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Journal articles on the topic 'Semantic Database Model'

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Published: 4 June 2021

Last updated: 1 February 2022

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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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Matrone, F., E. Colucci, V. De Ruvo, A. Lingua, and A. Spanò. "HBIM IN A SEMANTIC 3D GIS DATABASE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W11 (May 5, 2019): 857–65. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w11-857-2019.

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Abstract. This work describes the different attempts and the consequent results derived from the integration of an HBIM model into an already structured spatial database (DB) and its 3D visualisation in a GIS project.This study is connected to the European ResCult (Increasing Resilience of Cultural Heritage) project where a DB for multiscale analyses was defined. To test the methodology proposed, the case study of Santa Maria dei Miracoli church in Venice was chosen since it represents a complex architectural heritage piece in a risk zone, it has been subject to a vast restoration intervention in the recent past but a digital documentation and model concerning it was missing.The 3D model of the church was structured in Revit as a HBIM, with the association of different kind of information and data related to the architectural elements by means of ‘shared parameters’ and ‘system families’. This procedure allows to reach an even higher Level of Detail (LOD4), but lead to some issues related to the semantic and software interoperability. To solve these problems the existing DB for the resilience of cultural heritage was extended adding a new entity representing the architectural elements designed in the BIM project.The aim of the test is to understand how the data and attributes inserted in the HBIM are converted and handled when dealing with a GIS DB, stepping from the IFC to the CityGML standard, through the FME software.

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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." ACM SIGMOD Record 17, no. 3 (1988): 46–55. http://dx.doi.org/10.1145/971701.50207.

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TRIGANO, PHILIPPE, JEAN-PAUL BARTHES, CATHERINE GREBOVAL, and FRANÇOIS VALLERNAUD. "QUERYING AN OBJECT-ORIENTED DATABASE USING FREE LANGUAGE." International Journal of Pattern Recognition and Artificial Intelligence 04, no. 03 (1990): 429–45. http://dx.doi.org/10.1142/s0218001490000265.

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A man-machine interface in natural language for a DataBase Management System (DBMS) called VORAS, based upon semantics, is presented. This DBMS can also be used for knowledge representation, and is well-suited to the design of queries in natural language. The system VORAS is an object-oriented DBMS developed from a specific model of representation called the Property Driven Model (PDM). A user may write a query in natural language. Most of the analysis is done at a semantic level. A syntactic level has been added, to improve the performance of the system. However, it is still possible to use a short-hand style.

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SCHIESSL, Marcelo, and Marisa BRÄSCHER. "Ontology lexicalization: Relationship between content and meaning in the context of Information Retrieval." Transinformação 29, no. 1 (2017): 57–72. http://dx.doi.org/10.1590/2318-08892017000100006.

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Abstract The proposal presented in this study seeks to properly represent natural language to ontologies and vice-versa. Therefore, the semi-automatic creation of a lexical database in Brazilian Portuguese containing morphological, syntactic, and semantic information that can be read by machines was proposed, allowing the link between structured and unstructured data and its integration into an information retrieval model to improve precision. The results obtained demonstrated that the methodology can be used in the risco financeiro (financial risk) domain in Portuguese for the construction of an ontology and the lexical-semantic database and the proposal of a semantic information retrieval model. In order to evaluate the performance of the proposed model, documents containing the main definitions of the financial risk domain were selected and indexed with and without semantic annotation. To enable the comparison between the approaches, two databases were created based on the texts with the semantic annotations to represent the semantic search. The first one represents the traditional search and the second contained the index built based on the texts with the semantic annotations to represent the semantic search. The evaluation of the proposal was based on recall and precision. The queries submitted to the model showed that the semantic search outperforms the traditional search and validates the methodology used. Although more complex, the procedure proposed can be used in all kinds of domains.

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Poux, F., R. Neuville, P. Hallot, and R. Billen. "MODEL FOR SEMANTICALLY RICH POINT CLOUD DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W5 (October 23, 2017): 107–15. http://dx.doi.org/10.5194/isprs-annals-iv-4-w5-107-2017.

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This paper proposes an interoperable model for managing high dimensional point clouds while integrating semantics. Point clouds from sensors are a direct source of information physically describing a 3D state of the recorded environment. As such, they are an exhaustive representation of the real world at every scale: 3D reality-based spatial data. Their generation is increasingly fast but processing routines and data models lack of knowledge to reason from information extraction rather than interpretation. The enhanced smart point cloud developed model allows to bring intelligence to point clouds via 3 connected meta-models while linking available knowledge and classification procedures that permits semantic injection. Interoperability drives the model adaptation to potentially many applications through specialized domain ontologies. A first prototype is implemented in Python and PostgreSQL database and allows to combine semantic and spatial concepts for basic hybrid queries on different point clouds.

