Proceedings Ontology Matching 2009
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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. -
The Semantic Web - ISWC 2004
Lecture Notes in Computer Science 3298 The Semantic Web - ISWC 2004 Third International Semantic Web Conference, Hiroshima, Japan, November 7-11, 2004. Proceedings Bearbeitet von Sheila A. McIlraith, Dimitris Plexousakis, Frank van Harmelen 1. Auflage 2004. Taschenbuch. xxxv, 844 S. Paperback ISBN 978 3 540 23798 3 Format (B x L): 15,5 x 23,5 cm Gewicht: 970 g Weitere Fachgebiete > EDV, Informatik > Datenbanken, Informationssicherheit, Geschäftssoftware > Datenkompression, Dokumentaustauschformate schnell und portofrei erhältlich bei Die Online-Fachbuchhandlung beck-shop.de ist spezialisiert auf Fachbücher, insbesondere Recht, Steuern und Wirtschaft. Im Sortiment finden Sie alle Medien (Bücher, Zeitschriften, CDs, eBooks, etc.) aller Verlage. Ergänzt wird das Programm durch Services wie Neuerscheinungsdienst oder Zusammenstellungen von Büchern zu Sonderpreisen. Der Shop führt mehr als 8 Millionen Produkte. Preface The 3rd International Semantic Web Conference (ISWC 2004) was held Novem- ber 7–11, 2004 in Hiroshima, Japan. If it is true what the proverb says: “Once by accident, twice by habit, three times by tradition,” then this third ISWC did indeed firmly establish a tradition. After the overwhelming interest in last year’s conference at Sanibel Island, Florida, this year’s conference showed that the Semantic Web is not just a one-day wonder, but has established itself firmly on the research agenda. At a time when special interest meetings with a Seman- tic Web theme are springing up at major conferences in numerous areas (ACL, VLDB, ECAI, AAAI, ECML, WWW, to name but a few), the ISWC series has established itself as the primary venue for Semantic Web research. Response to the call for papers for the conference continued to be strong. -
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. -
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. -
Trombert-Paviot, Alan Rector, Robert Baud, Pieter
HIMJ: Reviewed articles The development of CCAM: The new French coding system of clinical HIMJ HOME procedures Beatrice Trombert-Paviot, Alan Rector, Robert Baud, Pieter CONTENTS Zanstra, Caroline Martin, Egbert van der Haring, Lucienne Clavel and Jean Marie Rodrigues GUIDELINES Abstract MISSION A new French coding system of clinical procedures, the Classification Commune Des Actes Medicaux (CCAM), has been developed at the turn of the millennium (between 1996 and CONTACT US 2001). Two methodologies were used: a traditional domain- experts consensus method, and an artificial-intelligence-based semantic representation. An economic evaluation of clinical procedures was also undertaken for the rating for fee-for- service payment. We present the methodologies used and stress how the European Union research project, ‘European Consortium, Generalised Architecture for Languages, Encyclopaedias and Nomenclatures in Medicine’ (GALEN), facilitated the sharing and maintaining of consistent medical knowledge. This country case study highlights the significant cost to individual countries in developing their own HIMAA classifications in isolation. It also demonstrates the benefits of Locked Bag 2045 North Ryde, NSW contributing to international efforts such as GALEN that enable Australia 1670 harmonisation, yet still allow for diversity. ABN 54 008 451 910 Key words : Artificial intelligence; coding system; clinical procedures; terminology; GALEN Introduction Following an Act passed in the National Assembly and issued in January 1993, the Ministry -
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. -
Ontologies and the Semantic Web
Ontologies and the Semantic Web Ian Horrocks <[email protected]> Information Management Group School of Computer Science University of Manchester The Semantic Web Today’s Web • Distributed hypertext/hypermedia • Information accessed via (keyword based) search and browse • Browser tools render information for human consumption What is the Semantic Web? • Web was “invented” by Tim Berners-Lee (amongst others), a physicist working at CERN • His vision of the Web was much more ambitious than the reality of the existing (syntactic) Web: “… a set of connected applications … forming a consistent logical web of data …” “… an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation …” • This vision of the Web has become known as the Semantic Web Hard Work using “Syntactic Web” Find images of Peter Patel-Schneider, Frank van Harmelen and Alan Rector… Rev. Alan M. Gates, Associate Rector of the Church of the Holy Spirit, Lake Forest, Illinois Impossible (?) using “Syntactic Web” • Complex queries involving background knowledge – Find information about “animals that use sonar but are neither bats nor dolphins” , e.g., Barn Owl • Locating information in data repositories – Travel enquiries – Prices of goods and services – Results of human genome experiments • Finding and using “web services” – Given a DNA sequence, identify its genes, determine the proteins they can produce, and hence the biological processes they control • Delegating complex tasks to web “agents” – Book me a holiday next weekend somewhere warm, not too far away, and where they speak either French or English What is the Problem? Consider a typical web page: • Markup consists of: – rendering information (e.g., font size and colour) – Hyper-links to related content • Semantic content is accessible to humans, but not (easily) to computers… What is the (Proposed) Solution? • Add semantic annotations to web resources Dr. -
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. -
Knowledge Driven Software and “Fractal Tailoring”: Ontologies in Development Environments for Clinical Systems
Knowledge Driven Software and “Fractal Tailoring”: Ontologies in development environments for clinical systems Alan RECTOR School of Computer Science, University of Manchester, UK Abstract. Ontologies have been highly successful in applications involving annotation and data fusion. However, ontologies as the core of “Knowledge Driven Architectures” have not achieved the same influence as “Model Driven Architectures”, despite the fact that many biomedical applications require features that seem achievable only via ontological technologies – composition of descriptions, automatic classification and inference, and management of combinatorial explosions in many contexts. Our group adopted Knowledge Driven Architectures based on ontologies to address these problems in the early 1990s. In this paper we discuss first the use cases and requirements and then some of the requirements for more effective use of Knowledge Driven Architectures today: clearer separation of language and formal ontology, integration with contingent knowledge, richer and better distinguished annotations, higher order representations, integration with data models, and improved auxiliary structures to allow easy access and browsing by users. Keywords. Ontology based systems, Model driven architecture, OWL Citation. Rector A; Knowledge driven software and “fractal tailoring”: Ontologies in development environments for clinical systems. 2010; Formal Ontology in Information Systems (FOIS 2010): IOS Press; 17-30. 1. Introduction In the past fifteen years, the notion of “ontology” has gone from being an obscure “O” word to almost a synonym for “good”. Everybody thinks, and is often told, that they need ontologies. At the same time ontologies have, on the one hand, become dissociated from other forms of knowledge representation and, on the other hand, been taken by some to be all of knowledge representation. -
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.