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Journal articles on the topic 'Ontology mapping'

To see the other types of publications on this topic, follow the link: Ontology mapping.

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

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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3

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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4

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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5

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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9

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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10

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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11

Ivanova, Tatyana. "Ontology Alignment." International Journal of Knowledge and Systems Science 1, no. 4 (October 2010): 22–40. http://dx.doi.org/10.4018/jkss.2010100102.

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A grand number of ontologies have been developed and are publicly accessible on the Web making techniques for mapping between various ontologies more significant. Research has been made in the area of ontology alignment, a grand number of approaches, algorithms, and tools have been developed in recent years, but are still not “perfect” and excellent knowledge. In this article, the author makes an overall view of the state of ontology alignment, including the latest research, comparing many approaches, and analyzing their strengths and drawbacks. The main motivation behind this work is the fact that despite many component matching solutions that have been developed so far, there is no integrated solution that is a clear success, which can be used for ontology mapping in all cases, making knowledge about developed ontology mapping methods and their clear classification needed.

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Wu, J. Z., X. Yu, and W. Gao. "Distance computation of ontology vector for ontology similarity measuring and ontology mapping." Journal of Difference Equations and Applications 23, no. 1-2 (March 3, 2016): 30–41. http://dx.doi.org/10.1080/10236198.2016.1148147.

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13

Jiang, Jian Jun, Jun Biao Wang, and Nan Liu. "The Ontology Mapping Technology Based on the Information Coding Ontology Model." Advanced Materials Research 139-141 (October 2010): 1022–27. http://dx.doi.org/10.4028/www.scientific.net/amr.139-141.1022.

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To our knowledge, there is no paper in the open literature dealing with the topic in the title. This paper provides an Information Coding Ontology Model (ICOM) to shield semantic heterogeneous of information resources, and to achieve information integration. Sections 1 and 2 of the full paper present the information coding ontology model and its formal definition, then design the mapping framework of the information coding ontology model. Section 3 discusses the mapping algorithm of the information coding ontology model from three aspects: semantic similarity analysis, obtaining semantic similarity algorithm, and semantic mapping and its implementation. Section 4 gives Fig. 7 as the mapping flow chart of the information coding ontology model and uses a numerical example to illustrate the four-step procedure of the mapping implementation process.

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Vargas-Vera, Maria, and Miklos Nagy. "State of the Art on Ontology Alignment." International Journal of Knowledge Society Research 6, no. 1 (January 2015): 17–42. http://dx.doi.org/10.4018/ijksr.2015010102.

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Ontology mapping as a semantic data integration approach has evolved from traditional data integration solutions. The core problems and open issues related to early data integration approaches are also applicable to ontology mapping on the Semantic Web community. Therefore, in this review the authors present the related literature, starting from the traditional data integration approaches, in order to highlight the evolution of data integration from the early approaches. Once the roots of semantic data integration have been presented, the authors proceed to introduce the state-of-the-art of the ontology mappings systems including the early approaches and the systems that can be compared through the Ontology Alignment Initiative (OAEI).

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15

Silva, Nuno, and Joao Rocha. "Multidimensional service-oriented ontology mapping." International Journal of Web Engineering and Technology 2, no. 1 (2005): 50. http://dx.doi.org/10.1504/ijwet.2005.007464.

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Choi, Namyoun, Il-Yeol Song, and Hyoil Han. "A survey on ontology mapping." ACM SIGMOD Record 35, no. 3 (September 2006): 34–41. http://dx.doi.org/10.1145/1168092.1168097.

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17

Zhang, Hai Fei. "A On-Demand Mapping Method on Query Request in Relational Database Semantic Query." Applied Mechanics and Materials 411-414 (September 2013): 341–44. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.341.

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Relational database semantic query namely RDF access to relational database is an important issue in semantic Web research. To realize the query is to build mapping relation between relational database schema and ontology. However, there is natural isomerism between them. The isomerism can be eliminated by transforming relational database schema into similar ontology and then building mapping between conversion ontology and input ontology. This paper realized an ondemanding mapping method when users request query, avoided building all concepts and attributes mapping between conversion ontology and input ontology and improved mapping efficiency.

