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Exposing vocabularies for soil as linked open data

Information Processing in Agriculture 2015年第3期2卷 208-216页

作者： Giovanni L’Abate Caterina Caracciolo Valeria Pesce Guntram Geser Vassilis Protonotarios Edoardo A.C.Costantini Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria(CREA) Agrobiology and Pedology Research CentreFirenzeItaly Global Forum on Agricultural Research RomeItaly Food and Agriculture Organization of the United Nations RomeItaly University of Alcala Alcala de HenaresSpain Salzburg Research SalzburgAustria

Standards to describe soil properties are well established,with many ISO specifications and a few international thesauri available for specific ***,in recent years,the European directive on "Infrastructure for Spatial Information in the European Community(INSPIRE)"has brought together most of the existing standards into a well defined ***,the adoption of these standards so far has not reached the level of semantic interoperability,defined in the paper,which would facilitate the building of data services that reuse and combine data from different *** paper reviews standards for describing soil data and reports on the work done within the EC funded agINFRA project to apply linked data technologies to existing standards and data in order to improve the interoperability of soil *** main result of this work is ***,an RDF vocabulary for soil concepts based on the UML INSPIRE model was ***,a KOS(Knowledge Organization System)for soil data was published and mapped to existing relevant KOS,based on the analysis of the SISI database of the CREA of *** work also has a methodological value,in that it proposes and applies a methodology to standardize metadata used in local scientific databases,a very common situation in the scientific ***,this work aims at contributing towards a wider adoption of the INSPIRE directive,by providing an RDF version of it.

关键词： Agriculture Soil Knowledge Organization Systems Vocabularies Resource Description Framework linked open data INSPIRE

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FAIR + FIT: Guiding Principles and Functional Metrics for linked open data(LOD) KOS Products

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Journal of data and Information Science 2020年第1期5卷 93-118页

作者： Marcia Lei Zeng Julaine Clunis School of Information Kent State UniversityOhioUSA

Purpose:To develop a set of metrics and identify criteria for assessing the functionality of LOD KOS products while providing common guiding principles that can be used by LOD KOS producers and users to maximize the functions and usages of LOD KOS ***/methodology/approach:data collection and analysis were conducted at three time periods in 2015–16,2017 and *** sample data used in the comprehensive data analysis comprises all datasets tagged as types of KOS in the datahub and extracted through their respective SPARQL endpoints.A comparative study of the LOD KOS collected from terminology services linked open Vocabularies(LOV)and BioPortal was also ***:The study proposes a set of Functional,Impactful and Transformable(FIT)metrics for LOD KOS as value *** FAIR principles,with additional recommendations,are presented for LOD KOS as open *** limitations:The metrics need to be further tested and aligned with the best practices and international standards of both open data and various types of *** implications:Assessment performed with FAIR and FIT metrics support the creation and delivery of user-friendly,discoverable and interoperable LOD KOS datasets which can be used for innovative applications,act as a knowledge base,become a foundation of semantic analysis and entity extractions and enhance research in science and the ***/value:Our research provides best practice guidelines for LOD KOS as value vocabularies.

关键词： Knowledge Organization Systems linked open data FAIR FIT Semantic web

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A multi-agent framework for mining semantic relations from linked data

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Journal of Zhejiang University-Science C(Computers and Electronics) 2012年第4期13卷 295-307页

作者： Hua-jun CHEN Tong YU Qing-zhao ZHENG Pei-qin GU Yu ZHANG School of Computer Science and Technology Zhejiang UniversityHangzhou 310027China Collegc of Information Zhejiang Sci-Tech UniversityHangzhou 310018China

linked data is a decentralized space of interlinked Resource Description Framework(RDF) graphs that are published,accessed,and manipulated by a multitude of Web ***,we present a multi-agent framework for mining hypothetical semantic relations from linked data,in which the discovery,management,and validation of relations can be carried out independently by different *** agents collaborate in relation mining by publishing and exchanging inter-dependent knowledge elements,e.g.,hypotheses,evidence,and proofs,giving rise to an evidentiary network that connects and ranks diverse knowledge *** results show that the framework is scalable in a multi-agent ***-world applications show that the framework is suitable for interdisciplinary and collaborative relation discovery tasks in social domains.

关键词： Semantic Web linked open data Semantic association discovery

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World-Wide Semantic Web of Agriculture Knowledge

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Journal of Integrative Agriculture 2012年第5期11卷 769-774页

作者： Dickson Lukose MIMOS Bhd. Technology Park MalaysiaKuala Lumpur 57000Malaysia

The rapid increase in the publication of knowledge bases as linked open data （LOD） warrants serious consideration from all concerned, as this phenomenon will potentially scale exponentially. This paper will briefly describe the evolution of the LOD, the emerging world-wide semantic web （WWSW）, and explore the scalability and performance features Of the service oriented architecture that forms the foundation of the semantic technology platform developed at MIMOS Bhd., for addressing the challenges posed by the intelligent future internet. This paper＂ concludes with a review of the current status of the agriculture linked open data.

