Academic literature on the topic 'Semantic Database Model'

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Journal articles on the topic "Semantic Database Model"

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Abiteboul, Serge, and Richard Hull. "IFO: a formal semantic database model." ACM Transactions on Database Systems 12, no. 4 (1987): 525–65. http://dx.doi.org/10.1145/32204.32205.

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Fong, Joseph, Ringo Pang, Anthony Fong, Francis Pang, and Kenny Poon. "Concurrent Data Materialization for Object-Relational Database with Semantic Metadata." International Journal of Software Engineering and Knowledge Engineering 13, no. 03 (2003): 257–91. http://dx.doi.org/10.1142/s0218194003001287.

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For a company with many databases in different data models, it is necessary to consolidate them into one data model interchangeable and present data in one data model concurrently to users. The benefit is to let user stick to his/her own data model to access database in another data model. This paper presents a semantic metadata to preserve database constraints for data materialization to support user's view of database on an ad hoc base. The semantic metadata can store the captured semantics of a relational or an object-oriented database into classes and stored procedures triggered by events. The stored constraints and data can be materialized into a target database upon user request. The user is allowed to perform the data materialization many times alternatively. The process can provide a relational as well as an object oriented view to the users simultaneously. This concurrent data materialization function can be applied to data warehouse to consolidate heterogeneous database into a fact table in a data model of user's choice. Furthermore, a user can obtain either a relational view or an object-oriented view of the same dataset of an object-relational database interchangeably.
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Hamaz, Kamal, and Fouzia Benchikha. "A novel method for providing relational databases with rich semantics and natural language processing." Journal of Enterprise Information Management 30, no. 3 (2017): 503–25. http://dx.doi.org/10.1108/jeim-01-2015-0005.

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Purpose With the development of systems and applications, the number of users interacting with databases has increased considerably. The relational database model is still considered as the most used model for data storage and manipulation. However, it does not offer any semantic support for the stored data which can facilitate data access for the users. Indeed, a large number of users are intimidated when retrieving data because they are non-technical or have little technical knowledge. To overcome this problem, researchers are continuously developing new techniques for Natural Language Interfaces to Databases (NLIDB). Nowadays, the usage of existing NLIDBs is not widespread due to their deficiencies in understanding natural language (NL) queries. In this sense, the purpose of this paper is to propose a novel method for an intelligent understanding of NL queries using semantically enriched database sources. Design/methodology/approach First a reverse engineering process is applied to extract relational database hidden semantics. In the second step, the extracted semantics are enriched further using a domain ontology. After this, all semantics are stored in the same relational database. The phase of processing NL queries uses the stored semantics to generate a semantic tree. Findings The evaluation part of the work shows the advantages of using a semantically enriched database source to understand NL queries. Additionally, enriching a relational database has given more flexibility to understand contextual and synonymous words that may be used in a NL query. Originality/value Existing NLIDBs are not yet a standard option for interfacing a relational database due to their lack for understanding NL queries. Indeed, the techniques used in the literature have their limits. This paper handles those limits by identifying the NL elements by their semantic nature in order to generate a semantic tree. This last is a key solution towards an intelligent understanding of NL queries to relational databases.
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FONG, JOSEPH, HERBERT SHIU, and YIN FEI YEUNG. "CONCURRENT DATA MATERIALIZATION FOR XML-ENABLED DATABASE WITH SEMANTIC METADATA." International Journal of Software Engineering and Knowledge Engineering 20, no. 03 (2010): 377–422. http://dx.doi.org/10.1142/s0218194010004748.

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For a company with many databases in different data models, it is necessary to consolidate them into one interchangeable data model and present data in more than one data model concurrently to different users or individual users who need to access the data in more than one data model. The benefit is to let the user stick to his/her own data model to access database in another data model. This paper presents a semantic metadata to preserve database constraints for data materialization to support the user's view of database on an ad hoc basis. The semantic metadata can store the captured semantics of a relational or an XML-enabled database into classes. The stored constraints and data can be materialized into a target database upon user request. The user is allowed to perform data materialization many times alternatively. The process can provide a relational as well as an XML view to the users simultaneously. This concurrent data materialization function can be applied into data warehouse to consolidate heterogeneous database into a fact table in a data model of user's choice. Furthermore, a user can obtain either a relational view or an XML view of the same dataset of an XML-enabled database interchangeably.
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Lyngbaek, Peter, and Victor Vianu. "Mapping a semantic database model to the relational model." ACM SIGMOD Record 16, no. 3 (1987): 132–42. http://dx.doi.org/10.1145/38714.38732.

