Relevant bibliographies by topics / Ontology mapping

Academic literature on the topic 'Ontology mapping'

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Published: 4 June 2021
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Journal articles on the topic "Ontology mapping"

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Serpeloni, Felipe, Regina Moraes, and Rodrigo Bonacin. "Ontology Mapping Validation." International Journal of Web Portals 3, no. 3 (July 2011): 1–11. http://dx.doi.org/10.4018/jwp.2011070101.

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The use of ontologies and ontology mappings is increasing in companies. Often the same context is modeled in different ontologies. Mapping is necessary to integrate these ontologies; however, in many cases these mappings are incorrect, i.e., they incorrectly link semantic concepts with different meanings. Tools that validate these mappings are necessary to ensure reliable communication between heterogeneous systems. This validation cannot be done in a completely automatic way, because the mappings are based on human interpretation. This work describes a semi-automatic tool that supports this activity, based on graphs that generate instances validated in a semi-automatic process that aims to ensure mapping robustness. This algorithm deals with an NP-Complete problem in order to generate all the instances. This paper presents a first prototype of the tool and the methodology used to validate the instances automatically generated by the tool.
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Zhu, Linli, Yu Pan, Mohammad Reza Farahani, and Wei Gao. "Graph Laplacian Based Ontology Regularization Distance Framework for Ontology Similarity Measuring and Ontology Mapping." Journal of Computational Mathematica 1, no. 1 (June 30, 2017): 88–98. http://dx.doi.org/10.26524/cm6.

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Shenoy, Manjula. "Secured Ontology Mapping." International journal of Web & Semantic Technology 3, no. 4 (October 31, 2012): 83–91. http://dx.doi.org/10.5121/ijwest.2012.3408.

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Lei Liu, Xiang Ji, Zhao Liu, Qikai Guo, and Shuai Lu. "An Ontology Mapping Method using Ontology Mapping Results as Background Knowledge." International Journal of Advancements in Computing Technology 5, no. 6 (March 31, 2013): 318–25. http://dx.doi.org/10.4156/ijact.vol5.issue6.37.

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KALFOGLOU, YANNIS, and MARCO SCHORLEMMER. "Ontology mapping: the state of the art." Knowledge Engineering Review 18, no. 1 (January 2003): 1–31. http://dx.doi.org/10.1017/s0269888903000651.

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Ontology mapping is seen as a solution provider in today's landscape of ontology research. As the number of ontologies that are made publicly available and accessible on the Web increases steadily, so does the need for applications to use them. A single ontology is no longer enough to support the tasks envisaged by a distributed environment like the Semantic Web. Multiple ontologies need to be accessed from several applications. Mapping could provide a common layer from which several ontologies could be accessed and hence could exchange information in semantically sound manners. Developing such mappings has been the focus of a variety of works originating from diverse communities over a number of years. In this article we comprehensively review and present these works. We also provide insights on the pragmatics of ontology mapping and elaborate on a theoretical approach for defining ontology mapping.
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Zhu, Junwu. "Survey on Ontology Mapping." Physics Procedia 24 (2012): 1857–62. http://dx.doi.org/10.1016/j.phpro.2012.02.273.

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Sleeman, Jumah Y. J., and Jehad Abdulhamid Hammad. "A Review of Accessing Big Data with Significant Ontologies." Knowledge Engineering and Data Science 3, no. 2 (December 31, 2020): 67. http://dx.doi.org/10.17977/um018v3i22020p67-76.

