Path-Based Semantic Annotation for Web Service Discovery
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Towards Ontology Based BPMN Implementation. Sophea Chhun, Néjib Moalla, Yacine Ouzrout
Towards ontology based BPMN Implementation. Sophea Chhun, Néjib Moalla, Yacine Ouzrout To cite this version: Sophea Chhun, Néjib Moalla, Yacine Ouzrout. Towards ontology based BPMN Implementation.. SKIMA, 6th Conference on Software Knowledge Information Management and Applications., Jan 2012, Chengdu, China. 8 p. hal-01551452 HAL Id: hal-01551452 https://hal.archives-ouvertes.fr/hal-01551452 Submitted on 6 Nov 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. 1 Towards ontology based BPMN implementation CHHUN Sophea, MOALLA Néjib and OUZROUT Yacine University of Lumiere Lyon2, laboratory DISP, France Natural language is understandable by human and not machine. None technical persons can only use natural language to specify their business requirements. However, the current version of Business process management and notation (BPMN) tools do not allow business analysts to implement their business processes without having technical skills. BPMN tool is a tool that allows users to design and implement the business processes by connecting different business tasks and rules together. The tools do not provide automatic implementation of business tasks from users’ specifications in natural language (NL). Therefore, this research aims to propose a framework to automatically implement the business processes that are expressed in NL requirements. -
V a Lida T in G R D F Da
Series ISSN: 2160-4711 LABRA GAYO • ET AL GAYO LABRA Series Editors: Ying Ding, Indiana University Paul Groth, Elsevier Labs Validating RDF Data Jose Emilio Labra Gayo, University of Oviedo Eric Prud’hommeaux, W3C/MIT and Micelio Iovka Boneva, University of Lille Dimitris Kontokostas, University of Leipzig VALIDATING RDF DATA This book describes two technologies for RDF validation: Shape Expressions (ShEx) and Shapes Constraint Language (SHACL), the rationales for their designs, a comparison of the two, and some example applications. RDF and Linked Data have broad applicability across many fields, from aircraft manufacturing to zoology. Requirements for detecting bad data differ across communities, fields, and tasks, but nearly all involve some form of data validation. This book introduces data validation and describes its practical use in day-to-day data exchange. The Semantic Web offers a bold, new take on how to organize, distribute, index, and share data. Using Web addresses (URIs) as identifiers for data elements enables the construction of distributed databases on a global scale. Like the Web, the Semantic Web is heralded as an information revolution, and also like the Web, it is encumbered by data quality issues. The quality of Semantic Web data is compromised by the lack of resources for data curation, for maintenance, and for developing globally applicable data models. At the enterprise scale, these problems have conventional solutions. Master data management provides an enterprise-wide vocabulary, while constraint languages capture and enforce data structures. Filling a need long recognized by Semantic Web users, shapes languages provide models and vocabularies for expressing such structural constraints. -
Semantic Description of Web Services
Semantic Description of Web Services Thabet Slimani CS Department, Taif University, P.O.Box 888, 21974, KSA Abstract syntaxes) and in terms of the paradigms proposed for The tasks of semantic web service (discovery, selection, employing these in practice. composition, and execution) are supposed to enable seamless interoperation between systems, whereby human intervention is This paper is dedicated to provide an overview of these kept at a minimum. In the field of Web service description approaches, expressing their classification in terms of research, the exploitation of descriptions of services through commonalities and differences. It provides an semantics is a better support for the life-cycle of Web services. understanding of the technical foundation on which they The large number of developed ontologies, languages of are built. These techniques are classified from a range of representations, and integrated frameworks supporting the research areas including Top-down, Bottom-up and Restful discovery, composition and invocation of services is a good Approaches. indicator that research in the field of Semantic Web Services (SWS) has been considerably active. We provide in this paper a This paper does also provide some grounding that could detailed classification of the approaches and solutions, indicating help the reader perform a more detailed analysis of the their core characteristics and objectives required and provide different approaches which relies on the required indicators for the interested reader to follow up further insights objectives. We provide a little detailed comparison and details about these solutions and related software. between some approaches because this would require Keywords: SWS, SWS description, top-down approaches, addressing them from the perspective of some tasks bottom-up approaches, RESTful services. -