关键词： linked open data world wide semantic web agriculture knowledge ontology semantic technology,service oriented architecture

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Dictionary,Thesaurus or Ontology? Disentangling Our Choices in the Semantic Web Jungle

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Journal of Integrative Agriculture 2012年第5期11卷 710-719页

作者： Armando Stellato Department of Enterprise Engineering University of Tor VergataRome 00133Italy

The Semantic Web seems finally close to maintaining its promise about a real world-wide graph of interconnected resources. The SPARQL query language and protocols and the linked open data initiative have laid the way for endless data endpoints sparse around the globe. However, for the Semantic Web to really happen, it does not suffice to get billions of triples out there： these must be shareable, interlinked and conform to widely accepted vocabularies. While more and more data are converted from already available large knowledge repositories of companies and organizations, the question whether these should be carefully converted to semantically consistent ontology vocabularies or find other shallow representations for their content naturally arises. The danger is to come up with massive amounts of useless data, a boomerang which could result to be contradictory for the success of the web of data. In this paper, I provide some insights on common problems which may arise when porting huge amount of existing data or conceptual schemes （very common in the agriculture domain） to resource description framwork （RDF）, and will address different modeling choices, by discussing in particular the relationship between the two main modeling vocabularies offered by W3C： OWL and SKOS.

关键词： ontologies thesauri knowledge modeling linked open data

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Introducing the open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis

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能源与人工智能(英文) 2021年第3期5卷 53-66页

作者： Meisam Booshehri Lukas Emele Simon Flügel Hannah Förster Johannes Frey Ulrich Frey Martin Glauer Janna Hastings Christian Hofmann Carsten Hoyer-Klick Ludwig Hülk Anna Kleinau Kevin Knosala Leander Kotzur Patrick Kuckertz Till Mossakowski Christoph Muschner Fabian Neuhaus Michaja Pehl Martin Robinius Vera Sehn Mirjam Stappel Institute for Applied Ecology BerlinGermany Institute for Applied Informatics(InfAI)/Leipzig University Germany Department of Energy Systems Analysis Institute of Networked Energy SystemsGerman Aerospace Center(DLR)StuttgartGermany Faculty of Computer Science Otto-von-Guericke-Universität MagdeburgGermany Reiner Lemoine Institut BerlinGermany Institute of Techno-economic Systems Analysis(IEK-3) Forschungszentrum JülichJülichGermany Institute of Energy Economics and Rational Energy Use University of StuttgartGermany Potsdam Institute for Climate Impact Research PotsdamGermany Fraunhofer Institute for Energy Economics and Energy System Technology KasselGermany

Heterogeneous data,different definitions and incompatible models are a huge problem in many domains,with no exception for the field of energy systems ***,it is hard to re-use results,compare model results or couple models at *** provide a precisely defined vocabulary to build a common and shared conceptu-alisation of the energy ***,we present the open Energy Ontology(OEO)developed for the domain of energy systems *** the OEO provides several benefits for the ***,it enables consistent annotation of large amounts of data from various research *** example is the open Energy Platform(OEP).Adding such annotations makes data semantically searchable,exchangeable,re-usable and ***,computational model coupling becomes much *** advantages of using an ontology such as the OEO are demonstrated with three use cases:data representation,data annotation and interface *** also describe how the ontology can be used for linked open data(LOD).

关键词： Collaborative ontology development linked open data Metadata annotation Energy systems analysis

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A Recommendation Mechanism for Web Publishing Based on Sentiment Analysis of Microblog

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Wuhan University Journal of Natural Sciences 2015年第2期20卷 146-152页

作者： TIAN Pingfang ZHU Zhonghua XIONG Li XU Fangfang College of Computer Science and Technology Wuhan University of Science and Technology Intelligent Information Processing and Real-time Industrial System Hubei Province Key Laboratory

Microblog is a social platform with huge user community and mass data. We propose a semantic recommendation mechanism based on sentiment analysis for microblog. Firstly, the keywords and sensibility words in this mechanism are extracted by natural language processing including segmentation, lexical analysis and strategy selection. Then, we query the background knowledge base based on linked open data （LOD） with the basic information of users. The experiment result shows that the accuracy of recommendation is within the range of 70% -89% with sentiment analysis and semantic query. Compared with traditional recommendation method, this method can satisfy users＇ requirement greatly.

