generasi ke generasi lain yang memungkinkan informasi tersebut hilang. Selain itu, beberapa sumber yang ada juga menyajikan informasi yang berbeda. Oleh karena itu, ontologi bisa menjadi salah satu solusi untuk membantu mengelola informasi yang sudah ada supaya lebih terorganisir. Ontologi membantu memformalkan semua <em>vocabulary</em> pada domain ulos. <em>Vocabulary</em> itu kemudian dalam ontologi disebut sebagai konsep. Konsep tersebut akan saling berkaitan satu sama lain sehingga dapat dilihat sebagai suatu bentuk kesatuan (<em>linked</em> data) yang membentuk pola yang terstruktur. Kondisi ini yang kemudian mendukung pencarian dengan hasil yang bernilai semantik karena pola data yang disediakan saling berkaitan satu sama lain. Ontologi tersebut dapat direprentasikan menggunakan Web Ontology Language (OWL) yang merupakan <em>vocabulary extension</em> dari Resource Descriptive Framework (RDF). Kemudian untuk proses <em>retrieving</em> data akan menggunakan SPARQL.</p><p class="Abstrak"> </p><p class="Judul2"><strong><em>Abstract</em></strong><em> </em></p><p class="Judul2" align="left"><em>Indonesia is a very rich country in cultural diversity. Most of the ethnic groups have their own uniqueness including the peculiarities in traditional textiles. One of them is Ulos, traditional cloth of Batak Toba. Ulos is a sacred symbol in the Batak traditions. Ulos consists of various types, motifs, colors, meanings and functions. However, it is unfortunate that information of Ulos has not been well documented. This cultural heritage is well-transferred from generation to generation. The existing sources sometime provide different information. Therefore, ontology can be one solution to help manage existing information to be more well-organized. Ontology helps formalize all vocabularies on the Ulos domain. The vocabulary in the ontology is called as a concept. The concepts are related to each other so that it can be seen as a linked data that form a structured pattern. This feature supports information searching with semantic value. The ontology of Ulos Batak Toba has been developed by using Web Ontology Language (OWL), which is a vocabulary extension of the Resource Descriptive Framework (RDF). Then for the data retrieving process we use SPARQL</em></p><p class="Abstrak"> </p>

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Lozano-Rubí, Raimundo, Xavier Pastor, and Esther Lozano. "OWLing Clinical Data Repositories With the Ontology Web Language." JMIR Medical Informatics 2, no. 2 (August 1, 2014): e14. http://dx.doi.org/10.2196/medinform.3023.

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Rajput, Quratulain. "Ontology based Semantic Annotation of Urdu Language Web Documents." Procedia Computer Science 35 (2014): 662–70. http://dx.doi.org/10.1016/j.procs.2014.08.148.

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Mcguinness, D. L., R. Fikes, J. Hendler, and L. A. Stein. "DAML+OIL: an ontology language for the Semantic Web." IEEE Intelligent Systems 17, no. 5 (September 2002): 72–80. http://dx.doi.org/10.1109/mis.2002.1039835.

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de Vergara, J. E. L., V. A. Villagra, and J. Berrocal. "Applying the Web ontology language to management information definitions." IEEE Communications Magazine 42, no. 7 (July 2004): 68–74. http://dx.doi.org/10.1109/mcom.2004.1316535.

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Song, Lan, Li Xia Lei, Hong Wang, and Jun Hong Hua. "Research on Ontology-Based Semantic Reasoning." Advanced Materials Research 171-172 (December 2010): 136–39. http://dx.doi.org/10.4028/www.scientific.net/amr.171-172.136.

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As a new emerging web, semantic web, has recently drawn considerable attention from both academic and industry field. Nowadays, RDF, RDF Schema, OWL etc. have become commonly used languages in the Semantic Web. This paper describes the ontology language and description logic, shows the relationship of them, and finally presents a reasoning path for transitive closure in an ontology document.