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Ontology Based Data Access (OBDA) is a recently proposed approach which is able to provide a conceptual view on relational data sources. It addresses the problem of the direct access to big data through providing end-users with an ontology that goes between users and sources in which the ontology is connected to the data via mappings. We introduced the languages used to represent the ontologies and the mapping assertions technique that derived the query answering from sources. Query answering is divided into two steps: (i) Ontology rewriting, in which the query is rewritten with respect to the ontology into new query; (ii) mapping rewriting the query that obtained from previous step reformulating it over the data sources using mapping assertions. In this survey, we aim to study the earlier works done by other researchers in the fields of ontology, mapping and query answering over data sources.
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WANG, YAPING, GUIHUA HAN, JIANGHUA GE, JINGRUI QI, and JIANYUAN XU. "RESEARCH ON DESIGN METHOD OF DEMAND-DRIVEN PRODUCT CONFIGURATION FOR MASS CUSTOMIZATION." Journal of Advanced Manufacturing Systems 10, no. 01 (June 2011): 117–25. http://dx.doi.org/10.1142/s0219686711002053.

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This paper proposed demand-driven personalized product configuration design method. A variety of customer orders were clustered and fuzzy transformed; using ontology's feature to establish customer demand ontology model; in order to enable the product family to meet the dynamic demand of customers, established mapping relationship of customer demands and product family; using ontology to express product family model, achieved mapping of customer needs ontology and product family ontology, and improved efficiency of product configuration. Finally, we take planetary reducer as an example to demonstrate the feasibility of the method.
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Yang, Kai, Amanda Lo, and Robert Steele. "Ontology mediated XML data translation." International Journal of Web Information Systems 4, no. 2 (June 20, 2008): 181–97. http://dx.doi.org/10.1108/17440080810882360.

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PurposeThe purpose of this paper is to address problems that exist in the context of XML to ontology translation. Existing research results dealing with XML to ontology translation do not facilitate bidirectional data translation due to the fundamental differences between XML schema and ontologies. This paper proposes a mapping representation ontology for modeling concept mappings defined between XML schema and ontology, enabling data translation without any information loss.Design/methodology/approachA two‐step compensation approach is proposed that aims to prevent the loss of data type, structural and relational information during any single trip data translation. The mapping representation ontology proposed is capable in capturing enough information to compensate the loss of information during translation, hence allowing bidirectional conversions between XML and ontology.FindingsFundamental differences between XML schema and ontology are identified as the main reason causing the loss of information during data translation. A compensation approach that captures a sufficient amount of concept mapping information data translation is found to be successful in enabling lossless data transformation.Practical implicationsOutcomes from this work allow for the seamless data translation between XML documents, it demonstrates how web applications can seamlessly communicate and exchange data with each other without the need to conform to a predefined data standard. This paper aims to enhance interoperability between distributed systems.Originality/valueThis paper presents a mapping ontology that captures concept mappings defined between XML schema and ontology. Two algorithms facilitating the bidirectional XML to ontology translation are also proposed.
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Li, Shun Xin, and Lei Jun Shi. "Ontology Mapping Based on Domain Framework." Applied Mechanics and Materials 65 (June 2011): 21–24. http://dx.doi.org/10.4028/www.scientific.net/amm.65.21.

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For service-oriented software engineering, the basic problem faced is the sharing and interaction of information between software, and in essence, is to make the software to communicate from the semantic level. Ontology is as a carrier of information and expression methods and is used to describe software entities. We may want to take into account the reuse of ontology modules, meanwhile different ontology libraries may face ontology heterogeneity and other problems, and all the above problems can be resolved through ontology mapping. Based on the process of ontology construction, the ways of presenting information by ontology and the reference to the practical process of ontology mapping, we put forward a framework of ontology mapping and ontology mapping algorithm which analyze from the ontology concept, instance, structure, hierarchy, and while take advantage of WordNet thesaurus and manual adjustments and other means to improve the accuracy of mapping discovery and mapping process.
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Dissertations / Theses on the topic "Ontology mapping"

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Ghawi, Raji. "Ontology-based cooperation of information systems : contributions to database-to-ontology mapping and XML-to-ontology mapping." Phd thesis, Université de Bourgogne, 2010. http://tel.archives-ouvertes.fr/tel-00559089.