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18

Gao, Wei, Tian Wei Xu, Li Liang, and Jian Hou Gan. "Supervised Multidimensional Scaling for Ontology Similarity Measure and Ontology Mapping via Multi-Dividing." Advanced Materials Research 748 (August 2013): 1051–58. http://dx.doi.org/10.4028/www.scientific.net/amr.748.1051.

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Ontology similarity calculation and ontology mapping are crucial research topics in information retrieval. Ontology, as a concept structure model, is also widely used in biology, physics, geography and social sciences. In this paper, we propose new algorithms for ontology similarity measurement and ontology mapping using supervised multidimensional scaling and multi-dividing technologies. Two experimental results show that the proposed new algorithm has high accuracy and efficiency on ontology similarity calculation and ontology mapping in certain applications.

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Gao, Wei, Mohammad Reza Farahani, Adnan Aslam, and Sunilkumar Hosamani. "Distance learning techniques for ontology similarity measuring and ontology mapping." Cluster Computing 20, no. 2 (May 3, 2017): 959–68. http://dx.doi.org/10.1007/s10586-017-0887-3.

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20

Ryma, Guefrouchi, and Kholladi Mohamed-Khireddine. "Genetic Algorithm With Hill Climbing for Correspondences Discovery in Ontology Mapping." Journal of Information Technology Research 12, no. 4 (October 2019): 153–70. http://dx.doi.org/10.4018/jitr.2019100108.

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Meta-heuristics are used as a tool for ontology mapping process in order to improve their performance in mapping quality and computational time. In this article, ontology mapping is resolved as an optimization problem. It aims at optimizing correspondences discovery between similar concepts of source and target ontologies. For better guiding and accelerating the concepts correspondences discovery, the article proposes a meta-heuristic hybridization which incorporates the Hill Climbing method within the mutation operator in the genetic algorithm. For test concerns, syntactic and lexical similarities are used to validate correspondences in candidate mappings. The obtained results show the effectiveness of the proposition for improving mapping performances in quality and computational time even for large OAEI ontologies.

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Vargas-Vera, Maria. "The Implementation of DSSim." International Journal of Knowledge Society Research 8, no. 3 (July 2017): 34–53. http://dx.doi.org/10.4018/ijksr.2017070103.

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This paper presents the decisions taken during the implementation of DSSim (DSSim stands for Similarity based on Dempster-Shafer) our multi-agent ontology mapping system. It describes several types of agents and their roles in the DSSim architecture. These agents are mapping agents which are able to perform either semantic or syntactic similarity. Our architecture is generic as no mappings need to be learned in advance and it could be easily extended by adding new mapping agents in the framework. The new added mapping agents could run different similarity algorithms (either semantic or syntactic). In this way, DSSim could assess which algorithm has a better performance. Additionally, this paper presents the algorithms used in our ontology alignment system DSSim.

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Ling, Shi Yong, and Jin Hong Gong. "Research of Composite Ontology Mapping Strategy on the Parsing Graph." Advanced Materials Research 765-767 (September 2013): 1068–72. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.1068.

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According to complex context relation of ontology, considering different input schema, ontology graph is created with general environment. Based on ontology structure, the paper constructs multiple level graph representation, By introducing similarity propagation of structural and instance level on context relation and rapid mapping with a rapid matching algorithm, a composite ontology mapping strategy is proposed, which iteratively achieves ontology mapping result with reused idea. Finally feasibility and effectiveness of the strategy is proved by complexity analysis to algorithm and some contrast test.

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23

JIANG, GUOFEI, GEORGE CYBENKO, and JAMES A. HENDLER. "SEMANTIC INTEROPERABILITY AND INFORMATION FLUIDITY." International Journal of Cooperative Information Systems 15, no. 01 (March 2006): 1–21. http://dx.doi.org/10.1142/s0218843006001268.