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Yang, Shi Han, Jin Zhao Wu, and An Ping He. "Automatically Transforming Legacy XML Documents Into OWL Ontology." Applied Mechanics and Materials 241-244 (December 2012): 2638–44. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.2638.

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It is a challenge to transform legacy XML-based data into ontology for applications of the semantic web, such as semantic-based integrations, intelligent web searching, and internet based knowledge reasoning. We propose a new technique to transform XML data into ontology data automatically by modeling XML documents semantically. Firstly, we provide the XML a semantically interpretation by developing a graph-based formal language, w-graph. Then, the result of the interpretation can be automatically mapped into OWL web ontology language with semantics preserved. The proof of semantics preserved also been considered, and automatic mapping tool has been developed.

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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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Gasevic, Dragan. "Petri nets on the semantic web guidelines and infrastructure." Computer Science and Information Systems 1, no. 2 (2004): 127–51. http://dx.doi.org/10.2298/csis0402127g.

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This paper gives the Petri net ontology as the most important element in providing Petri net support for the Semantic Web. Available Petri net formal descriptions are: metamodels, UML profiles, ontologies and syntax. Metamodels are useful, but their main purpose is for Petri net tools. Although the current Petri-net community effort Petri Net Markup Language (PNML) is XML-based, it lacks a precise definition of semantics. Existing Petri net ontologies are partial solutions specialized for a specific problem. In order to show current Petri net model sharing features we use P3 tool that uses PNML/XSLT-based approach for model sharing. This paper suggests developing the Petri net ontology to represent semantics appropriately. This Petri net ontology is described using UML, Resource Description Framework (Schema) RDF(S) and the Web Ontology Language-OWL.

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Rahwan, Iyad, Bita Banihashemi, Chris Reed, Douglas Walton, and Sherief Abdallah. "Representing and classifying arguments on the Semantic Web." Knowledge Engineering Review 26, no. 4 (December 2011): 487–511. http://dx.doi.org/10.1017/s0269888911000191.

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AbstractUntil recently, little work has been dedicated to the representation and interchange of informal, semi-structured arguments of the type found in natural language prose and dialogue. To redress this, the research community recently initiated work towards an Argument Interchange Format (AIF). The AIF aims to facilitate the exchange of semi-structured arguments among different argument analysis and argumentation-support tools. In this paper, we present a Description Logic ontology for annotating arguments, based on a new reification of the AIF and founded in Walton's theory of argumentation schemes. We demonstrate how this ontology enables a new kind of automated reasoning over argument structures, which complements classical reasoning about argument acceptability. In particular, Web Ontology Language reasoning enables significantly enhanced querying of arguments through automatic scheme classifications, instance classification, inference of indirect support in chained argument structures, and inference of critical questions. We present the implementation of a pilot Web-based system for authoring and querying argument structures using the proposed ontology.

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Choudhary, Jaytrilok, and Deepak Singh Tomar. "Semi-Automated Ontology building through Natural Language Processing." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 8 (August 23, 2014): 4738–46. http://dx.doi.org/10.24297/ijct.v13i8.7072.

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Ontology is a backbone of semantic web which is used for domain knowledge representation. Ontology provides the platform for effective extraction of information. Usually, ontology is developed manually, but the manual ontology construction requires lots of efforts by domain experts. It is also time consuming and costly. Thus, an approach to build ontology in semi-automated manner has been proposed. The proposed approach extracts concept automatically from open directory Dmoz. The Stanford Parser is explored to parse natural language syntax and extract the parts of speech which are used to form the relationship among the concepts. The experimental result shows a fair degree of accuracy which may be improved in future with more sophisticated approach.

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Cheng, Xian Yi, Ai Qin Yang, and Xue Yun Cheng. "The Study of Ontology Reasoning to Semantic Web." Advanced Materials Research 204-210 (February 2011): 375–80. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.375.

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The next generation Web, Semantic Web, has recently been drawn considerable attention from both academia and industry. The ontology is regarded as the cornerstone of the Semantic Web, is playing an important role with the knowledge expression and knowledge reasoning. Ontology language, Description Logic and the relationships of them were presented. This paper analyzes the principle of semantic reasoning about DL and reasoning machine. Finally, performers testify the practical reasoning task about a concrete Ontology construction.