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This thesis treats the area of ontology-based cooperation of information systems. We propose a global architecture called OWSCIS that is based on ontologies and web-services for the cooperation of distributed heterogeneous information systems. In this thesis, we focus on the problem of connecting the local information sources to the local ontologies within OWSCIS architecture. This problem is articulated by three main axes: 1) the creation of the local ontology from the local information sources, 2) the mapping of local information sources to an existing local ontology, and 3) the translation of queries over the local ontologies into queries over local information sources.
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Corsar, David. "Developing knowledge-based systems through ontology mapping and ontology guided knowledge acquisition." Thesis, Available from the University of Aberdeen Library and Historic Collections Digital Resources, 2009. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?application=DIGITOOL-3&owner=resourcediscovery&custom_att_2=simple_viewer&pid=25800.

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Wang, Ying. "Developing Ontology Mapping approaches for Semantic Interoperability." Thesis, Queen's University Belfast, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.527911.

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Sengupta, Kunal. "A Language for Inconsistency-Tolerant Ontology Mapping." Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1441044183.

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Huve, Cristiane Aparecida Gonçalves. "An architecture for mapping relational database to ontology." reponame:Repositório Institucional da UFPR, 2017. http://hdl.handle.net/1884/47423.

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Orientadora : Profa. Leticia Mara Peres
Dissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 01/02/2017
Inclui referências : f. 80-85
Resumo: Nos últimos anos tem sido propostos trabalhos sobre definições de mapeamento de um banco de dados para ontologias. Este trabalho de mestrado propõe a construção de uma arquitetura que viabiliza um processo de mapeamento automático de um banco de dados relacional para uma ontologia OWL. Para isto, faz uso de regras novas e existentes e tem como contribuições a nomeação dos elementos e sua eliminação quando duplicados, aumentando a legibilidade da ontologia gerada. Destacamos na arquitetura a estrutura de mapeamento de elementos, que permite manter uma rastreabilidade de origem e destino para verificações. Para validar a arquitetura e as regras propostas, um estudo de caso _e realizado utilizando um banco de dados de atendimento odontológico. Palavras-Chave: Banco de dados relacional. Ontologia. Mapeamento.
Abstract: In recent years a number of researches have been written on the topic of definitions of mapping of a database to ontology. This dissertation presents the proposal and the construction of an architecture which enables an automatic mapping process of relational database to OWL ontology. For this purpose, it makes use of new and existent rules and offers as contributions naming and elimination of duplicated elements, increasing the legibility of the generated ontology. We stand out the structure of element mapping, which allows to maintain a source-to-target traceability for verifications. Validating of proposed architecture and rules is made by a case study using a dental care database. Key-words: Relational database. Ontology. Mapping.
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Arnold, Patrick. "Semantic Enrichment of Ontology Mappings." Doctoral thesis, Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-192438.