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Ontologies are developed to describe data semantics on the Semantic Web. Given the distributed nature and scale of the Semantic Web, a large number of ontologies with different terminologies and structures will be created to describe the same concepts and domains. Without semantic mapping, information fluidity within the Web could be blocked at the boundaries of these ontologies. Therefore, ontology mapping is needed to translate datasets represented by disparate ontologies. We believe that over time communities will incrementally build an ontology mapping between select ontologies based on their own communication interests. How will these interest-driven mapping activities eventually change semantic interoperability and information fluidity across the Web? This paper proposes metrics to quantify information fluidity and builds an analytical model with "small-world" graph theory to analyze the growth of the Semantic Web. Further with this model, we analyze how information fluidity can evolve by "market-driven" semantic mapping activities occurring across the Web. Our results can be useful in evaluating mapping efforts needed for large-scale heterogeneous information systems. One conclusion, based on this model, is that the development of decentralized ontology mappings can lead to significant information fluidity within the Semantic Web.

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Jenifer, M., P. S. Balamurugan, and T. Prince. "Ontology Mapping for Dynamic Multiagent Environment." International Journal of Research in Computer Science 2, no. 3 (April 30, 2012): 51–56. http://dx.doi.org/10.7815/ijorcs.23.2012.029.

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LI, Ye-da, and Xiong-wen PANG. "Domain learning-based ontology mapping method." Journal of Computer Applications 29, no. 7 (July 30, 2009): 1981–84. http://dx.doi.org/10.3724/sp.j.1087.2009.01981.

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Tang, Jie, Juanzi Li, Bangyong Liang, Xiaotong Huang, Yi Li, and Kehong Wang. "Using Bayesian decision for ontology mapping." Journal of Web Semantics 4, no. 4 (December 2006): 243–62. http://dx.doi.org/10.1016/j.websem.2006.06.001.

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27

Mannan. J, Mannar, Sudha T, and Sundarambal M. "Dynamic Inter-Domain Multiple Ontology Mapping." International Journal of Engineering Trends and Technology 36, no. 4 (June 25, 2016): 174–79. http://dx.doi.org/10.14445/22315381/ijett-v36p232.

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Leshcheva, Irina, and Dmitry Leshchev. "Ontology as mapping of material world." SHS Web of Conferences 44 (2018): 00055. http://dx.doi.org/10.1051/shsconf/20184400055.

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This paper reviews currently available approaches and methods of automated population and enrichment of ontologies or ontology-based knowledge bases by structured data stored in various heterogeneous sources. Advantages and disadvantages of each approach are pointed out. The results of the analysis allow concluding that the existing methods are not effective enough to solve practical problems. A new method suggested does not have any of the specified disadvantages. The suggested method allows integrating data from different types of sources and considering the distributed nature of data and the necessity of authentication during accessing network resources. The method also suggests a solution to the problem of integrated data conflict resolution so that it will reduce the complexity of populating and enriching ontologies using the collected data arrays, irrespective of the format in which data are stored or represented.

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Qazvinian, Vahed, Hassan Abolhassani, Seyed H. Haeri (Hossein), and Babak Bagheri Hariri. "Evolutionary coincidence-based ontology mapping extraction." Expert Systems 25, no. 3 (July 2008): 221–36. http://dx.doi.org/10.1111/j.1468-0394.2008.00462.x.

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30

Hu, Bo, and Bin Hu. "On Capturing Semantics in Ontology Mapping." World Wide Web 11, no. 3 (April 22, 2008): 361–85. http://dx.doi.org/10.1007/s11280-008-0042-4.

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31

Harrow, Ian, Rama Balakrishnan, Ernesto Jimenez-Ruiz, Simon Jupp, Jane Lomax, Jane Reed, Martin Romacker, et al. "Ontology mapping for semantically enabled applications." Drug Discovery Today 24, no. 10 (October 2019): 2068–75. http://dx.doi.org/10.1016/j.drudis.2019.05.020.