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Hirao, T. "Extension of the relational database semantic processing model." IBM Systems Journal 29, no. 4 (1990): 539–50. http://dx.doi.org/10.1147/sj.294.0539.

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Ram, Sudha. "Intelligent database design using the unifying semantic model." Information & Management 29, no. 4 (1995): 191–206. http://dx.doi.org/10.1016/0378-7206(95)00015-o.

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Jia, Hui, Guo Hua Geng, and Jin Xia Yang. "Research on Construction of Semantic Web of 3D Model Database." Advanced Materials Research 532-533 (June 2012): 1263–67. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1263.

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This paper presented a new method to construct semantic web of three-dimension model database based on ontology. Firstly we build ontology of three-dimension model database, according the model to extract classes, objects and attributes. Secondly utilize WordNet which is an English ontology to expand original ontology node to semantic extension node, including synonym, hypernym, hyponym and holonym. Experiment result shows that this method not only effectively expands the semantic vocabularies of a 3D model database, but also keeps good semantic relevance of the expanded vocabularies to the original ones, so as to achieve semantic based 3D model retrieval effectively.
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Bouaziz, Rafik, Salem Chakhar, Vincent Mousseau, Sudha Ram, and Abdelkader Telmoudi. "Database design and querying within the fuzzy semantic model." Information Sciences 177, no. 21 (2007): 4598–620. http://dx.doi.org/10.1016/j.ins.2007.05.013.

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Ma, Chuangtao, Bálint Molnár, and András Benczúr. "A Semi-Automatic Semantic Consistency-Checking Method for Learning Ontology from Relational Database." Information 12, no. 5 (2021): 188. http://dx.doi.org/10.3390/info12050188.

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To tackle the issues of semantic collision and inconsistencies between ontologies and the original data model while learning ontology from relational database (RDB), a semi-automatic semantic consistency checking method based on graph intermediate representation and model checking is presented. Initially, the W-Graph, as an intermediate model between databases and ontologies, was utilized to formalize the semantic correspondences between databases and ontologies, which were then transformed into the Kripke structure and eventually encoded with the SMV program. Meanwhile, description logics (DLs) were employed to formalize the semantic specifications of the learned ontologies, since the OWL DL showed good semantic compatibility and the DLs presented an excellent expressivity. Thereafter, the specifications were converted into a computer tree logic (CTL) formula to improve machine readability. Furthermore, the task of checking semantic consistency could be converted into a global model checking problem that could be solved automatically by the symbolic model checker. Moreover, an example is given to demonstrate the specific process of formalizing and checking the semantic consistency between learned ontologies and RDB, and a verification experiment was conducted to verify the feasibility of the presented method. The results showed that the presented method could correctly check and identify the different kinds of inconsistencies between learned ontologies and its original data model.
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Dissertations / Theses on the topic "Semantic Database Model"

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Lodico, Marc Richard. "SQLSDM : a SQL front-end semantic data model /." Online version of thesis, 1989. http://hdl.handle.net/1850/10620.

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Xie, Linchi. "The design of semantic database model SDBM." Thesis, University of British Columbia, 1987. http://hdl.handle.net/2429/26113.

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This thesis is mainly concerned with semantic data modelling related to database design. The domain of this research is restricted to general data modelling and the discussion is carried out at the conceptual level. The thesis assesses a number of serious modelling shortcomings of the conventional data models and reviews several basic principles and mechanisms developed in current semantic data modelling research. Based on these findings, the thesis identifies the inadequacy of the conceptualization of data modelling and develops the two-view conceptualization of data modelling. The basic idea behind the two-view conceptualization is that the conceptual structure of the applications being modelled should be separated from its external data representation. A new semantic database model, SDBM, is designed based on the conceptualization. The model makes a clear separation between the conceptual structure and its external data representation. It offers a data type mechanism to deal with the data representation, a window mechanism to model the conceptual structure, and a transaction mechanism to provide database operations. One of the major extensions of the current semantic data models is that with SDBM the specialization relationship is just a special case of constraints that can be specified among SDBM windows. A formal syntax and informal semantics of SDBM are given in the thesis along with comparisons between SDBM and a closely-related semantic data model, Taxis.<br>Business, Sauder School of<br>Graduate
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Watanabe, Toyohide, Yuusuke Uehara, Yuuji Yoshida, and Teruo Fukumura. "A semantic data model for intellectual database access." IEEE, 1990. http://hdl.handle.net/2237/6923.