关键词： sentiment analysis microblog keyword extraction linked open data background knowledge base

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Constructing and Cleaning Identity Graphs in the LOD Cloud

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data Intelligence 2020年第3期2卷 323-352页

作者： Joe Raad Wouter Beek Frank van Harmelen Jan Wielemaker Nathalie Pernelle Fatiha Saïs Deptartment of Computer Science Vrije UniversityAmsterdamThe Netherlands Computer Science Research Laboratory(LRI)of the University Paris Sud French National Centre for Scientific ResearchParis Saclay UniversityOrsayFrance

In the absence of a central naming authority on the Semantic Web,it is common for different data sets to refer to the same thing by different *** multiple names are used to denote the same thing,owl:sameAs statements are needed in order to link the data and foster *** that date back as far as 2009,observed that the owl:sameAs property is sometimes used *** our previous work,we presented an identity graph containing over 500 million explicit and 35 billion implied owl:sameAs statements,and presented a scalable approach for automatically calculating an error degree for each identity *** this paper,we generate subgraphs of the overall identity graph that correspond to certain error *** show that even though the Semantic Web contains many erroneous owl:sameAs statements,it is still possible to use Semantic Web data while at the same time minimising the adverse effects of misusing owl:sameAs.

关键词： linked open data Identity Quality Reasoning

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Healthcare data analytics:using a metadata annotation approach for integrating electronic hospital records

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Journal of Management Analytics 2016年第2期3卷 136-151页

作者： Boyi Xu Ke Xu LiuLiu Fu Ling Li Weiwei Xin Hongming Cai College of Economics&Management Shanghai Jiao Tong UniversityShanghai 200030China School of Software Shanghai Jiao Tong UniversityShanghai 200240China CSIS Department Stockton UniversityGallowayNJ 08205USA Strome College of Business Old Dominion UniversityNorfolkVA 23529USA Renji Hospital Shanghai Jiao Tong UniversityShanghai 200127China

The data in electronic medical records(EMR)are complex in *** are independent,yet related to each *** order to improve information access through the use of EMR,annotating work on these data is *** annotation on metadata,the resource data which contain a meta-model of the database,is the basis of the annotating work if a semi-automated or an automated annotating approach which aims at making the database more accessible is *** this study,a method has been proposed to transform the terms which cannot be matched directly by changing them literally but maintaining their semantics,and then annotating them *** the transforming work,a refinement method which is reducible to phrase sense disambiguation(PSD)is employed to ensure accuracy.A pilot study on a hospital database has been conducted to test the accuracy and effectiveness of the proposed method.

关键词： healthcare data analytics metadata annotation linked open data semantics phrase sense disambiguation

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基于DBpedia的材料知识抽取系统设计与实现

基于DBpedia的材料知识抽取系统设计与实现

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作者：李欣河北科技大学

学位级别：硕士

DBpedia是一个跨领域跨语言的综合链式数据集,并且具有上亿条三元组。虽然DBpedia提供了丰富的知识图谱,但由于DBpedia知识图谱包含各个学科数据,并且数据量庞大,使用金属领域材料数据不方便。另外DBpedia在金属材料领域知识不够全面,... 详细信息

DBpedia是一个跨领域跨语言的综合链式数据集,并且具有上亿条三元组。虽然DBpedia提供了丰富的知识图谱,但由于DBpedia知识图谱包含各个学科数据,并且数据量庞大,使用金属领域材料数据不方便。另外DBpedia在金属材料领域知识不够全面,缺少金属材料的性能数据、金属材料文献、金属材料图片等。本文为了构建金属材料知识图谱,提出了一种基于DBpedia的方法,设计了从DBpedia中抽取金属材料领域数据,并从Wikipedia网页中抽取相关金属领域数据进行丰富的系统。为了实现金属材料知识图谱的生成,本课题做了以下研究工作:1)从DBpedia中抽取金属材料知识。为了实现抽取,先通过直接连接子图语义距离算法进行初步筛选,然后再运用背景知识库算法做进一步筛选,以把所有金属材料相关的知识全部抽取出来。2)通过基于本体的表格抽取算法,对Wikipedia网页中金属材料性能表格进行处理,把从表格中抽取的性能数据和从DBpedia抽取的实体进行链接,实现对金属材料数据的补充。3)通过关键字和模板算法,对Wikipedia中金属材料网页的参考文献、材料专家、图片等信息进行抽取,对金属材料数据做进一步的补充。4)利用SILK关联框架,通过链接规则把形成的金属材料图谱和金属材料本体建立链接,以对金属材料图谱进一步语义丰富。基于DBpedia的金属材料抽取系统,不仅实现了从DBpedia中抽取了金属材料数据,而且实现了从Wikipedia网页中抽取相关信息进行补充,还实现了金属材料图谱和材料本体的链接。本文可以为材料信息学专家提供了一个从链式数据中抽取材料知识的方法,并提供一个语义丰富的金属材料知识图谱。

关键词： DBpedia Wikipedia linked open data 金属材料数据集成语义补充知识图谱

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