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PAN, MIIN-JENG, SHI-KUO CHANG, and CHIEN-CHIAO YANG. "A TWO-LEVEL METADATA DICTIONARY APPROACH FOR SEMANTIC QUERY PROCESSING IN MULTIDATABASE SYSTEMS." International Journal of Software Engineering and Knowledge Engineering 03, no. 02 (1993): 231–55. http://dx.doi.org/10.1142/s0218194093000112.

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A multidatabase system (MDBS) is a system that integrates several autonomous database systems and provides users with a uniform access to all the databases. In this paper we developed a two-level active metadata dictionary approach for semantic query processing. To capture the global view of data schemas of participating databases which may be heterogeneous, a Hornclause data model is used. The lower-level metadata dictionaries (LLMDs) keep metadata for each corresponding local database in MDBS. The higher-level metadata dictionary (HLMD) integrates the metadata about all LLMDs. The database integration strategy includes two phases: schema translation and schema integration. It is a bottom-up approach integrating schema from the underlying database schemas. The evaluation strategy is a top-down approach. It starts with a query as a global goal to be achieved, unifies and optimizes the query to decompose the goal into subgoals that can be evaluated against extensional database, then translates these subgoals into corresponding queries against underlying DBMSs. To solve the control problem, we employ a G-net model for procedure control and inference control. An experimental implementation in Prolog is described.

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Zhou, Hong Gen, Wen Cheng Tang, Xu Wen Jing, and Xiao Jun Zhang. "The Research and Realization of the Product Design and Process Decision System Based on the Semantic Technology." Applied Mechanics and Materials 52-54 (March 2011): 633–38. http://dx.doi.org/10.4028/www.scientific.net/amm.52-54.633.

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After several decades of research, the traditional modeling methods, such as geometric model，feature model and integrated product model, can’t fully meet the integrated product information and the related knowledge that required. Meanwhile, lack of semantics, is not conducive to the using and sharing of knowledge. Semantic-based product design and process decision can shorten the product development cycle, reduce costs and allow the computer to participate in the design. Based on building of complex electronic products ontology and already been built OWL ontology file introduced into database, this paper combines Ontology model of complex electronic products with the semantic Web, studies a network design model based on the semantic Web and develops a product design and process decision Web platform based on semantic. The platform can realize product design, semantic search of the resources and decision of parts machining process, thus, to improve the productivity and enhance the efficiency and competitiveness of the company by the system.

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Kang, Nan Nan, Xiao Fang Wang, and Rong Rong Zhang. "Image Classification Based on Color Topic Model." Applied Mechanics and Materials 556-562 (May 2014): 4770–73. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4770.

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This paper addresses semantic image classification with topic model, which focusing on discovering a hidden semantic to solve the semantic gap between low-level visual feature and high-level feature. In our approach, Latent Dirichlet Allocation (LDA) model successfully reflect the high level features and the RGB SIFT features which integrating the Scale-invariant feature transform (SIFT) features with color features on the assumption that pictures generated by mixture of latent semantic which we called topics. The proposed approach has a sufficient theoretical basis and the experimental evaluations the COREL database demonstrate its promise of the effectiveness.

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Guo, Hui Ling. "Research on the Model of Database Intrusion Protection System Based on E-Commerce Platform." Applied Mechanics and Materials 336-338 (July 2013): 2559–62. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.2559.

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Based on attack model of database,a model of database intrusion prevention system is proposed in electronic commerce platform. The model is divided into session level intrusion detection model, schema level intrusion detection model and semantic level intrusion detection model according to the abstraction level of test information. It extends the COAST firewall model with intrusion detection, and a layered intrusion prevention model which detect intrusion behavior according to session level, schema level and semantic level information of transactions. Thereby, it updates database security from passive protection to proactive protection.

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Li, Qing, Na Li, Liping Wang, and Xiaoping Sun. "A new semantic model with applications in a multimedia database system." Concurrency and Computation: Practice and Experience 21, no. 5 (2009): 691–704. http://dx.doi.org/10.1002/cpe.1373.

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Hou, Suyu, Kuiyang Lou, and Karthik Ramani. "SVM-based Semantic Clustering and Retrieval of a 3D Model Database." Computer-Aided Design and Applications 2, no. 1-4 (2005): 155–64. http://dx.doi.org/10.1080/16864360.2005.10738363.

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Liu, Jun Qiang, and Xiao Ling Guan. "Semantic Data Management in PDM System Based on Object Deputy Model." Applied Mechanics and Materials 48-49 (February 2011): 1024–27. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.1024.