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Morgan, Jac F. "The design of a small business database using the Semantic Database Model." Thesis, Kansas State University, 1985. http://hdl.handle.net/2097/9867.

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Al-Shawi, Abdalah. "Semantic and organization considerations in database conceptual modelling : the Semantic Conceptual Organizational Model (SECOM)." Thesis, Aston University, 1991. http://publications.aston.ac.uk/10666/.

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This thesis presents a new approach to designing large organizational databases. The approach emphasizes the need for a holistic approach to the design process. The development of the proposed approach was based on a comprehensive examination of the issues of relevance to the design and utilization of databases. Such issues include conceptual modelling, organization theory, and semantic theory. The conceptual modelling approach presented in this thesis is developed over three design stages, or model perspectives. In the semantic perspective, concept definitions were developed based on established semantic principles. Such definitions rely on meaning - provided by intension and extension - to determine intrinsic conceptual definitions. A tool, called meaning-based classification (MBC), is devised to classify concepts based on meaning. Concept classes are then integrated using concept definitions and a set of semantic relations which rely on concept content and form. In the application perspective, relationships are semantically defined according to the application environment. Relationship definitions include explicit relationship properties and constraints. The organization perspective introduces a new set of relations specifically developed to maintain conformity of conceptual abstractions with the nature of information abstractions implied by user requirements throughout the organization. Such relations are based on the stratification of work hierarchies, defined elsewhere in the thesis. Finally, an example of an application of the proposed approach is presented to illustrate the applicability and practicality of the modelling approach.
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Kolapalli, Pavani. "An analysis of structural and semantic validity in the data model." Click here for download, 2008. http://proquest.umi.com/pqdweb?did=1637583301&sid=1&Fmt=2&clientId=3260&RQT=309&VName=PQD.

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Hassan, Mansoor Ahmed. "View integration using the entity-relationship model." Thesis, Aston University, 1989. http://publications.aston.ac.uk/10641/.

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Tekli, Joe, Richard Chbeir, Agma J. M. Traina, et al. "Full-fledged semantic indexing and querying model designed for seamless integration in legacy RDBMS." Elsevier B.V, 2018. http://hdl.handle.net/10757/624626.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.<br>In the past decade, there has been an increasing need for semantic-aware data search and indexing in textual (structured and NoSQL) databases, as full-text search systems became available to non-experts where users have no knowledge about the data being searched and often formulate query keywords which are different from those used by the authors in indexing relevant documents, thus producing noisy and sometimes irrelevant results. In this paper, we address the problem of semantic-aware querying and provide a general framework for modeling and processing semantic-based keyword queries in textual databases, i.e., considering the lexical and semantic similarities/disparities when matching user query and data index terms. To do so, we design and construct a semantic-aware inverted index structure called SemIndex, extending the standard inverted index by constructing a tightly coupled inverted index graph that combines two main resources: a semantic network and a standard inverted index on a collection of textual data. We then provide a general keyword query model with specially tailored query processing algorithms built on top of SemIndex, in order to produce semantic-aware results, allowing the user to choose the results' semantic coverage and expressiveness based on her needs. To investigate the practicality and effectiveness of SemIndex, we discuss its physical design within a standard commercial RDBMS allowing to create, store, and query its graph structure, thus enabling the system to easily scale up and handle large volumes of data. We have conducted a battery of experiments to test the performance of SemIndex, evaluating its construction time, storage size, query processing time, and result quality, in comparison with legacy inverted index. Results highlight both the effectiveness and scalability of our approach.<br>This study is partly funded by the National Council for Scientific Research - Lebanon (CNRS-L), by the Lebanese American University (LAU), and the Research Support Foundation of the State of Sao Paulo ( FAPESP ). Appendix SemIndex Weighting Scheme We propose a set of weighting functions to assign weight scores to SemIndex entries, including: index nodes , index edges, data nodes , and data edges . The weighting functions are used to select and rank semantically relevant results w.r.t. the user's query (cf. SemIndex query processing in Section 5). Other weight functions could be later added to cater to the index designer's needs.<br>Revisión por pares
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Junior, Caetano Traina. "Máquina e modelo de dados dedicados para aplicações de engenharia." Universidade de São Paulo, 1986. http://www.teses.usp.br/teses/disponiveis/54/54132/tde-20082014-091445/.

