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IEEE International Conference on Services Computing (IEEE SCC)

作者： Teniente, Ernest Univ Politecn Catalunya UPC Barcelona Catalonia Spain

ISBN: (数字)9781665481465

ISBN: (纸本)9781665481465

In this position statement we advocate how the use of ontologies may provide a proper solution for IoT semantic data integration. With this, we will improve IoT interoperability and will facilitate the development of software applications that allow providing management and monitoring of different IoT installations in a generic and homogeneous way.

关键词： IoT semantic data integration ontology

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DIKG2: A semantic data integration Approach for Knowledge Graphs Generation from Web Forms 1

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34th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE)

作者： Dandan, Rahma Despres, Sylvie Sorbonne Paris Nord Univ F-93000 Bobigny France

ISBN: (数字)9783030794576

ISBN: (纸本)9783030794569;9783030794576

Over the last two decades, semantic-based data integration has become one of the major data challenges. The use of semantic Web standards and Linked Open data (LOD), especially knowledge graphs (KGs), provides support to address issues related to data access and integration. In this paper, we propose the approach DIKG2 to structure semi-structured data from Web forms and stored in a RDB and to facilitate the dynamic data integration in the KGs. By exploiting a common vocabulary between the Web forms and the entities of our domain ontology, OAFE, we describe the mapping process performed between RDB schema and the ontology's conceptual schema.

关键词： semantic data integration Knowledge Graph Ontology based data integration User profiling Personalized recommendation

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SNOWL model: social networks unification-based semantic data integration

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KNOWLEDGE AND INFORMATION SYSTEMS 2020年第11期62卷 4297-4336页

作者： Sebei, Hiba Hadj Taieb, Mohamed Ali Ben Aouicha, Mohamed Multimedia InfoRmat Syst & Adv Comp Lab Sfax Tunisia Univ Sfax Dept Comp Sci Fac Sci Sfax Tunisia

Integrating social networks data in the process of promoting business and marketing applications is widely addressed by several researchers. However, regarding the isolation between social network platforms managing such data has become a challenging task facing data scientist. In this respect, the present paper is designed to put forward a special semantic data integration approach, whereby a unified presentation and access to social networks data can be maintained. To this end, the novel SNOWL (Social Network OWL) ontology aims to provide a new social network content modeling, following the UPON Lite ontology-construction methodology. The advanced ontology is not created from scratch;it is but a continuation of some previously devised ontologies, elaborated to integrate an additional selection of newly incorporated social entities, such as content and user popularity. Additionally, and for an effective advantage of the model to be gained, a special mapping of the social networks data has been firstly implemented to the designed ontology, developed on the basis of the RML mapping language. Secondly, the SNOWL ontology is evaluated through the OOPS! Pitfall tool. Finally, a set of SPARQL-based services has also been designed on top of the SNOWL ontology in a bid to ensure a unified access to the mapped social data.

关键词： Social networks semantic data integration Knowledge discovery SPARQL query RML

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Towards a conceptual framework for African smart cities semantic data integration 5

Towards a conceptual framework for African smart cities sema...

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5th IEEE Annual International Smart Cities Conference (ISC2)

作者： Guinko, Ferdinand Traore, Yaya Ben Sta, Hatem Univ Joseph KI ZERBO Comp Sci Dept Inst Burkinabe Arts & Metiers Ouagadougou Burkina Faso Univ El Manar Comp Sci Dept Tunis Tunisia

ISBN: (纸本)9781728108469

Smart cities are composed of a number of dimensions such as transportation system, education system, health system, governance system, etc., that generate great amount of data that are stored such as heap of data. But these heap of data remain independent one from another. In this paper, we have presented a conceptual framework for a smart city semantic data integration.

关键词： African cities semantic data integration Internet of Things (IoT) Smart Cities Africa Smart mobility Burkinabe cities Internet of Things (IoT) Smart Cities

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MapSDI: A Scaled-Up semantic data integration Framework for Knowledge Graph Creation

MapSDI: A Scaled-Up Semantic Data Integration Framework for ...