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Semantic data management is a key issue in PDM (product data management) system. Traditional PDM system is not suitable for complex semantic data management. Moreover, most of the PDM systems cannot provide efficient semantic search over database. In this paper, we present a data warehouse which adopts the object deputy model to store semantic product data. Most important of all, we can provide more convenient and efficient semantic search for users. The experiment results show that our approach is more feasible and efficient than the traditional one.

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Garcia-Ortiz, Luis Brandon, Jose Portillo-Portillo, Aldo Hernandez-Suarez, et al. "FASSD-Net Model for Person Semantic Segmentation." Electronics 10, no. 12 (2021): 1393. http://dx.doi.org/10.3390/electronics10121393.

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This paper proposes the use of the FASSD-Net model for semantic segmentation of human silhouettes, these silhouettes can later be used in various applications that require specific characteristics of human interaction observed in video sequences for the understanding of human activities or for human identification. These applications are classified as high-level task semantic understanding. Since semantic segmentation is presented as one solution for human silhouette extraction, it is concluded that convolutional neural networks (CNN) have a clear advantage over traditional methods for computer vision, based on their ability to learn the representations of appropriate characteristics for the task of segmentation. In this work, the FASSD-Net model is used as a novel proposal that promises real-time segmentation in high-resolution images exceeding 20 FPS. To evaluate the proposed scheme, we use the Cityscapes database, which consists of sundry scenarios that represent human interaction with its environment (these scenarios show the semantic segmentation of people, difficult to solve, that favors the evaluation of our proposal), To adapt the FASSD-Net model to human silhouette semantic segmentation, the indexes of the 19 classes traditionally proposed for Cityscapes were modified, leaving only two labels: One for the class of interest labeled as person and one for the background. The Cityscapes database includes the category “human” composed for “rider” and “person” classes, in which the rider class contains incomplete human silhouettes due to self-occlusions for the activity or transport used. For this reason, we only train the model using the person class rather than human category. The implementation of the FASSD-Net model with only two classes shows promising results in both a qualitative and quantitative manner for the segmentation of human silhouettes.

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BAGAI, RAJIV, and RAJSHEKHAR SUNDERRAMAN. "COMPUTING THE WELL-FOUNDED MODEL OF DEDUCTIVE DATABASES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 04, no. 02 (1996): 157–75. http://dx.doi.org/10.1142/s021848859600010x.

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The well-founded model is one of the most popular models of general logic programs, i.e. logic programs with negation in the bodies of clauses. We present a method for constructing this model for general deductive databases, which are logic programs without any function symbols. The method adopts paraconsistent relations as the semantic objects associated with the predicate symbols of the database. Paraconsistent relations are a generalization of ordinary relations in that they allow manipulation of incomplete as well as inconsistent information. The first step in the model construction method is to transform the database clauses into paraconsistent relation definitions involving these operators. The second step is to build the well-founded model iteratively. Algorithms for both steps are presented and their termination and correctness is also established.

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Jean, Stéphane, Yamine Aït-Ameur, and Guy Pierra. "OntoQL: An Alternative to Semantic Web Query Languages." International Journal of Semantic Computing 09, no. 01 (2015): 105–37. http://dx.doi.org/10.1142/s1793351x1550004x.

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Ontologies are used in several application domains for representing knowledge. The defined approaches differ according to the type of addressed ontology (conceptual or linguistic) and to the used ontology model (e.g. OWL or PLIB). Several languages have been proposed to manipulate ontologies and their instances, especially in the Semantic Web domain. However these languages are often specific to a given ontology model, they focus on conceptual ontologies and they are not compatible with database exploitation languages. We address these three problems in this paper by proposing the OntoQL language. This language has three main original characteristics: (1) OntoQL is based on a core ontology model composed of the shared constructors of ontology models. This core ontology model can be extended by the language itself, (2) OntoQL queries can be expressed with different natural languages features using the linguistic layer of an ontology, and (3) OntoQL is fully compatible with SQL enabling a smooth integration between SQL queries of classical database applications and ontological queries. As a theoretical validation of this language, we present the algebra of operators that sets up its formal semantics. On the operational side, we describe the implementation of OntoQL on the OntoDB database and we illustrate the interest of this language by reporting several applications where this language has been extensively used and proved powerful.

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Liu, Yongmei, Tanakrit Wongwitit, and Linsen Yu. "Automatic Image Annotation Based on Scene Analysis." International Journal of Image and Graphics 14, no. 03 (2014): 1450012. http://dx.doi.org/10.1142/s0219467814500120.