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Schema and ontology matching play an important part in the field of data integration and semantic web. Given two heterogeneous data sources, meta data matching usually constitutes the first step in the data integration workflow, which refers to the analysis and comparison of two input resources like schemas or ontologies. The result is a list of correspondences between the two schemas or ontologies, which is often called mapping or alignment. Many tools and research approaches have been proposed to automatically determine those correspondences. However, most match tools do not provide any information about the relation type that holds between matching concepts, for the simple but important reason that most common match strategies are too simple and heuristic to allow any sophisticated relation type determination. Knowing the specific type holding between two concepts, e.g., whether they are in an equality, subsumption (is-a) or part-of relation, is very important for advanced data integration tasks, such as ontology merging or ontology evolution. It is also very important for mappings in the biological or biomedical domain, where is-a and part-of relations may exceed the number of equality correspondences by far. Such more expressive mappings allow much better integration results and have scarcely been in the focus of research so far. In this doctoral thesis, the determination of the correspondence types in a given mapping is the focus of interest, which is referred to as semantic mapping enrichment. We introduce and present the mapping enrichment tool STROMA, which obtains a pre-calculated schema or ontology mapping and for each correspondence determines a semantic relation type. In contrast to previous approaches, we will strongly focus on linguistic laws and linguistic insights. By and large, linguistics is the key for precise matching and for the determination of relation types. We will introduce various strategies that make use of these linguistic laws and are able to calculate the semantic type between two matching concepts. The observations and insights gained from this research go far beyond the field of mapping enrichment and can be also applied to schema and ontology matching in general. Since generic strategies have certain limits and may not be able to determine the relation type between more complex concepts, like a laptop and a personal computer, background knowledge plays an important role in this research as well. For example, a thesaurus can help to recognize that these two concepts are in an is-a relation. We will show how background knowledge can be effectively used in this instance, how it is possible to draw conclusions even if a concept is not contained in it, how the relation types in complex paths can be resolved and how time complexity can be reduced by a so-called bidirectional search. The developed techniques go far beyond the background knowledge exploitation of previous approaches, and are now part of the semantic repository SemRep, a flexible and extendable system that combines different lexicographic resources. Further on, we will show how additional lexicographic resources can be developed automatically by parsing Wikipedia articles. The proposed Wikipedia relation extraction approach yields some millions of additional relations, which constitute significant additional knowledge for mapping enrichment. The extracted relations were also added to SemRep, which thus became a comprehensive background knowledge resource. To augment the quality of the repository, different techniques were used to discover and delete irrelevant semantic relations. We could show in several experiments that STROMA obtains very good results w.r.t. relation type detection. In a comparative evaluation, it was able to achieve considerably better results than related applications. This corroborates the overall usefulness and strengths of the implemented strategies, which were developed with particular emphasis on the principles and laws of linguistics.
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Lian, Zonghui. "A Tool to Support Ontology Creation Based on Incremental Mini-Ontology Merging." BYU ScholarsArchive, 2008. https://scholarsarchive.byu.edu/etd/1663.

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This thesis addresses the problem of tool support for semi-automatic ontology mapping and merging. Solving this problem contributes to ontology creation and evolution by relieving users from tedious and time-consuming work. This thesis shows that a tool can be built that will take a “mini-ontology” and a “growing ontology” as input and make it possible to produce manually, semi-automatically, or automatically an extended growing ontology as output. Characteristics of this tool include: (1) a graphical, interactive user interface with features that will allow users to map and merge ontologies, and (2) a framework supporting pluggable, semi-automatic, and automatic mapping and merging algorithms.
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Groß, Anika. "Evolution von ontologiebasierten Mappings in den Lebenswissenschaften." Doctoral thesis, Universitätsbibliothek Leipzig, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-136766.