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Esta tese envolve duas áreas de conhecimento, nomeadamente a de modelagem de dados para Sistemas de Gerecnciamento de Bases de Dados, e a de desenvolvimento de Máquinas de Bases de Dados. Devido a isso, esta tese apresenta-se também dividida em duas partes. Na primeira parte analisam-se os modelos já existentes e a partir das deficiências que apresentam para aplicações como Base de Dados para Engenharia, define-se o Modelo de Representação de Objetos. Na segunda parte são analisados arquiteturas de Máquinas de Base de Dados existentes e faz-se a proposta de uma nova arquitetura dedicada, para suportar uma implementação capaz de aproveitar o paralelismo que o modelo apresentado permite. Nas duas partes faz-se um levantamento de trabalhos relevantes que existem nas respectivas áreas, e mostra-se como as soluções apresentadas satisfazem as necessidades inerentes de cada parte<br>This work deals with two research areas: the data modeling for Database Management Systems; and the development of Database Machines. This work is divided in two parts. The first one analyzes already existent data models, and based on the characteristics required from engineering database applications, the Object Representation Model is defined. The second part analyzes existing database machines architectures and proposes a new one intended to support the intrinsic parallelism of the algorithms developed to implement the presented data model. A survey of relevant results obtained in both areas is included and a thorough discussion concludes the work
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Brown, Simon Ambrose. "The semantics of database query languages." Thesis, University of Sheffield, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310781.

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Books on the topic "Semantic Database Model"

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AlShawi, Abdalah. Semantic and organization considerations in database conceptual modelling: The SEmantic Conceptual Organizational Model (SECOM). Aston University. Department of Computing Science and Applied Mathematics, 1991.

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Wang, Y. Richard. An accounting model-based approach to semantic reconciliation in heterogeneous database systems. Sloan School of Management, Massachusetts Institute of Technology, 1990.

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Fausto, Giunchiglia, Tanca Letizia, and SpringerLink (Online service), eds. Semantic Web Information Management: A Model-Based Perspective. Springer-Verlag Berlin Heidelberg, 2010.

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G, Kulkarni Krishnarao, and Paton Norman W, eds. Object-oriented databases: A semantic data model approach. Prentice Hall, 1992.

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1938-, Kashyap Rangasami L., and Ghafoor Arif 1953-, eds. Semantic models for multimedia database searching and browsing. Kluwer Academic Publishers, 2000.

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Chen, Shu-Ching. Semantic models for multimedia database searching and browsing. Kluwer Academic Publishers, 2002.

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Data with semantics: Data models and data management. Van Nostrand Reinhold, 1989.

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Rios-Zertuche, Daniel. Semantic data models can be implemented efficienty: A performance analysis perspective. Computer Systems Research Institute, University of Toronto, 1988.

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A computational model of natural language communication: Interpretation, inference, and production in database semantics. Springer, 2006.

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Huajun, Chen, Kim Hong-Gee, Li Juanzi, et al., eds. The Semantic Web: Joint International Semantic Technology Conference, JIST 2011, Hangzhou, China, December 4-7, 2011. Proceedings. Springer Berlin Heidelberg, 2012.

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Book chapters on the topic "Semantic Database Model"

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Embley, David W. "Semantic Data Model." In Encyclopedia of Database Systems. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_105-2.

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Embley, David W. "Semantic Data Model." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_105.

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Embley, David W. "Semantic Data Model." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_105.

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Boll, Susanne, and Wolfgang Klas. "ZYX — A Semantic Model for Multimedia Documents and Presentations." In Database Semantics. Springer US, 1999. http://dx.doi.org/10.1007/978-0-387-35561-0_12.

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Viswanathan, V., and Ilango Krishnamurthi. "Ranking Semantic Associations between Two Entities – Extended Model." In Intelligent Information and Database Systems. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28493-9_17.

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Tari, Zahir. "Designing Object-Oriented Databases with a Semantic Data Model and a Rule Model." In Database and Expert Systems Applications. Springer Vienna, 1992. http://dx.doi.org/10.1007/978-3-7091-7557-6_19.

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Wojtkiewicz, Krystian, Marcin Jodłowiec, and Waldemar Pokuta. "Towards Association-Oriented Model of Lexical Base for Semantic Knowledge Base." In Intelligent Information and Database Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75420-8_3.

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Hu, Fei, Xiaofei Xu, Jingyuan Wang, Zhanbo Yang, and Li Li. "Memory-Enhanced Latent Semantic Model: Short Text Understanding for Sentiment Analysis." In Database Systems for Advanced Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55753-3_25.

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Heuer, Andreas. "A data model for complex objects based on a semantic database model and nested relations." In Nested Relations and Complex Objects in Databases. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/3-540-51171-7_33.