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Automatic image annotation is an important and challenging job for image analysis and understanding such as content-based image retrieval (CBIR). The relationship between the keywords and visual features is too complicated due to the semantic gap. We present an approach of automatic image annotation based on scene analysis. With the constrain of scene semantics, the correlation between keywords and visual features becomes simpler and clearer. Our model has two stages of process. The first stage is training process which groups training image data set into semantic scenes using the extracted semantic feature and visual scenes constructed from the calculation distances of visual features for every pairs of training images by using Earth mover's distance (EMD). Then, combine a pair of semantic and visual scene together and apply Gaussian mixture model (GMM) for all scenes. The second stage is to test and annotate keywords for test image data set. Using the visual features provided by Duygulu, experimental results show that our model outperforms probabilistic latent semantic analysis (PLSA) & GMM (PLSA&GMM) model on Corel5K database.

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Bouougada, Benamar, Djelloul Bouchiha, Redha Rebhi, et al. "Mapping Relational Database to OWL Ontology Based on MDE Settings." Revue d'Intelligence Artificielle 35, no. 3 (2021): 217–22. http://dx.doi.org/10.18280/ria.350305.

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Ontology is an important aspect of the semantic web, which is why semantic web developers are interested in constructing ontology in various applications based on domain experts. By transforming an existing application database into ontology, we many construct ontologies without having to hire an expert in the field. Model-driven engineering is the foundation of the suggested strategy (MDE). In a nutshell, the technique is divided into two phases, the first of which attempts to prepare the data needed for the transformation in the form of a model with a database. A compliance relationship between this model and its meta-model is required. Phase (2) applies a set of rules written in the Atlas Transformational Language to change the model produced in the first phase into another model, which is an OWL ontology (ATL). We tested our solution using a set of databases created specifically for this purpose and built it in an eclipse environment using an EMF and ATL transform language. The acquired findings demonstrate the strength and efficacy of the recommended strategy.

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Liang, Percy, Michael I. Jordan, and Dan Klein. "Learning Dependency-Based Compositional Semantics." Computational Linguistics 39, no. 2 (2013): 389–446. http://dx.doi.org/10.1162/coli_a_00127.

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Suppose we want to build a system that answers a natural language question by representing its semantics as a logical forxm and computing the answer given a structured database of facts. The core part of such a system is the semantic parser that maps questions to logical forms. Semantic parsers are typically trained from examples of questions annotated with their target logical forms, but this type of annotation is expensive. Our goal is to instead learn a semantic parser from question–answer pairs, where the logical form is modeled as a latent variable. We develop a new semantic formalism, dependency-based compositional semantics (DCS) and define a log-linear distribution over DCS logical forms. The model parameters are estimated using a simple procedure that alternates between beam search and numerical optimization. On two standard semantic parsing benchmarks, we show that our system obtains comparable accuracies to even state-of-the-art systems that do require annotated logical forms.

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Zhang, Z., S. Zhao, X. Li, D. Cong, and D. Sun. "AN INFORMATION SERVICE MODEL FOR REMOTE SENSING EMERGENCY SERVICES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 12, 2017): 187–91. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-187-2017.

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This paper presents a method on the semantic access environment, which can solve the problem about how to identify the correct natural disaster emergency knowledge and return to the demanders. The study data is natural disaster knowledge text set. Firstly, based on the remote sensing emergency knowledge database, we utilize the sematic network to extract the key words in the input documents dataset. Then, using the semantic analysis based on words segmentation and PLSA, to establish the sematic access environment to identify the requirement of users and match the emergency knowledge in the database. Finally, the user preference model was established, which could help the system to return the corresponding information to the different users. The results indicate that semantic analysis can dispose the natural disaster knowledge effectively, which will realize diversified information service, enhance the precision of information retrieval and satisfy the requirement of users.

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Hor, A. H., G. Sohn, P. Claudio, M. Jadidi, and A. Afnan. "A SEMANTIC GRAPH DATABASE FOR BIM-GIS INTEGRATED INFORMATION MODEL FOR AN INTELLIGENT URBAN MOBILITY WEB APPLICATION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4 (September 19, 2018): 89–96. http://dx.doi.org/10.5194/isprs-annals-iv-4-89-2018.

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Abstract. Over the recent years, the usage of semantic web technologies and Resources Description Framework (RDF) data models have been notably increased in many fields. Multiple systems are using RDF data to describe information resources and semantic associations. RDF data plays a very important role in advanced information retrieval, and graphs are efficient ways to visualize and represent real world data by providing solutions to many real-time scenarios that can be simulated and implemented using graph databases, and efficiently query graphs with multiple attributes representing different domains of knowledge. Given that graph databases are schema less with efficient storage for semi-structured data, they can provide fast and deep traversals instead of slow RDBMS SQL based joins allowing Atomicity, Consistency, Isolation and durability (ACID) transactions with rollback support, and by utilizing mathematics of graph they can enormous potential for fast data extraction and storage of information in the form of nodes and relationships. In this paper, we are presenting an architectural design with complete implementation of BIM-GIS integrated RDF graph database. The proposed integration approach is composed of four main phases: ontological BIM and GIS model’s construction, mapping and semantic integration using interoperable data formats, then an import into a graph database with querying and filtering capabilities. The workflows and transformations of IFC and CityGML schemas into object graph databases model are developed and applied to an intelligent urban mobility web application on a game engine platform validate the integration methodology.