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Im Bereich der Lebenswissenschaften steht eine große und wachsende Menge heterogener Datenquellen zur Verfügung, welche häufig in quellübergreifenden Analysen und Auswertungen miteinander kombiniert werden. Um eine einheitliche und strukturierte Erfassung von Wissen sowie einen formalen Austausch zwischen verschiedenen Applikationen zu erleichtern, kommen Ontologien und andere strukturierte Vokabulare zum Einsatz. Sie finden Anwendung in verschiedenen Domänen wie der Molekularbiologie oder Chemie und dienen zumeist der Annotation realer Objekte wie z.B. Gene oder Literaturquellen. Unterschiedliche Ontologien enthalten jedoch teilweise überlappendes Wissen, so dass die Bestimmung einer Abbildung (Ontologiemapping) zwischen ihnen notwendig ist. Oft ist eine manuelle Mappingerstellung zwischen großen Ontologien kaum möglich, weshalb typischerweise automatische Verfahren zu deren Abgleich (Matching) eingesetzt werden. Aufgrund neuer Forschungserkenntnisse und Nutzeranforderungen verändern sich die Ontologien kontinuierlich weiter. Die Evolution der Ontologien hat wiederum Auswirkungen auf abhängige Daten wie beispielsweise Annotations- und Ontologiemappings, welche entsprechend aktualisiert werden müssen. Im Rahmen dieser Arbeit werden neue Methoden und Algorithmen zum Umgang mit der Evolution ontologie-basierter Mappings entwickelt. Dabei wird die generische Infrastruktur GOMMA zur Verwaltung und Analyse der Evolution von Ontologien und Mappings genutzt und erweitert. Zunächst wurde eine vergleichende Analyse der Evolution von Ontologiemappings für drei Subdomänen der Lebenswissenschaften durchgeführt. Ontologien sowie Mappings unterliegen teilweise starken Änderungen, wobei die Evolutionsintensität von der untersuchten Domäne abhängt. Insgesamt zeigt sich ein deutlicher Einfluss von Ontologieänderungen auf Ontologiemappings. Dementsprechend können bestehende Mappings infolge der Weiterentwicklung von Ontologien ungültig werden, so dass sie auf aktuelle Ontologieversionen migriert werden müssen. Dabei sollte eine aufwendige Neubestimmung der Mappings vermieden werden. In dieser Arbeit werden zwei generische Algorithmen zur (semi-) automatischen Adaptierung von Ontologiemappings eingeführt. Ein Ansatz basiert auf der Komposition von Ontologiemappings, wohingegen der andere Ansatz eine individuelle Behandlung von Ontologieänderungen zur Adaptierung der Mappings erlaubt. Beide Verfahren ermöglichen die Wiederverwendung unbeeinflusster, bereits bestätigter Mappingteile und adaptieren nur die von Änderungen betroffenen Bereiche der Mappings. Eine Evaluierung für sehr große, biomedizinische Ontologien und Mappings zeigt, dass beide Verfahren qualitativ hochwertige Ergebnisse produzieren. Ähnlich zu Ontologiemappings werden auch ontologiebasierte Annotationsmappings durch Ontologieänderungen beeinflusst. Die Arbeit stellt einen generischen Ansatz zur Bewertung der Qualität von Annotationsmappings auf Basis ihrer Evolution vor. Verschiedene Qualitätsmaße erlauben die Identifikation glaubwürdiger Annotationen beispielsweise anhand ihrer Stabilität oder Herkunftsinformationen. Eine umfassende Analyse großer Annotationsdatenquellen zeigt zahlreiche Instabilitäten z.B. aufgrund temporärer Annotationslöschungen. Dementsprechend stellt sich die Frage, inwieweit die Datenevolution zu einer Veränderung von abhängigen Analyseergebnissen führen kann. Dazu werden die Auswirkungen der Ontologie- und Annotationsevolution auf sogenannte funktionale Analysen großer biologischer Datensätze untersucht. Eine Evaluierung anhand verschiedener Stabilitätsmaße erlaubt die Bewertung der Änderungsintensität der Ergebnisse und gibt Aufschluss, inwieweit Nutzer mit einer signifikanten Veränderung ihrer Ergebnisse rechnen müssen. Darüber hinaus wird GOMMA um effiziente Verfahren für das Matching sehr großer Ontologien erweitert. Diese werden u.a. für den Abgleich neuer Konzepte während der Adaptierung von Ontologiemappings benötigt. Viele der existierenden Match-Systeme skalieren nicht für das Matching besonders großer Ontologien wie sie im Bereich der Lebenswissenschaften auftreten. Ein effizienter, kompositionsbasierter Ansatz gleicht Ontologien indirekt ab, indem existierende Mappings zu Mediatorontologien wiederverwendet und miteinander kombiniert werden. Mediatorontologien enthalten wertvolles Hintergrundwissen, so dass sich die Mappingqualität im Vergleich zu einem direkten Matching verbessern kann. Zudem werden generelle Strategien für das parallele Ontologie-Matching unter Verwendung mehrerer Rechenknoten vorgestellt. Eine größenbasierte Partitionierung der Eingabeontologien verspricht eine gute Lastbalancierung und Skalierbarkeit, da kleinere Teilaufgaben des Matchings parallel verarbeitet werden können. Die Evaluierung im Rahmen der Ontology Alignment Evaluation Initiative (OAEI) vergleicht GOMMA und andere Systeme für das Matching von Ontologien in verschiedenen Domänen. GOMMA kann u.a. durch Anwendung des parallelen und kompositionsbasierten Matchings sehr gute Ergebnisse bezüglich der Effektivität und Effizienz des Matchings, insbesondere für Ontologien aus dem Bereich der Lebenswissenschaften, erreichen