D1.2.1 WSMO Grounding in SAWSDL
Project Number: 215219 Project Acronym: SOA4ALL Project Title: Service Oriented Architectures for All Instrument: Integrated Project Thematic Information and Communication Priority: Technologies D1.2.1 WSMO grounding in SAWSDL Activity N: 1 Fundamental and Integration Work Package: 1 Service Web Architecture Due Date: M6 Submission Date: 27/08/2008 Start Date of Project: 01/03/2006 Duration of Project: 36 Months Organisation Responsible of Deliverable: UIBK Revision: 1.0 Author(s): Jacek Kopecký, Adi Schütz UIBK Project co-funded by the European Commission within the Seventh Framework Programme (2007-2013) Dissemination Level PU Public X PP Restricted to other programme participants (including the Commission) RE Restricted to a group specified by the consortium (including the Commission) CO Confidential, only for members of the consortium (including the Commission) SOA4All –FP7 – 215219 – D1.2.1 WSMO grounding in SAWSDL Version History Version Date Comments, Changes, Status Authors, contributors, reviewers 0.1 2008/07/16 Initial version Jacek Kopecký 0.2 2008/07/24 Filling in more Jacek Kopecký, Adi Schütz 0.9 2008/07/25 A complete draft, ready for review Jacek Kopecký, Adi Schütz 0.95 2008/07/19 Review comments incorporated Elisabetta Di Nitto, Marin Dimitrov, Jacek Kopecký 1.0 2008/07/23 Version to be submitted Jacek Kopecký © SOA4ALL consortium Page 2 of 28 SOA4All –FP7 – 215219 – D1.2.1 WSMO grounding in SAWSDL Table of Contents EXECUTIVE SUMMARY ____________________________________________________ 5 1. INTRODUCTION ______________________________________________________ -
Harnessing the Power of Folksonomies for Formal Ontology Matching On-The-Fly
Edinburgh Research Explorer Harnessing the power of folksonomies for formal ontology matching on the fly Citation for published version: Togia, T, McNeill, F & Bundy, A 2010, Harnessing the power of folksonomies for formal ontology matching on the fly. in Proceedings of the ISWC workshop on Ontology Matching. <http://ceur-ws.org/Vol- 689/om2010_poster4.pdf> Link: Link to publication record in Edinburgh Research Explorer Document Version: Early version, also known as pre-print Published In: Proceedings of the ISWC workshop on Ontology Matching General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 01. Oct. 2021 Harnessing the power of folksonomies for formal ontology matching on-the-y Theodosia Togia, Fiona McNeill and Alan Bundy School of Informatics, University of Edinburgh, EH8 9LE, Scotland Abstract. This paper is a short introduction to our work on build- ing and using folksonomies to facilitate communication between Seman- tic Web agents with disparate ontological representations. We briey present the Semantic Matcher, a system that measures the semantic proximity between terms in interacting agents' ontologies at run-time, fully automatically and minimally: that is, only for semantic mismatches that impede communication. -
OGC Testbed-14: Semantically Enabled Aviation Data Models Engineering Report
OGC Testbed-14 Semantically Enabled Aviation Data Models Engineering Report Table of Contents 1. Summary . 4 1.1. Requirements & Research Motivation . 4 1.2. Prior-After Comparison. 4 1.3. Recommendations for Future Work . 5 1.4. What does this ER mean for the Working Group and OGC in general . 6 1.5. Document contributor contact points . 6 1.6. Foreword . 6 2. References . 8 3. Terms and definitions . 9 3.1. Semantics . 9 3.2. Service Description. 9 3.3. Service-Oriented Architecture (SOA) . 9 3.4. Registry . 9 3.5. System Wide Information Management (SWIM) . 9 3.6. Taxonomy . 9 3.7. Web Service . 10 4. Abbreviated Terms . 11 5. Overview . 12 6. Review of Data Models . 13 6.1. Information Exchange Models . 13 6.1.1. Flight Information Exchange Model (FIXM). 13 6.1.2. Aeronautical Information Exchange (AIXM) Model. 13 6.1.3. Weather Information Exchange Model (WXXM) . 14 6.1.4. NASA Air Traffic Management (ATM) Model . 14 6.2. Service description models . 19 6.2.1. Service Description Conceptual Model (SDCM) . 19 6.2.2. Web Service Description Ontological Model (WSDOM). 23 6.2.3. SWIM Documentation Controlled Vocabulary (FAA) . 25 7. Semantic Enablement Approaches . 27 8. Metadata level semantic enablement . 33 8.1. Issues with existing metadata standards . 34 8.1.1. Identification of Resources. 34 8.1.2. Resolvable URI. 34 8.1.3. Multilingual Support . 35 8.1.4. External Resource Descriptions . 35 8.1.5. Controlled Vocabulary Management . 36 8.1.6. Keywords Types . 37 8.1.7. Keyword Labeling Inconsistencies . -
Learning to Match Ontologies on the Semantic Web