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Davydov, Oleg Izmailovich, and Alexandr Konstantinovich Platonov. "Database for semantic model of the operating environment of mobile service robot." Keldysh Institute Preprints, no. 7 (2017): 1–24. http://dx.doi.org/10.20948/prepr-2017-7.

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Mann, G., and R. Haux. "Database scheme design for clinical studies based on a semantic data model." Computational Statistics & Data Analysis 15, no. 1 (1993): 81–108. http://dx.doi.org/10.1016/0167-9473(93)90220-n.

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Sorokine, A., and R. N. Stewart. "SPATIO-TEMPORAL DATA MODEL FOR INTEGRATING EVOLVING NATION-LEVEL DATASETS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W2 (October 19, 2017): 69–76. http://dx.doi.org/10.5194/isprs-annals-iv-4-w2-69-2017.

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Ability to easily combine the data from diverse sources in a single analytical workflow is one of the greatest promises of the Big Data technologies. However, such integration is often challenging as datasets originate from different vendors, governments, and research communities that results in multiple incompatibilities including data representations, formats, and semantics. Semantics differences are hardest to handle: different communities often use different attribute definitions and associate the records with different sets of evolving geographic entities. Analysis of global socioeconomic variables across multiple datasets over prolonged time is often complicated by the difference in how boundaries and histories of countries or other geographic entities are represented. Here we propose an event-based data model for depicting and tracking histories of evolving geographic units (countries, provinces, etc.) and their representations in disparate data. The model addresses the semantic challenge of preserving identity of geographic entities over time by defining criteria for the entity existence, a set of events that may affect its existence, and rules for mapping between different representations (datasets). Proposed model is used for maintaining an evolving compound database of global socioeconomic and environmental data harvested from multiple sources. Practical implementation of our model is demonstrated using PostgreSQL object-relational database with the use of temporal, geospatial, and NoSQL database extensions.

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GIUNCHIGLIA, FAUSTO, and PAVEL SHVAIKO. "Semantic matching." Knowledge Engineering Review 18, no. 3 (2003): 265–80. http://dx.doi.org/10.1017/s0269888904000074.

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We think of match as an operator that takes two graph-like structures (e.g. database schemas or ontologies) and produces a mapping between elements of the two graphs that correspond semantically to each other. The goal of this paper is to propose a new approach to matching, called semantic matching. As its name indicates, in semantic matching the key intuition is to exploit the model-theoretic information, which is codified in the nodes and the structure of graphs. The contributions of this paper are (i) a rational reconstruction of the major matching problems and their articulation in terms of the more generic problem of matching graphs, (ii) the identification of semantic matching as a new approach for performing generic matching and (iii) a proposal for implementing semantic matching by testing propositional satisfiability.

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Li, W., S. Zlatanova, J. J. Yan, A. Diakite, and M. Aleksandrov. "A GEO-DATABASE SOLUTION FOR THE MANAGEMENT AND ANALYSIS OF BUILDING MODEL WITH MULTI-SOURCE DATA FUSION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W20 (November 15, 2019): 55–63. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w20-55-2019.

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Abstract. Over the last decades, building models have become valuable for a multitude of application scenarios, such as visualisation, simulations and decision support. As the growth of multi-source data consisting of semantic and 2D/3D spatial information, data management becomes feasibility means for facilitating the development and deployment of building model applications. In addition, most studies focus on modelling buildings at geometric level, while semantic analysis can become a promising approach to get a better understanding of built environment. How to utilize multi-source data in a joint manner to further express the building model, therefore, is an emerging challenge. In this paper, we develop a semantic 3D building model based on complex multi-source data. Then, we tackle data management and analysis problems in a geo-database solution for our unified building model. Performance studies on the University of New South Wales (UNSW) campus demonstrate the efficiency of our solution.

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Adeyelu, A. A., and E. O. Anyebe. "Implementing an Improved Mediator Wrapper Paradigm for Heterogeneous Database Integration." NIGERIAN ANNALS OF PURE AND APPLIED SCIENCES 1 (March 14, 2019): 224–35. http://dx.doi.org/10.46912/napas.29.

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This study developed and implemented an improved mediator wrapper approach to addressing the challenges of integration of semantic heterogeneous databases. It employed Local as View (LaV) paradigm of database integration so as to reduce its cost as well as offer the local sources a degree of independence. The developed model was implemented as web service using JEE and some software development tools with a number of heterogeneous databases as a case study.