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32

Gao, Wei, Linli Zhu, and Kaiyun Wang. "Ontology Sparse Vector Learning Algorithm for Ontology Similarity Measuring and Ontology Mapping via ADAL Technology." International Journal of Bifurcation and Chaos 25, no. 14 (December 30, 2015): 1540034. http://dx.doi.org/10.1142/s0218127415400349.

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Ontology, a model of knowledge representation and storage, has had extensive applications in pharmaceutics, social science, chemistry and biology. In the age of “big data”, the constructed concepts are often represented as higher-dimensional data by scholars, and thus the sparse learning techniques are introduced into ontology algorithms. In this paper, based on the alternating direction augmented Lagrangian method, we present an ontology optimization algorithm for ontological sparse vector learning, and a fast version of such ontology technologies. The optimal sparse vector is obtained by an iterative procedure, and the ontology function is then obtained from the sparse vector. Four simulation experiments show that our ontological sparse vector learning model has a higher precision ratio on plant ontology, humanoid robotics ontology, biology ontology and physics education ontology data for similarity measuring and ontology mapping applications.

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Doğan, Tunca. "HPO2GO: prediction of human phenotype ontology term associations for proteins using cross ontology annotation co-occurrences." PeerJ 6 (August 2, 2018): e5298. http://dx.doi.org/10.7717/peerj.5298.

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Analysing the relationships between biomolecules and the genetic diseases is a highly active area of research, where the aim is to identify the genes and their products that cause a particular disease due to functional changes originated from mutations. Biological ontologies are frequently employed in these studies, which provides researchers with extensive opportunities for knowledge discovery through computational data analysis. In this study, a novel approach is proposed for the identification of relationships between biomedical entities by automatically mapping phenotypic abnormality defining HPO terms with biomolecular function defining GO terms, where each association indicates the occurrence of the abnormality due to the loss of the biomolecular function expressed by the corresponding GO term. The proposed HPO2GO mappings were extracted by calculating the frequency of the co-annotations of the terms on the same genes/proteins, using already existing curated HPO and GO annotation sets. This was followed by the filtering of the unreliable mappings that could be observed due to chance, by statistical resampling of the co-occurrence similarity distributions. Furthermore, the biological relevance of the finalized mappings were discussed over selected cases, using the literature. The resulting HPO2GO mappings can be employed in different settings to predict and to analyse novel gene/protein—ontology term—disease relations. As an application of the proposed approach, HPO term—protein associations (i.e., HPO2protein) were predicted. In order to test the predictive performance of the method on a quantitative basis, and to compare it with the state-of-the-art, CAFA2 challenge HPO prediction target protein set was employed. The results of the benchmark indicated the potential of the proposed approach, as HPO2GO performance was among the best (Fmax = 0.35). The automated cross ontology mapping approach developed in this work may be extended to other ontologies as well, to identify unexplored relation patterns at the systemic level. The datasets, results and the source code of HPO2GO are available for download at: https://github.com/cansyl/HPO2GO.

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Li, Rong, and Lei Liu. "Approaches of Ontological Property Mapping Based on Multi-Strategy." Applied Mechanics and Materials 536-537 (April 2014): 570–73. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.570.

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Ontology mapping is the technological foundation of semantic integration, ontology merging and ontology alignment. In this paper, we propose approaches of ontological property mapping based on multi-strategy. First we use different strategies to deal with data type property and object type property. Then the optimal property mapping results of multi strategies are chosen. Finally, we present experimental results with high accuracy and point out future possible research directions.

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Gao, Yun, Mohammad Reza Farahani, and Wei Gao. "Ontology optimization tactics via distance calculating." Applied Mathematics and Nonlinear Sciences 1, no. 1 (January 29, 2016): 159–74. http://dx.doi.org/10.21042/amns.2016.1.00012.