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Zhang, Fu, Z. M. Ma, Gaofeng Fan, and Xing Wang. "Automatic Fuzzy Semantic Web Ontology Learning from Fuzzy Object-Oriented Database Model." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15364-8_2.

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Conference papers on the topic "Semantic Database Model"

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Zorgati, Hela, Raoudha Ben Djemaa, Ikram Amous Ben Amor, and Florence Sedes. "QoC enhanced semantic IoT model." In IDEAS 2020: 24th International Database Engineering & Applications Symposium. ACM, 2020. http://dx.doi.org/10.1145/3410566.3410610.

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Lyngbaek, Peter, and Victor Vianu. "Mapping a semantic database model to the relational model." In the 1987 ACM SIGMOD international conference. ACM Press, 1987. http://dx.doi.org/10.1145/38713.38732.

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Watanabe, T., Y. Uehara, Y. Yoshida, and T. Fukumura. "A semantic data model for intellectual database access." In Ninth Annual International Phoenix Conference on Computers and Communications. 1990 Conference Proceedings. IEEE, 1990. http://dx.doi.org/10.1109/pccc.1990.101691.

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Rishe, Naphtali, and Qiang Li. "Application of Semantic Data Modeling to Science and Engineering." In ASME 1993 International Computers in Engineering Conference and Exposition. American Society of Mechanical Engineers, 1993. http://dx.doi.org/10.1115/cie1993-0084.

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Abstract This paper describes the application of semantic modeling using the Semantic Binary Database Model, SBM, to the design of databases having embedded measurement and scientific data, as well as complex interrelations between various objects. A case study of an environmental engineering database is considered.
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Hou, Jinkui. "Formal Semantic Model for Agent-Based Software System." In 2010 2nd International Workshop on Database Technology and Applications (DBTA). IEEE, 2010. http://dx.doi.org/10.1109/dbta.2010.5658949.

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Jagannathan, D., B. L. Fritchman, R. L. Guck, J. P. Thompson, and D. M. Tolbert. "SIM: a database system based on the semantic data model." In the 1988 ACM SIGMOD international conference. ACM Press, 1988. http://dx.doi.org/10.1145/50202.50207.

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perka, Svatopluk, and Pavel Smr. "Towards Adaptive and Semantic Database Model for RDF Data Stores." In 2012 Sixth International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS). IEEE, 2012. http://dx.doi.org/10.1109/cisis.2012.137.

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Wang, Xiaoyun, and Jianfeng Zhou. "An Improvement on the Model of Ontology-Based Semantic Similarity Computation." In 2009 First International Workshop on Database Technology and Applications, DBTA. IEEE, 2009. http://dx.doi.org/10.1109/dbta.2009.17.

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Lou, Kuiyang, Subramaniam Jayanti, Natraj Iyer, Yagnanarayanan Kalyanaraman, Sunil Prabhakar, and Karthik Ramani. "A Reconfigurable 3D Engineering Shape Search System: Part II — Database Indexing, Retrieval, and Clustering." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/cie-48188.

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This paper introduces database and related techniques for a reconfigurable, intelligent 3D engineering shape search system, which retrieves similar 3D models based on their shape content. Feature vectors, which are numeric “fingerprints” of 3D models, and skeletal graphs, which are the “minimal representations of the shape content” of a 3D model, represent the shape content. The Euclidean distance of the feature vectors, as well as the distance between skeletal graphs, provides indirect measures of shape similarity between the 3D models. Critical database issues regarding 3D shape search systems are discussed: (a) database indexing, (b) semantic gap, (c) subjectivity of similarity, and (d) database clustering. An Rtree based multidimensional index is used to speed up the feature-vector based search operation, while a decision treebased approach is used for efficiently indexing/searching skeletal graphs. Interactions among users and the search system, such as relevance feedback and feature vector reconfiguration, are used to bridge the semantic gap and to customize the system for different users. Database clustering of the R-tree index is compared with that generated by a selforganizing map (SOM). Synthetic databases and real 3D model databases are employed to investigate the efficiency of the multidimensional index and the effectiveness of relevance feedback.
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Danielson, Scott G. "Designing an Engineering Database for Telephone Networks." In ASME 1991 International Computers in Engineering Conference and Exposition. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/edm1991-0179.

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Abstract An engineering database modeling telephone outside plant networks is developed. Semantic and relational database design methodologies are used with the semantic data model developed based on an extended entity-relationship approach. This logical model is used to generate a normalized relational data structure. This database holds engineering data supporting engineering analyses, engineering work order generation procedures, and network planning activities. The database has been linked to separate network analysis programs and CAD-based network maps by a database application.
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