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Vidakovic, Jovana, Sonja Ristic, Slavica Kordic, and Ivan Lukovic. "Extended tuple constraint type as a complex integrity constraint type in XML data model - definition and enforcement." Computer Science and Information Systems 15, no. 3 (2018): 821–43. http://dx.doi.org/10.2298/csis180324029v.

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A database management system (DBMS) is based on a data model whose concepts are used to express a database schema. Each data model has a specific set of integrity constraint types. There are integrity constraint types, such as key constraint, unique constraint and foreign key constraint that are supported by most DBMSs. Other, more complex constraint types are difficult to express and enforce and are mostly completely disregarded by actual DBMSs. The users have to manage those using custom procedures or triggers. eXtended Markup Language (XML) has become the universal format for representing and exchanging data. Very often XML data are generated from relational databases and exported to a target application or another database. In this context, integrity constraints play the essential role in preserving the original semantics of data. Integrity constraints have been extensively studied in the relational data model. Mechanisms provided by XML schema languages rely on a simple form of constraints that is sufficient neither for expressing semantic constraints commonly found in databases nor for expressing more complex constraints induced by the business rules of the system under study. In this paper we present a classification of constraint types in relational data model, discuss possible declarative mechanisms for their specification and enforcement in the XML data model, and illustrate our approach to the definition and enforcement of complex constraint types in the XML data model on the example of extended tuple constraint type.

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Mironov, Valeriy, Artem Gusarenko, and Gayz Tuguzbaev. "Extracting Semantic Information from Graphic Schemes." Informatics and Automation 20, no. 4 (2021): 940–70. http://dx.doi.org/10.15622/ia.20.4.7.

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The problem of extracting semantic information from an electronic document specified in the vector graphics format and containing a graphic model (diagram) built using a graphic editor is considered. The problem is to program retrieving certain structural properties and parametric circuit and entering them into a database for later use. Based on the analysis of the capabilities of graphic editors, a conclusion has made about the relevance of this task for universal editors that are not tied to specific graphic notations and use open graphic document formats, which allows program processing. The proposed approach considers graphic documents at three levels of abstraction: conceptual (semantic properties of a schema), logical (presentation of semantic properties at the internal level of the document) and physical (internal organization of a graphic document). The solution to the problem is based on the construction of a conceptual-logical mapping, i.e., mapping a conceptual model of a circuit to a logical model of a graphic document, according to its physical model. Within the framework of the approach, an algorithm for constructing the indicated mapping is developed, presented in the form of an object-oriented pseudocode. The study of internal markup in open graphic formats made it possible to build models for identifying circuit elements and their connections to each other, which is necessary for a specific application of the algorithm. Expressions for addressing schema elements and accessing their properties are obtained. The proposed approach is implemented on the base of a situation-oriented paradigm, within which the extraction process is driven by a hierarchical situational model. The processed data is specified in the situational model in the form of virtual documents displayed on heterogeneous external data sources. For the problem being solved, we consider the mapping to two variants of vector graphics formats: to a "flat" markup file and to a set of such files in an electronic archive. The practical use of the results is illustrated by the example of extracting semantic information from graphical models developed at various stages of database design.

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CHUBB, CHARLIE, YOSHI INAGAKI, C. COTMAN, B. CUMMINGS, and P. C. Y. SHEU. "SEMANTIC BIOLOGICAL IMAGE MANAGEMENT AND ANALYSIS." International Journal on Artificial Intelligence Tools 13, no. 04 (2004): 881–96. http://dx.doi.org/10.1142/s0218213004001879.

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Most commercially available image retrieval systems are so generic that they are not specialized to handle biological images and the feature domains associated with them. In molecular biology, neurobiology and cellular biology, for example, the recognition, classification and retrieval of distinct cellular features is a critically needed tool representing a computational problem that embodies the central challenges facing biological image database research. It often requires the consideration of expert/conceptual knowledge of images and the objects contained within such images. This paper discusses the feasibility of developing a set of imaging algorithms that allows the user to train an imaging system/database to recognize biological objects of various sorts based on their own criteria. The image software builds a model of the selected objects by reiterative training, evolving the ability (i.e., the underlying rules) to recognize these objects. These objects are in turn archived into a growing database that builds upon the experience of multiple individuals that can be referred to as an object zoo. The user can search these zoos using newly acquired images based on similarly using more narrow or broadened criteria based on a new, semantic database framework called SemanticObjects.

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Kaladzavi, Guidedi, Papa Fary Diallo, Cedric Bere, et al. "Ontologies-based architecture for sociocultural knowledge co-construction systems." Online Journal of Applied Knowledge Management 6, no. 1 (2018): 226–39. http://dx.doi.org/10.36965/ojakm.2018.6(1)226-239.