In the life sciences, there is an increasing number of heterogeneous data sources that need to be integrated and combined in comprehensive analysis tasks. Often ontologies and other structured vocabularies are used to provide a formal representation of knowledge and to facilitate data exchange between different applications. Ontologies are used in different domains like molecular biology or chemistry. One of their most important applications is the annotation of real-world objects like genes or publications. Since different ontologies can contain overlapping knowledge it is necessary to determine mappings between them (ontology mappings). A manual mapping creation can be very time-consuming or even infeasible such that (semi-) automatic ontology matching methods are typically applied. Ontologies are not static but underlie continuous modifications due to new research insights and changing user requirements. The evolution of ontologies can have impact on dependent data like annotation or ontology mappings. This thesis presents novel methods and algorithms to deal with the evolution of ontology-based mappings. Thereby the generic infrastructure GOMMA is used and extended to manage and analyze the evolution of ontologies and mappings. First, a comparative evolution analysis for ontologies and mappings from three life science domains shows heavy changes in ontologies and mappings as well as an impact of ontology changes on the mappings. Hence, existing ontology mappings can become invalid and need to be migrated to current ontology versions. Thereby an expensive redetermination of the mappings should be avoided. This thesis introduces two generic algorithms to (semi-) automatically adapt ontology mappings: (1) a composition-based adaptation relies on the principle of mapping composition, and (2) a diff-based adaptation algorithm allows for individually handling change operations to update mappings. Both approaches reuse unaffected mapping parts, and adapt only affected parts of the mappings. An evaluation for very large biomedical ontologies and mappings shows that both approaches produce ontology mappings of high quality. Similarly, ontology changes may also affect ontology-based annotation mappings. The thesis introduces a generic evaluation approach to assess the quality of annotation mappings based on their evolution. Different quality measures allow for the identification of reliable annotations, e.g., based on their stability or provenance information. A comprehensive analysis of large annotation data sources shows numerous instabilities, e.g., due to the temporary absence of annotations. Such modifications may influence results of dependent applications such as functional enrichment analyses that describe experimental data in terms of ontological groupings. The question arises to what degree ontology and annotation changes may affect such analyses. Based on different stability measures the evaluation assesses change intensities of application results and gives insights whether users need to expect significant changes of their analysis results. Moreover, GOMMA is extended by large-scale ontology matching techniques. Such techniques are useful, a.o., to match new concepts during ontology mapping adaptation. Many existing match systems do not scale for aligning very large ontologies, e.g., from the life science domain. One efficient composition-based approach indirectly computes ontology mappings by reusing and combining existing mappings to intermediate ontologies. Intermediate ontologies can contain useful background knowledge such that the mapping quality can be improved compared to a direct match approach. Moreover, the thesis introduces general strategies for matching ontologies in parallel using several computing nodes. A size-based partitioning of the input ontologies enables good load balancing and scalability since smaller match tasks can be processed in parallel. The evaluation of the Ontology Alignment Evaluation Initiative (OAEI) compares GOMMA and other systems in terms of matching ontologies from different domains. Using the parallel and composition-based matching, GOMMA can achieve very good results w.r.t. efficiency and effectiveness, especially for ontologies from the life science domain
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Kong, Choi-yu. "Effective partial ontology mapping in a pervasive computing environment." Click to view the E-thesis via HKUTO, 2004. http://sunzi.lib.hku.hk/hkuto/record/B32002737.