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27th International Conference on Cooperative Information Systems, (CoopIS) / International Conference on Ontologies, databases, and Applications of semantics (ODBASE) / Conference on Cloud and Trusted Computing (C and TC)

作者： Jozashoori, Samaneh Vidal, Maria-Esther Leibniz Univ Hannover L3S Res Ctr Hannover Germany TIB Leibniz Informat Ctr Sci & Technol Hannover Germany

ISBN: (纸本)9783030332464;9783030332457

semantic web technologies have significantly contributed with effective solutions for the problems of data integration and knowledge graph creation. However, with the rapid growth of big data in diverse domains, different interoperability issues still demand to be addressed, being scalability one of the main challenges. In this paper, we address the problem of knowledge graph creation at scale and provide MapSDI, a mapping rule-based framework for optimizing semantic data integration into knowledge graphs. MapSDI allows for the semantic enrichment of large-sized, heterogeneous, and potentially low-quality data efficiently. The input of MapSDI is a set of data sources and mapping rules being generated by a mapping language such as RML. First, MapSDI pre-processes the sources based on semantic information extracted from mapping rules, by performing basic database operators;it projects out required attributes, eliminates duplicates, and selects relevant entries. All these operators are defined based on the knowledge encoded by the mapping rules which will be then used by the semantification engine (or RDFizer) to produce a knowledge graph. We have empirically studied the impact of MapSDI on existing RDFizers, and observed that knowledge graph creation time can be reduced on average in one order of magnitude. It is also shown, theoretically, that the sources and rules transformations provided by MapSDI are data-lossless.

关键词： Knowledge graph creation semantic data integration Transformation rules data integration system

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An ontology-guided semantic data integration framework to support integrative data analysis of cancer survival

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2018年第2期18卷 129-147页

作者： Zhang, Hansi Guo, Yi Li, Qian George, Thomas J. Shenkman, Elizabeth Modave, Francois Bian, Jiang Univ Florida Coll Med Dept Hlth Outcomes & Biomed Informat Clin & Translat Res Bldg Suite 32282004 Mowry Rd Gainesville FL 32610 USA Univ Florida Coll Med Dept Med Div Hematol & Oncol Gainesville FL USA

Background: Cancer is the second leading cause of death in the United States, exceeded only by heart disease. Extant cancer survival analyses have primarily focused on individual-level factors due to limited data availability from a single data source. There is a need to integrate data from different sources to simultaneously study as much risk factors as possible. Thus, we proposed an ontology-based approach to integrate heterogeneous datasets addressing key data integration challenges. Methods: Following best practices in ontology engineering, we created the Ontology for Cancer Research Variables (OCRV) adapting existing semantic resources such as the National Cancer Institute (NCI) Thesaurus. Using the global-as-view data integration approach, we created mapping axioms to link the data elements in different sources to OCRV. Implemented upon the Ontop platform, we built a data integration pipeline to query, extract, and transform data in relational databases using semantic queries into a pooled dataset according to the downstream multi-level Integrative data Analysis (IDA) needs. Results: Based on our use cases in the cancer survival IDA, we created tailored ontological structures in OCRV to facilitate the data integration tasks. Specifically, we created a flexible framework addressing key integration challenges: (1) using a shared, controlled vocabulary to make data understandable to both human and computers, (2) explicitly modeling the semantic relationships makes it possible to compute and reason with the data, (3) linking patients to contextual and environmental factors through geographic variables, (4) being able to document the data manipulation and integration processes clearly in the ontologies. Conclusions: Using an ontology-based data integration approach not only standardizes the definitions of data variables through a common, controlled vocabulary, but also makes the semantic relationships among variables from different sources explicit and clear to al

关键词： semantic data integration Ontology semantic web Cancer survival Integrative data analysis

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4D-SETL A semantic data integration Framework 18

4D-SETL <i>A Semantic Data Integration Framework</i>

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18th International Conference on Enterprise Information Systems (ICEIS)

作者： de Cesare, Sergio Foy, George Lycett, Mark Brunel Univ London Coll Business Arts & Social Sci Uxbridge Middx England Brunel Univ London Coll Engn Design & Phys Sci Uxbridge Middx England

ISBN: (纸本)9789897581878

Although successfully employed as the foundation for a number of large-scale government and energy industry projects, foundational ontologies have not been widely adopted within mainstream Enterprise Systems (ES) data integration practice. However, as the closed-worlds of ES are opened to Internet scale data sources, there is an emerging need to better understand the semantics of such data and how they can be integrated. Foundational ontologies can help establish this understanding and therefore, there is a need to investigate how such ontologies can be applied to underpin practical ES integration solutions. This paper describes research undertaken to assess the effectiveness of such an approach through the development and application of the 4D-semantic Extract Transform Load (4D-SETL) framework. 4D-SETL was employed to integrate a number of large scale datasets and to persist the resultant ontology within a prototype warehouse based on a graph database. The advantages of the approach included the ability to combine foundational, domain and instance level ontological objects within a single coherent system. Furthermore, the approach provided a clear means of establishing and maintaining the identity of domain objects as their constituent spatiotemporal parts unfolded over time, enabling process and static data to be combined within a single model.