The VLDB Journal manuscript No. (will be inserted by the editor) Learning to Match Ontologies on the Semantic Web AnHai Doan1, Jayant Madhavan2, Robin Dhamankar1, Pedro Domingos2, Alon Halevy2 1 Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA fanhai,[email protected] 2 Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA fjayant,pedrod,[email protected] Received: date / Revised version: date Abstract On the Semantic Web, data will inevitably come and much of the potential of the Web has so far remained from many different ontologies, and information processing untapped. across ontologies is not possible without knowing the seman- In response, researchers have created the vision of the Se- tic mappings between them. Manually finding such mappings mantic Web [BLHL01], where data has structure and ontolo- is tedious, error-prone, and clearly not possible at the Web gies describe the semantics of the data. When data is marked scale. Hence, the development of tools to assist in the ontol- up using ontologies, softbots can better understand the se- ogy mapping process is crucial to the success of the Seman- mantics and therefore more intelligently locate and integrate tic Web. We describe GLUE, a system that employs machine data for a wide variety of tasks. The following example illus- learning techniques to find such mappings. Given two on- trates the vision of the Semantic Web. tologies, for each concept in one ontology GLUE finds the most similar concept in the other ontology. We give well- founded probabilistic definitions to several practical similar- Example 1 Suppose you want to find out more about some- ity measures, and show that GLUE can work with all of them. -
Kgvec2go – Knowledge Graph Embeddings As a Service
Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 5641–5647 Marseille, 11–16 May 2020 c European Language Resources Association (ELRA), licensed under CC-BY-NC KGvec2go – Knowledge Graph Embeddings as a Service Jan Portisch (1,2), Michael Hladik (2), Heiko Paulheim (1) (1) University of Mannheim - Data and Web Science Group, (2) SAP SE (1) B 6, 26 68159 Mannheim, Germany (2) Dietmar-Hopp Allee 16, 60190, Walldorf, Germany [email protected], [email protected], [email protected] Abstract In this paper, we present KGvec2go, a Web API for accessing and consuming graph embeddings in a light-weight fashion in downstream applications. Currently, we serve pre-trained embeddings for four knowledge graphs. We introduce the service and its usage, and we show further that the trained models have semantic value by evaluating them on multiple semantic benchmarks. The evaluation also reveals that the combination of multiple models can lead to a better outcome than the best individual model. Keywords: RDF2Vec, knowledge graph embeddings, knowledge graphs, background knowledge resources 1. Introduction The data set presented here allows to compare the perfor- A knowledge graph (KG) stores factual information in the mance of different knowledge graph embeddings on differ- form of triples. Today, many such graphs exist for various ent application tasks. It further allows to combine embed- domains, are publicly available, and are being interlinked. dings from different knowledge graphs in downstream ap- As of 2019, the linked open data cloud (Schmachtenberg plications. We evaluated the embeddings on three semantic et al., 2014) counts more than 1,000 data sets with multiple gold standards and also explored the combination of em- billions of unique triples.1 Knowledge graphs are typically beddings. -
An Efficient Wikipedia Semantic Matching Approach to Text Document Classification
Information Sciences 393 (2017) 15–28 Contents lists available at ScienceDirect Information Sciences journal homepage: www.elsevier.com/locate/ins An efficient Wikipedia semantic matching approach to text document classification ∗ ∗ Zongda Wu a, , Hui Zhu b, , Guiling Li c, Zongmin Cui d, Hui Huang e, Jun Li e, Enhong Chen f, Guandong Xu g a Oujiang College, Wenzhou University, Wenzhou, Zhejiang, China b Wenzhou Vocational College of Science and Technology, Wenzhou, Zhejiang, China c School of Computer Science, China University of Geosciences, Wuhan, China d School of Information Science and Technology, Jiujiang University, Jiangxi, China e College of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou, Zhejiang, China f School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China g Faculty of Engineering and IT, University of Technology, Sydney, Australia a r t i c l e i n f o a b s t r a c t Article history: A traditional classification approach based on keyword matching represents each text doc- Received 28 July 2016 ument as a set of keywords, without considering the semantic information, thereby, re- Revised 6 January 2017 ducing the accuracy of classification. To solve this problem, a new classification approach Accepted 3 February 2017 based on Wikipedia matching was proposed, which represents each document as a con- Available online 7 February 2017 cept vector in the Wikipedia semantic space so as to understand the text semantics, and Keywords: has been demonstrated to improve the accuracy of classification. However, the immense Wikipedia matching Wikipedia semantic space greatly reduces the generation efficiency of a concept vector, re- Keyword matching sulting in a negative impact on the availability of the approach in an online environment. -