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Kong, Choi-yu, and 江采如. "Effective partial ontology mapping in a pervasive computing environment." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B32002737.

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Books on the topic "Ontology mapping"

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Mapping Sustainability Knowledge Enetworking And The Value Chain. Springer, 2007.

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Fraunhofer, Hedwig. Biopolitics, Materiality and Meaning in Modern European Drama. Edinburgh University Press, 2020. http://dx.doi.org/10.3366/edinburgh/9781474467438.001.0001.

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Mapping the -- not always chronological -- trajectory from representationalist-naturalist theatre (Strindberg, Sartre) to the theatre of the historical avant-garde (Brecht, Artaud), this book puts milestones of modernist theatre in conversation with new materialist, posthumanist philosophy and affect theory. Arguing that existing modernization theories have been unnecessarily one-sided, Biopolitics, Materiality and Meaning in Modern European Drama offers a rewriting of modernity that cuts across binary methodologies – nature and culture, mind and matter, epistemology and ontology, critique and affirmative writing, dramatic and postdramatic theatre. Going beyond the exclusive focus on questions of identity, representation and meaning on the one hand or materiality on the other hand, the book captures the complex material-discursive forces that have shaped modernity and modern theatre. In powerfully prescient readings of modern anxiety, contagion and performance, the volume specifically reworks the biopolitical, immunitarian exclusions that mark Western epistemology leading up to and beyond modernity’s totalitarian crisis point. The book reveals the performativity of theatre in its double sense -- as theatrical production and as the intra-activity of an open and dynamic system of relations between multiple human and more-than-human actants, energies, and affects. In modern theatre, public and private, human and more-than-human, materiality and meaning co-productively collapse in a common life.
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Book chapters on the topic "Ontology mapping"

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Noy, Natalya F. "Ontology Mapping." In Handbook on Ontologies, 573–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-92673-3_26.

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Beneventano, Domenico, Nikolai Dahlem, Sabina El Haoum, Axel Hahn, Daniele Montanari, and Matthias Reinelt. "Ontology-driven Semantic Mapping." In Enterprise Interoperability III, 329–41. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84800-221-0_26.

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Manjula Shenoy, K., K. C. Shet, and U. Dinesh Acharya. "NN Based Ontology Mapping." In Mobile Communication and Power Engineering, 122–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35864-7_18.

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Ehrig, Marc, and Steffen Staab. "QOM – Quick Ontology Mapping." In The Semantic Web – ISWC 2004, 683–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30475-3_47.

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Li, Li, Yun Yang, and Baolin Wu. "Agent-Based Ontology Mapping Towards Ontology Interoperability." In AI 2005: Advances in Artificial Intelligence, 843–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11589990_93.

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Su, Xiaomeng, and Jon Atle Gulla. "Semantic Enrichment for Ontology Mapping." In Natural Language Processing and Information Systems, 217–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27779-8_19.

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Ehrig, Marc, and York Sure. "Ontology Mapping – An Integrated Approach." In Lecture Notes in Computer Science, 76–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25956-5_6.

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Tang, Jie, Bang-Yong Liang, Juanzi Li, and Kehong Wang. "Risk Minimization Based Ontology Mapping." In Content Computing, 469–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30483-8_58.

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Ma, Zongmin, Fu Zhang, Li Yan, and Jingwei Cheng. "Fuzzy Semantic Web Ontology Mapping." In Fuzzy Knowledge Management for the Semantic Web, 157–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39283-2_6.

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Wang, Zongjiang, Yinglin Wang, Shensheng Zhang, Ge Shen, and Tao Du. "Effective Large Scale Ontology Mapping." In Knowledge Science, Engineering and Management, 454–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11811220_38.

