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         "type" : "Publication",
         "id"   : "https://puma.ub.uni-stuttgart.de/bibtex/24f334c0f8c03d6160eb15a0b62579558/diglezakis",         
         "tags" : [
            "metadata","linkedData","semanticWeb","integration"
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         "intraHash" : "4f334c0f8c03d6160eb15a0b62579558",
         "interHash" : "c96c3eac1cff15103a34fbbb1025b9a0",
         "label" : "Large-scale Semantic Integration of Linked Data: A Survey",
         "user" : "diglezakis",
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         "date" : "2024-06-19 15:02:54",
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         "journal": "ACM Comput. Surv.","publisher":"Association for Computing Machinery","address":"New York, NY, USA",
         "year": "2019", 
         "url": "https://doi.org/10.1145/3345551", 
         
         "author": [ 
            "Michalis Mountantonakis","Yannis Tzitzikas"
         ],
         "authors": [
         	
            	{"first" : "Michalis",	"last" : "Mountantonakis"},
            	{"first" : "Yannis",	"last" : "Tzitzikas"}
         ],
         "volume": "52","number": "5","abstract": "A large number of published datasets (or sources) that follow Linked Data principles is currently available and this number grows rapidly. However, the major target of Linked Data, i.e., linking and integration, is not easy to achieve. In general, information integration is difficult, because (a) datasets are produced, kept, or managed by different organizations using different models, schemas, or formats, (b) the same real-world entities or relationships are referred with different URIs or names and in different natural languages,<?brk?>(c) datasets usually contain complementary information, (d) datasets can contain data that are erroneous, out-of-date, or conflicting, (e) datasets even about the same domain may follow different conceptualizations of the domain, (f) everything can change (e.g., schemas, data) as time passes. This article surveys the work that has been done in the area of Linked Data integration, it identifies the main actors and use cases, it analyzes and factorizes the integration process according to various dimensions, and it discusses the methods that are used in each step. Emphasis is given on methods that can be used for integrating several datasets. Based on this analysis, the article concludes with directions that are worth further research.",
         "numpages" : "40",
         
         "articleno" : "103",
         
         "issn" : "0360-0300",
         
         "issue_date" : "September 2020",
         
         "doi" : "10.1145/3345551",
         
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         "id"   : "https://puma.ub.uni-stuttgart.de/bibtex/20eebb3ef5b8e464f74c55226c201ae32/diglezakis",         
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            "forschungsdaten","metadata","interoperability","semanticWeb","integration"
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         "intraHash" : "0eebb3ef5b8e464f74c55226c201ae32",
         "interHash" : "38e07e0ed9fba44a171eda575674c9dc",
         "label" : "Semantic Data Integration and Querying: A Survey and Challenges",
         "user" : "diglezakis",
         "description" : "Semantic Data Integration and Querying: A Survey and Challenges | ACM Computing Surveys",
         "date" : "2024-06-19 08:47:39",
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         "journal": "ACM Comput. Surv.","publisher":"Association for Computing Machinery","address":"New York, NY, USA",
         "year": "2024", 
         "url": "https://doi.org/10.1145/3653317", 
         
         "author": [ 
            "Maroua Masmoudi","Sana Ben Abdallah Ben Lamine","Mohamed Hedi Karray","Bernard Archimede","Hajer Baazaoui Zghal"
         ],
         "authors": [
         	
            	{"first" : "Maroua",	"last" : "Masmoudi"},
            	{"first" : "Sana",	"last" : "Ben Abdallah Ben Lamine"},
            	{"first" : "Mohamed Hedi",	"last" : "Karray"},
            	{"first" : "Bernard",	"last" : "Archimede"},
            	{"first" : "Hajer",	"last" : "Baazaoui Zghal"}
         ],
         "volume": "56","number": "8","pages": "1\u201335","abstract": "Digital revolution produces massive, heterogeneous and isolated data. These latter remain underutilized, unsuitable for integrated querying and knowledge discovering. Hence the importance of this survey on data integration which identifies challenging issues and trends. First, an overview of the different generations and basics of data integration is given. Then, semantic data integration is focused, since it semantically links data allowing wider insights and decision-making. More than thirty works are reviewed. The goal is to help analysts to identify relevant criteria to compare then choose among semantic data integration approaches, focusing on the category (materialized, virtual or hybrid) and querying techniques.",
         "numpages" : "35",
         
         "issn" : "0360-0300",
         
         "issue_date" : "August 2024",
         
         "doi" : "10.1145/3653317",
         
         "bibtexKey": "Masmoudi2024"

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