关键词： Foundational Ontology Perdurantist 4D semantic data integration Modelling Graph databases integration Frameworks

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KD SENSO-MERGER: An architecture for semantic integration of heterogeneous data

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 132卷

作者： Gutierrez, Yoan Salas, Jose I. Abreu Montoyo, Andres Munoz, Rafael Estevez-Velarde, Suilan Univ Alicante Univ Inst Comp Res Carretera San Vicente Raspeig S-N Alicante 03690 Spain Univ Havana Dept Artificial Intelligence & Comp Syst San Lazaro Y Edificio Felipe PoeyPlaza Revoluc Havana Cuba

This paper presents KD SENSO-MERGER, a novel Knowledge Discovery (KD) architecture that is capable of semantically integrating heterogeneous data from various sources of structured and unstructured data (i.e. geolocations, demographic, socio-economic, user reviews, and comments). This goal drives the main design approach of the architecture. It works by building internal representations that adapt and merge knowledge across multiple domains, ensuring that the knowledge base is continuously updated. To deal with the challenge of integrating heterogeneous data, this proposal puts forward the corresponding solutions: (i) knowledge extraction, addressed via a plugin-based architecture of knowledge sensors;(ii) data integrity, tackled by an architecture designed to deal with uncertain or noisy information;(iii) scalability, this is also supported by the plugin-based architecture as only relevant knowledge to the scenario is integrated by switching-off non-relevant sensors. Also, we minimize the expert knowledge required, which may pose a bottleneck when integrating a fast-paced stream of new sources. As proof of concept, we developed a case study that deploys the architecture to integrate population census and economic data, municipal cartography, and Google Reviews to analyze the socio-economic contexts of educational institutions. The knowledge discovered enables us to answer questions that are not possible through individual sources. Thus, companies or public entities can discover patterns of behavior or relationships that would otherwise not be visible and this would allow extracting valuable information for the decision-making process.

关键词： Heterogeneous data Knowledge discovery NERC Natural language processing Ontology and knowledge representation semantic data integration

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IPDS: A semantic mediator-based system using Spark for the integration of heterogeneous proteomics data sources

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第1期33卷 e5814-e5814页

作者： Messaoudi, Chaimaa Fissoune, Rachida Badir, Hassan Abdelmalek Essaadi Univ Syst & Data Engn Team BP 1818 Tangier 90000 Morocco

With the constant rise of data volumes in many disciplines, various new Big data management systems have emerged to provide scalable tools for efficient data integration, processing, and analysis. In this article, we provide an overview of biomedical data integration systems focusing on ontology-based semantic systems and Big data technologies based systems such as Apache Spark. We also propose a new semantic data integration system, called Integrated Proteomics data System (IPDS), which uses a mediator approach. IPDS provides users a unified interface for query processing and data exploration. This system takes advantage of the Apache Spark framework to perform the query transformation and execution needed to question the integrated data sources. We develop a domain ontology that allows the user to formulate its queries in terms defined in the ontology. IPDS is a case study of semantic proteomics data integration linking four data sources UniProt (protein annotation), String (protein-protein interaction), PDB (protein structure), and Pubmed (biomedical citation).

关键词： Apache spark mediator ontology proteomics semantic data integration

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Enhancing Rare Disease Research with semantic integration of Environmental and Health data 10

Enhancing Rare Disease Research with Semantic Integration of...

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10th International Joint Conference on Knowledge Graphs (IJCKG)

作者： Navarro-Gallinad, Albert Orlandi, Fabrizio O'Sullivan, Declan Trinity Coll Dublin ADAPT Ctr Digital Content Dublin Ireland

ISBN: (纸本)9781450395656

Knowledge Graph (KG) approaches are increasingly being used for data integration processes to combine clinical data with other data sources. Health data Researchers (HDR) could benefit from these technologies since they require additional types of data outside the health sector, like environmental data, to better understand the extrinsic factors that influence health outcomes in rare disease research. However, using and directly navigating the combined data in the KG can be an obstacle for HDRs. To address this problem, the semantic Environmental and Rare Disease data integration Framework (SERDIF) was designed to hide the complexities for these researchers when exploring linked environmental observations with clinical data using a KG approach. The framework was evaluated by HDRs for a case study on Anti-neutrophil cytoplasm antibody (ANCA)-associated vasculitis (AAV) in Ireland, and promising usability and effectiveness results were observed. HDRs studying AAV were able to access, explore and export environmental related data to be used as input for their statistical models. SERDIF has the potential to be a solution for HDRs, who require a flexible methodology to integrate environmental data with longitudinal and geospatial diverse clinical data, in their hypothesis validation of environmental factors for rare disease research.

关键词： semantic data integration Knowledge Graph Environmental Health Usability Testing Rare diseases

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