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Conference papers on the topic "Ontology mapping"

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Elgedawy, Islam. "Conditional Ontology Mapping." In 2012 IEEE 36th IEEE Annual Computer Software and Applications Conference Workshops (COMPSACW). IEEE, 2012. http://dx.doi.org/10.1109/compsacw.2012.109.

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Cheatham, Michelle. "Targeted ontology mapping." In 2010 International Symposium on Collaborative Technologies and Systems. IEEE, 2010. http://dx.doi.org/10.1109/cts.2010.5478518.

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Nguyen, Khai, Kaisei Reio, and Ryutaro Ichise. "Clinical Ontology Mapping." In International Workshop on Artificial Intelligence for Health. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006753107220726.

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Kim, OkJoon, Uma Jayaram, Sankar Jayaram, and Lijuan Zhu. "An Ontology Mapping Application Using a Shared Ontology Approach and a Bridge Ontology." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87754.

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This paper presents our continuing work to develop methods to exchange product knowledge in the semantic level in the CAD/CAE domains. We present an approach based on a shared ontology, in which a higher level of ontologies are shared among lower levels of ontologies. Key mapping strategies, such as Equivalency, Attribute Similarity, Composition Similarity, and Inheritance Similarity are defined to map concepts and properties defined in a product design domain and an assembly simulation domain. In addition, a Bridge Ontology is designed to store information obtained from mapping processes and construct a link between different knowledge repositories. An Ontology Mapping Application (OMA) which brings together all these elements has been designed and implemented. It is a Java-based application that allows the user to load source and target ontologies, calculate concept and property similarities between them, display the mapping results, and output a corresponding Bridge Ontology.
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Huang, Li, Guoxiong Hu, and Xinghe Yang. "Review of ontology mapping." In 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet). IEEE, 2012. http://dx.doi.org/10.1109/cecnet.2012.6202092.

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Conroy, Colm, Declan O'Sullivan, and David Lewis. "Ontology Mapping Through Tagging." In 2008 International Conference on Complex, Intelligent and Software Intensive Systems. IEEE, 2008. http://dx.doi.org/10.1109/cisis.2008.14.

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Breitman, Karin K., Daniela Brauner, Marco Antonio Casanova, Ruy Milidi, Alexandre Gazola, and Marcelo Perazolo. "Instance-Based Ontology Mapping." In 2008 5th IEEE Workshop on Engineering of Autonomic and Autonomous Systems (EASe 2008). IEEE, 2008. http://dx.doi.org/10.1109/ease.2008.18.

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Lembo, Domenico, Riccardo Rosati, Valerio Santarelli, Domenico Fabio Savo, and Evgenij Thorstensen. "Mapping Repair in Ontology-based Data Access Evolving Systems." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/161.

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In this paper we study the evolution of ontology-based data access (OBDA) specifications, and focus on the case in which the ontology and/or the data source schema change, which may require a modification to the mapping between them to preserve both consistency and knowledge. Our approach is based on the idea of repairing the mapping according to the usual principle of minimal change and on a recent, mapping-based notion of consistency of the specification. We define and analyze two notions of mapping repair under ontology and source schema update. We then present a set of results on the complexity of query answering in the above framework, when the ontology is expressed in DL-LiteR.
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Liping, Zheng, Li Guangyao, Shajing Shajing, and Liang Yongquan. "Design of Ontology Mapping Framework." In 2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06). IEEE, 2006. http://dx.doi.org/10.1109/cimca.2006.84.

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Xu, Xiaoliang, You Wu, and Jinkui Chen. "Fuzzy FCA Based Ontology Mapping." In 2010 First International Conference on Networking and Distributed Computing (ICNDC). IEEE, 2010. http://dx.doi.org/10.1109/icndc.2010.45.

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Reports on the topic "Ontology mapping"

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Pan, Rong, Zhongli Ding, Yang Yu, and Yun Peng. A Bayesian Network Approach to Ontology Mapping. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada439697.

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