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AbstractIn this article, we propose an ontology learning algorithm for ontology similarity measure and ontology mapping in view of distance function learning techniques. Using the distance computation formulation, all the pairs of ontology vertices are mapped into real numbers which express the distance of their corresponding vectors. The more distance between two vertices, the smaller similarity between their corresponding concepts. The stabilities of our learning algorithm are defined and several bounds are yielded via stability assumptions. The simulation experimental conclusions show that the new proposed ontology algorithm has high efficiency and accuracy in ontology similarity measure and ontology mapping in certain engineering applications.

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Pham, Thuy Thi Thu. "An Improvement Method for Semantic Mapping Database to Ontology." Journal of Computer Science and Cybernetics 34, no. 3 (November 23, 2018): 187–98. http://dx.doi.org/10.15625/1813-9663/34/3/13100.

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Enormous amount of available data in relational database (RDB) format creates a demand for automatic transforming them into OWL ontology to reuse in the Semantic Web. Many approaches have been proposed, however, most of them simply generate output ontology as the same flat structure with the original database and result in redundancy of ontology data. As an attempt to resolve the redundant problem, we propose a novel approach to generate OWL ontology from relational database while focusing on the similarity measure of duplicate attributes in relational tables. Experimental results show that the proposed method reliably predicts semantic similarity of duplicate columns and produces a better-quality OWL ontology.

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Wang, Shuying, Kevin P. Brown, Jinghui Lu, and Miriam Capretz. "A Reference Ontology Based Approach for Service Oriented Semantic Interoperability." International Journal of Web Portals 3, no. 1 (January 2011): 1–16. http://dx.doi.org/10.4018/jwp.2011010101.

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To establish effective information exchange among applications in a distributed environment, participants not only share their functions and service interfaces, but often exchange data models. This paper proposes the use of ontologies to represent data models thus allowing applications to locate and integrate these models. A reference ontology based approach for service oriented ontology management is introduced. Specifically, a domain-specific reference ontology for use in the evaluation of a practical case is developed. To validate and evaluate the approach, a prototype system is developed to provide ontology deploying, browsing and mapping operations based on a service-oriented system. Experiments provide promising results that are consistent with the original ideas of managing ontologies and optimizing ontology mappings to facilitate data interoperability in a distributed environment.

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Ivanova, Tatyana. "E-Learning resource reuse, based on bilingual ontology annotation and ontology mapping." International Journal of Advanced Computer Research 9, no. 45 (October 25, 2019): 351–64. http://dx.doi.org/10.19101/ijacr.2019.940101.

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39

Gao, Wei, Linli Zhu, Yun Guo, and Kaiyun Wang. "Ontology learning algorithm for similarity measuring and ontology mapping using linear programming." Journal of Intelligent & Fuzzy Systems 33, no. 5 (October 27, 2017): 3153–63. http://dx.doi.org/10.3233/jifs-169367.

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40

Lembo, Domenico, and Federico Maria Scafoglieri. "Ontology-based Document Spanning Systems for Information Extraction." International Journal of Semantic Computing 14, no. 01 (March 2020): 3–26. http://dx.doi.org/10.1142/s1793351x20400012.

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Information Extraction (IE) is the task of automatically organizing in a structured form data extracted from free text documents. In several contexts, it is often desirable that the extracted data are then organized according to an ontology, which provides a formal and conceptual representation of the domain of interest. Ontologies allow for a better data interpretation, as well as for their semantic integration with other information, as in Ontology-based Data Access (OBDA), a popular declarative framework for data management where an ontology is connected to a data layer through mappings. However, the data layer considered so far in OBDA has consisted essentially of relational databases, and how to declaratively couple an ontology with unstructured data sources is still unexplored. By leveraging the recent study on document spanners for rule-based IE by Fagin et al., in this paper, we propose a new framework that allows to map text documents to ontologies, in the spirit of OBDA. We investigate the problem of answering conjunctive queries in this framework. For ontologies specified in the Description Logics [Formula: see text] and [Formula: see text], we show that the problem is polynomial in the size of the underlying documents. We also provide algorithms to solve query answering by rewriting the input query on the basis of the ontology and its mapping toward the source documents. Through these techniques, we pursue a virtual approach, similar to that typically adopted in OBDA, which allows us to answer a query without having to first populate the entire ontology. Interestingly, for [Formula: see text], both the spanners used in the mapping and the one computed by the rewriting algorithm belong to the same class of expressiveness. This holds also for [Formula: see text], modulo some limitations on the form of the mapping. These results say that in these cases our framework can be easily implemented by decoupling ontology management and document access, which can be delegated to an external IE system able to process the extraction rules we use in the mapping.