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Cheng, Xian Yi, Ai Qin Yang, and Xue Yun Cheng. "The Study of Ontology Reasoning to Semantic Web." Advanced Materials Research 181-182 (January 2011): 230–35. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.230.

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The next generation Web, Semantic Web, has recently been drawn considerable attention from both academia and industry. The ontology is regarded as the cornerstone of the Semantic Web, is playing an important role with the knowledge expression and knowledge reasoning. Ontology language, Description Logic and the relationships of them were presented. This paper analyzes the principle of semantic reasoning about DL and reasoning machine. Finally, performers testify the practical reasoning task about a concrete Ontology construction.

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Fahrurrozi, Muhammad, and Azhari SN. "Rancang Bangun Plugin Protégé Menggunakan Ekspresi SPARQL-DL Dengan Masukan Bahasa Alami." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 11, no. 2 (July 31, 2017): 155. http://dx.doi.org/10.22146/ijccs.18360.

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Semantic web is a technology that allows us to build a knowledge base or ontology for the information of the web page can be understood by computers. One software for building ontology-based semantic web is a protégé. Protege allows developers to develop an ontology with an expression of logic description. Protégé provides a plugin such as DL-Query and SPARQL-Query to display information that involve expression of class, property and individual in the ontology. The problem that then arises is DL-plugin Query only able to process the rules that involve expression of class to any object property, despite being equipped with the function of reasoning. while the SPARQL-Query plugin does not have reasoning abilities such as DL-Query plugin although the SPARQL-Query plugin can query memperoses rules involving class, property and individual. This research resulted in a new plugin using SPARQL-DL with input natural language as a protégé not provide a plugin with input natural language to see results from the combined expression-expression contained in the ontology that allows developers to view information ontology language that is easier to understand without having think of SPARQL query structure is complicated.

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LAM, TOBY H. W., JAMES N. K. LIU, and RAYMOND S. T. LEE. "MASTER: AN INTELLIGENT ONTOLOGY-BASED MULTI-AGENT SYSTEM FOR SIGHTSEER." International Journal of Software Engineering and Knowledge Engineering 19, no. 02 (March 2009): 137–57. http://dx.doi.org/10.1142/s021819400900412x.

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Recently, semantic web has received substantial attention from the research community. Semantic web aims to provide a new framework that can enable knowledge sharing and reusing. Semantic web is a collection of web technologies that include a number of markup languages such as RDF, OWL and RDFS. These markup languages are used for modeling a domain ontology. By using ontology to model resources, humans and computers (software agents) can have a consensus on the resource structure. The use of these technologies allows the creation of a more effective web search system. In this paper, we modeled travel domain ontology by using Web Ontology Language (OWL). Instead of inviting an expert to model the ontology, we created the travel ontology by collecting and analyzing the structural information from a number of travel related websites. Besides, we implemented an intelligent ontology-based Multi-Agent System for sighTseER (MASTER), which is constructed by using semantic web technologies. MASTER integrates Global Positioning System (GPS), ontology and agent technologies to support location awareness for providing the precise navigation and classify the tourist information for the users. The system was tested on 30 novice users. 83% of the users felt that the system can help tourists find tourist information in Hong Kong.

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Hanan Hassan Al Mutawa, Hanan Hassan Al Mutawa. "CAL2OWL: Direct Translation from CAL to OWL for." journal of King Abdulaziz University Computing and Information Technology Sciences 11, no. 1 (January 1, 2022): 13–25. http://dx.doi.org/10.4197/comp.11-1.2.

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Due to the difficulty of authoring the Web Ontology Language (OWL) by ontology engineers and domain experts with little or no engineering experience, the first Controlled Arabic Language (CAL) was proposed to ease ontology authoring by Arab experts. However, the CAL tool is based on Rabbit to OWL Ontology Language (ROO), meaning that CAL ontologies must be translated to Rabbit before being ultimately translated to OWL, which is a slow and inflexible process.