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Considering the evolution of the semantic wiki engine on-based platforms, two main approaches could be distinguished: Ontologies for Wikis (OfW) and Wikis for Ontologies (WfO). OfW vision requires the existing ontologies to be imported. Most of them use the Resource Description Framework (RDF-based) systems in conjunction with the standard Structured Query Language (SQL) database to manage and query semantic data. But, relational database is not an ideal type of storage for semantic data. A more natural data model for Semantic MediaWiki (SMW) is RDF, a data format that organizes information in graphs rather than in fixed database tables. This paper presents an ontology on-based architecture, which aims to implement this idea. The Architecture mainly includes three-layered functional architecture: Web User Interface Layer, Semantic Layer, and Persistence Layer.

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Shehab, Omar, and Ali Hussein Saleh Zolait. "Semantic Search Engine and Object Database Guidelines for Service Oriented Architecture Models." International Journal of Technology Diffusion 2, no. 3 (2011): 47–71. http://dx.doi.org/10.4018/jtd.2011070105.

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In this paper, the authors propose a Semantic Search Engine, which retrieves software components precisely and uses techniques to store these components in a database, such as ontology technology. The engine uses semantic query language to retrieve these components semantically. The authors use an exploratory study where the proposed method is mapped between object-oriented concepts and web ontology language. A qualitative survey and interview techniques were used to collect data. The findings after implementing this research are a set of guidelines, a model, and a prototype to describe the semantic search engine system. The guidelines provided help software developers and companies reduce the cost, time, and risks of software development.

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Lin, Shi, Xu Zhenguo, and Meng Xiangzeng. "Emotional Semantic Recognition of Visual Scene in Flash Animation." Journal of Control Science and Engineering 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/3768741.

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Based on the organization structure of the Flash animation files, we first use the edge density method to segment the Flash animation to obtain the visual scenes, then extract the visual features such as color and texture as the input parameters of BP neural network, and set up the sample database. Secondly, we choose a suitable model for emotion classification, use eight kinds of emotional adjectives to describe the emotion of Flash animation, such as warm, delightful, exaggerated, funny, desolate, dreary, complex, and illusory, and mark the emotion value of the visual scene in the sample database and so use it as the output parameter of the BP neural network. Finally, we use BP neural network with appropriate transfer function and learning function for training to obtain the rules for mapping from visual features of the visual scene to semantic space and, at last, complete the automatic classification work of emotional semantic of the visual scene. We used the algorithm to carry on the emotional semantics recognition to 5012 visual scenes, and the experiment effect is good. The results of our study can be used in the classification, retrieval, and other fields of Flash animation based on emotional semantics.

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Mao, B., L. Harrie, J. Cao, Z. Wu, and J. Shen. "NoSQL Based 3D City Model Management System." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-4 (April 23, 2014): 169–73. http://dx.doi.org/10.5194/isprsarchives-xl-4-169-2014.

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To manage increasingly complicated 3D city models, a framework based on NoSQL database is proposed in this paper. The framework supports import and export of 3D city model according to international standards such as CityGML, KML/COLLADA and X3D. We also suggest and implement 3D model analysis and visualization in the framework. For city model analysis, 3D geometry data and semantic information (such as name, height, area, price and so on) are stored and processed separately. We use a Map-Reduce method to deal with the 3D geometry data since it is more complex, while the semantic analysis is mainly based on database query operation. For visualization, a multiple 3D city representation structure CityTree is implemented within the framework to support dynamic LODs based on user viewpoint. Also, the proposed framework is easily extensible and supports geoindexes to speed up the querying. Our experimental results show that the proposed 3D city management system can efficiently fulfil the analysis and visualization requirements.

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Wysocki, O., B. Schwab, L. Hoegner, T. H. Kolbe, and U. Stilla. "PLASTIC SURGERY FOR 3D CITY MODELS: A PIPELINE FOR AUTOMATIC GEOMETRY REFINEMENT AND SEMANTIC ENRICHMENT." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-4-2021 (June 17, 2021): 17–24. http://dx.doi.org/10.5194/isprs-annals-v-4-2021-17-2021.