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Zhu, Linli, Yu Pan, and Jiangtao Wang. "Affine Transformation Based Ontology Sparse Vector Learning Algorithm." Applied Mathematics and Nonlinear Sciences 2, no. 1 (April 6, 2017): 111–22. http://dx.doi.org/10.21042/amns.2017.1.00009.

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AbstractIn information science and other engineering applications, ontology plays an irreplaceable role to find the intrinsic semantic link between concepts and to determine the similarity score returned to the user. Ontology mapping aims to excavate the intrinsic semantic relationship between concepts from different ontologies, and the essence of these applications is similarity computation. In this article, we propose the new ontology sparse vector approximation algorithms based on the affine transformation tricks. By means of these techniques, we study the equivalent form of ontology dual problem and determine its feasible set. The simulation experiments imply that our new proposed ontology algorithm has high efficiency and accuracy in ontology similarity computation and ontology mapping in biology, chemical and related fields.

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Rujuan Wang, Qiushuang Wang, Shuai Lu, Yu Liu, and Lei Liu. "Ontology Mapping Method based on Extended Information." International Journal of Advancements in Computing Technology 4, no. 10 (June 30, 2012): 185–94. http://dx.doi.org/10.4156/ijact.vol4.issue10.22.

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K, Manjula Shenoy. "Semantic Plagiarism Detection System Using Ontology Mapping." Advanced Computing: An International Journal 3, no. 3 (May 31, 2012): 59–62. http://dx.doi.org/10.5121/acij.2012.3306.

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Lü, Wen-tao, Yang XIANG, and Bo ZHANG. "Research of concept cluster based ontology mapping." Journal of Computer Applications 28, no. 11 (June 5, 2009): 2859–62. http://dx.doi.org/10.3724/sp.j.1087.2008.02859.

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ZHONG, Qian, Juan-Zi LI, Jie TANG, and Li-Zhu ZHOU. "Data Field Based Large Scale Ontology Mapping." Chinese Journal of Computers 33, no. 6 (July 1, 2010): 955–65. http://dx.doi.org/10.3724/sp.j.1016.2010.00955.

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OUYANG, Dan-tong, Xian-ji CUI, and Yu-xin YE. "Mapping integrity constraint ontology to relational databases." Journal of China Universities of Posts and Telecommunications 17, no. 6 (December 2010): 113–21. http://dx.doi.org/10.1016/s1005-8885(09)60534-3.

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Hayamizu, T. F., S. de Coronado, G. Fragoso, N. Sioutos, J. A. Kadin, and M. Ringwald. "The mouse-human anatomy ontology mapping project." Database 2012 (March 20, 2012): bar066. http://dx.doi.org/10.1093/database/bar066.

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Wang, Rong-Lin, Stephen Edwards, and Cataia Ives. "Ontology-based semantic mapping of chemical toxicities." Toxicology 412 (January 2019): 89–100. http://dx.doi.org/10.1016/j.tox.2018.11.005.

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Li, Bin, and Junwu Zhu. "On Ontology Mapping Based on Transfer Learning." Advanced Science Letters 5, no. 2 (February 1, 2012): 831–35. http://dx.doi.org/10.1166/asl.2012.1767.

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Mao, Ming, Yefei Peng, and Michael Spring. "Ontology mapping: as a binary classification problem." Concurrency and Computation: Practice and Experience 23, no. 9 (August 28, 2010): 1010–25. http://dx.doi.org/10.1002/cpe.1633.

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