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Halpin, Harry, and Valentina Presutti. "The identity of resources on the Web: An ontology for Web architecture." Applied Ontology 6, no. 3 (2011): 263–93. http://dx.doi.org/10.3233/ao-2011-0095.

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Bastola, Rama, and Subarna Shakya. "Developing Domain Ontology for Issuing Certificate of Citizenship of Nepal." June 2020 2, no. 2 (May 16, 2020): 73–90. http://dx.doi.org/10.36548/jitdw.2020.2.001.

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Most of the existing software systems are web-based and interactive. Semantic Web Technologies further allows for organizing and encoding information meaningfully. It enables software agents to interpret the information. However, the representation of information in different format has made challenging to interoperate among the heterogeneous systems. The representation of heterogeneous system with common standards and vocabularies is an effective solution for interoperability. For this, development of domain ontology is required for common understanding of the concept. The research work is mainly focused on development of domain ontology for issuing Citizenship of Nepal as e-government is widely considered as a good example of heterogeneous system. Protégé is used as ontology editor and Web Ontology Language (OWL) is used for representation of the concepts. A hybrid methodology with Unified Modelling Language (UML) is used to get in-depth concept of the domain. The domain ontology is verified using built-in reasoner of Protégé and validated with answering competency questions of the domain using SPARQL query.

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Pan, Wen Lin. "A Formal EXPRESS-to-OWL Mapping Algorithm." Key Engineering Materials 419-420 (October 2009): 689–92. http://dx.doi.org/10.4028/www.scientific.net/kem.419-420.689.

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Network-based collaborative product development has become a trend in manufacturing industry, which depends on two key information technology: the Semantic Web and the standard for the exchange of product model data (STEP). EXPRESS is the STEP product data modeling language, and OWL is the standard ontology representing language used in the Semantic Web. Only when the EXPRESS models are converted to OWL models, product information can be exchanged at the Web. The ontology meta-modeling theory was employed to analysis the ontology definition metamodel of EXPRESS and OWL, in order to build up the mapping relationship between them. A formal EXPRESS-to-OWL mapping algorithm is then proposed, represented by abstract syntax.

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Obrst, Leo, Michael Gruninger, Ken Baclawski, Mike Bennett, Dan Brickley, Gary Berg-Cross, Pascal Hitzler, et al. "Semantic Web and Big Data meets Applied Ontology." Applied Ontology 9, no. 2 (2014): 155–70. http://dx.doi.org/10.3233/ao-140135.

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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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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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Verhodubs, Olegs, and Janis Grundspenkis. "Algorithm of Ontology Transformation to Concept Map for Usage in Semantic Web Expert System." Applied Computer Systems 14, no. 1 (June 1, 2013): 80–87. http://dx.doi.org/10.2478/acss-2013-0010.

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Abstract The main purpose of this paper is to present an algorithm of OWL (Web Ontology Language) ontology transformation to concept map for subsequent generation of rules and also to evaluate the efficiency of this algorithm. These generated rules are necessary to supplement and even to develop SWES (Semantic Web Expert System) knowledge base. This paper is a continuation of the earlier research of OWL ontology transformation to rules.

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Ren, Min, and Feng Yang. "Research on Emergency Plan Ontology Model." Advanced Materials Research 488-489 (March 2012): 1288–92. http://dx.doi.org/10.4028/www.scientific.net/amr.488-489.1288.

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Emergency plan is a project document to guide and deal with public emergency events, and also is the core of emergency management. Nowadays, most emergency plans in China exist in non-semantic form and are difficult to play roles effectively. Ontology is an important method to describe semantic model. Therefore, in this paper, emergency plan ontology model is constructed by ontology technology of semantic Web and Web Ontology Language OWL, which formally describes the conceptions of emergency plan and the relations between them. Finally, the model is used in ontology-based semantic retrieval system, and improves the retrieval recall and precision.

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