A Survey of Schema Matching Research Using Database Schemas and Instances
(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 8, No. 10, 2017 A Survey of Schema Matching Research using Database Schemas and Instances Ali A. Alwan Mogahed Alzeber International Islamic University Malaysia, IIUM, International Islamic University Malaysia, IIUM Kuala Lumpur, Malaysia Kuala Lumpur, Malaysia Azlin Nordin Abedallah Zaid Abualkishik International Islamic University Malaysia, IIUM College of Computer Information Technology Kuala Lumpur, Malaysia American University in the Emirates Dubai, United Arab Emirates Abstract—Schema matching is considered as one of the which might negatively influence in the process of integrating essential phases of data integration in database systems. The the data [3]. main aim of the schema matching process is to identify the correlation between schema which helps later in the data Many firms might attempt to integrate some developed integration process. The main issue concern of schema matching heterogeneous data sources where these businesses have is how to support the merging decision by providing the various databases, and each database might consist of a vast correspondence between attributes through syntactic and number of tables that encompass different attributes. The semantic heterogeneous in data sources. There have been a lot of heterogeneity in these data sources leads to increasing the attempts in the literature toward utilizing database instances to complexity of handling these data, which result in the need for detect the correspondence between attributes during schema data integration [4]. Identifying the conflicts of (syntax matching process. Many approaches based on instances have (structure) and semantic heterogeneity) between schemas is a been proposed aiming at improving the accuracy of the matching significant issue during data integration. -
A Distributional Semantic Search Infrastructure for Linked Dataspaces
A Distributional Semantic Search Infrastructure for Linked Dataspaces Andr´eFreitas, Se´an O’Riain, Edward Curry Digital Enterprise Research Institute (DERI) National University of Ireland, Galway Abstract. This paper describes and demonstrates a distributional se- mantic search service infrastructure for Linked Dataspaces. The center of the approach relies on the use of a distributional semantics infrastruc- ture to provide semantic search and query services over data for users and applications, improving data accessibility over the Dataspace. By ac- cessing the services through a REST API, users can semantically index and search over data using the distributional semantic knowledge embed- ded in the reference corpus. The use of distributional semantic models, which rely on the automatic extraction from large corpora, supports a comprehensive and approximative semantic matching mechanism with a low associated adaptation cost for the inclusion of new data sources. Keywords: Distributional Semantics, Semantic Matching, Semantic Search, Explicit Semantic Analysis, Dataspaces, Linked Data. 1 Motivation Within the realm of the Web and of Big Data, dataspaces where data is more complex, sparse and heterogeneous are becoming more common. Consuming this data demands applications and search/query mechanisms with the seman- tic flexibility necessary to cope with the semantic/vocabulary gap between users, different applications and data sources within the dataspace. Traditionally, con- suming structured data demands that users, applications and databases share the same vocabulary before data consumption, where the semantic matching process is done manually. As dataspaces grow in complexity, the ability to se- mantically search over data using one’s own vocabulary becomes a fundamental functionality for dataspaces. -
YASA-M: a Semantic Web Service Matchmaker
YASA-M : a semantic Web service matchmaker Yassin Chabeb, Samir Tata, Alain Ozanne To cite this version: Yassin Chabeb, Samir Tata, Alain Ozanne. YASA-M : a semantic Web service matchmaker. 24th IEEE International Conference on Advanced Information Networking and Applications (AINA 2010):, Apr 2010, Perth, Australia. pp.966 - 973, 10.1109/AINA.2010.122. hal-01356801 HAL Id: hal-01356801 https://hal.archives-ouvertes.fr/hal-01356801 Submitted on 26 Aug 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. YASA-M: A Semantic Web Service Matchmaker Yassin Chabeb, Samir Tata, and Alain Ozanne TELECOM SudParis, CNRS UMR Samovar, Evry, France Email: fyassin.chabeb, samir.tata, [email protected] Abstract—In this paper, we present new algorithms for match- This paper is organized as follows. Section II presents a state ing Web services described in YASA4WSDL (YASA for short). of the art of semantic matching approaches. In Section III, We have already defined YASA that overcomes some issues we give an overview of our service description language then missing in WSDL or SAWSDL. In this paper, we continue on our contribution and show how YASA Web services are we detail our service matching algorithm.