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Abstract. Nowadays, the number of connected devices providing unstructured data is rapidly rising. These devices acquire data with a temporal and spatial resolution at an unprecedented level creating an influx of geoinformation which, however, lacks semantic information. Simultaneously, structured datasets like semantic 3D city models are widely available and assure rich semantics and high global accuracy but are represented by rather coarse geometries. While the mentioned downsides curb the usability of these data types for nowadays’ applications, the fusion of both shall maximize their potential. Since testing and developing automated driving functions stands at the forefront of the challenges, we propose a pipeline fusing structured (CityGML and HD Map datasets) and unstructured datasets (MLS point clouds) to maximize their advantages in the automatic 3D road space models reconstruction domain. The pipeline is a parameterized end-to-end solution that integrates segmentation, reconstruction, and modeling tasks while ensuring geometric and semantic validity of models. Firstly, the segmentation of point clouds is supported by the transfer of semantics from a structured to an unstructured dataset. The distinction between horizontal- and vertical-like point cloud subsets enforces a further segmentation or an immediate refinement while only adequately depicted models by point clouds are allowed. Then, based on the classified and filtered point clouds the input 3D model geometries are refined. Building upon the refinement, the semantic enrichment of the 3D models is presented. The deployment of a simulation engine for automated driving research and a city model database tool underlines the versatility of possible application areas.

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BINDER, ROBERT V., and JEFFREY J. P. TSAI. "KB/RMS: AN INTELLIGENT ASSISTANT FOR REQUIREMENT DEFINITION." International Journal on Artificial Intelligence Tools 01, no. 04 (1992): 503–22. http://dx.doi.org/10.1142/s021821309200003x.

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In this paper, we present a conceptual framework and a system model for an intelligent assistant for requirement definition, KB/RMS. The requirement definition process is characterized by the Requirement Context Model. Informal and formal methods for requirement definition are considered in the light of this model, which serves as the logical schema for the KB/RMS database. We summarize conventional and knowledge-based system support for requirement definition. The use of natural language processing, a semantic model of the problem and solution spaces, domain and technology models, inference driven augmentation, validation, and verification of the semantic model is discussed. Finally, we present the production of design representations from the augmented semantic model.

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MOGHADDAM, Azadeh Sharifi. "A Semantic Change Model for French Loanwords in Persian." Acta Linguistica Asiatica 3, no. 2 (2013): 73–88. http://dx.doi.org/10.4312/ala.3.2.73-88.

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The aim of this study is to propose a model of semantic change which corresponds to synchronic analysis of the data set. For this purpose, the study examined semantic change orientation of French loanwords in Persian. Zomorrodian’s (1373/1994) book on European loanwords in Persian was selected as the database from which 1878 French loanwords were extracted. The data was analyzed semantically by consulting Bellows and Bellows’ (1919) dictionary in order to compare meanings of the original lexical items against the present meaning of the loanwords. A number of 330 loaned items were found to have undergone a type of semantic change. The corpus was examined against the existing semantic change typologies. Finding these models to be incomplete, a new model was proposed. This model, being synchronic and non-linear, is of a more elaborate design with the following advantages: a) all items (loanwords) of the corpus could be contained in it, and b) all categories of this model were filled up by loaned items.

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Wieting, John, Mohit Bansal, Kevin Gimpel, and Karen Livescu. "From Paraphrase Database to Compositional Paraphrase Model and Back." Transactions of the Association for Computational Linguistics 3 (December 2015): 345–58. http://dx.doi.org/10.1162/tacl_a_00143.

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The Paraphrase Database (PPDB; Ganitkevitch et al., 2013) is an extensive semantic resource, consisting of a list of phrase pairs with (heuristic) confidence estimates. However, it is still unclear how it can best be used, due to the heuristic nature of the confidences and its necessarily incomplete coverage. We propose models to leverage the phrase pairs from the PPDB to build parametric paraphrase models that score paraphrase pairs more accurately than the PPDB’s internal scores while simultaneously improving its coverage. They allow for learning phrase embeddings as well as improved word embeddings. Moreover, we introduce two new, manually annotated datasets to evaluate short-phrase paraphrasing models. Using our paraphrase model trained using PPDB, we achieve state-of-the-art results on standard word and bigram similarity tasks and beat strong baselines on our new short phrase paraphrase tasks.

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Avrunin, Oleg, Oleksiy Vlasov, and Valentin Filatov. "MODEL OF SEMANTIC INTEGRATION OF INFORMATION SYSTEMS PROPERTIES IN RELAY DATABASE REENGINEERING PROBLEMS." Innovative Technologies and Scientific Solutions for Industries, no. 4 (14) (December 21, 2020): 5–12. http://dx.doi.org/10.30837/itssi.2020.14.005.

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Shu-Ching Chen and R. L. Kashyap. "A spatio-temporal semantic model for multimedia database systems and multimedia information systems." IEEE Transactions on Knowledge and Data Engineering 13, no. 4 (2001): 607–22. http://dx.doi.org/10.1109/69.940735.

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Berman, Sonia. "A semantic data model as the basis for an automated database design tool." Information Systems 11, no. 2 (1986): 149–65. http://dx.doi.org/10.1016/0306-4379(86)90004-9.

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