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Search with Meanings: an Overview of Semantic Search Systems
Search with Meanings: An Overview of Semantic Search Systems Wang Wei, Payam M. Barnaghi, Andrzej Bargiela School of Computer Science, University of Nottingham Malaysia Campus Jalan Broga, 43500 Semenyih, Selangor, Malaysia Email: feyx6ww; payam.barnaghi; [email protected] Abstract: Research on semantic search aims to improve con- information. The paper provides a survey to gain an overall ventional information search and retrieval methods, and facil- view of the current research status. We classify our studied sys- itate information acquisition, processing, storage and retrieval tems into several categories according to their most distinctive on the semantic web. The past ten years have seen a num- features, as discussed in the next section. The categorisation ber of implemented semantic search systems and various pro- by no means prevents a system from being classified into other posed frameworks. A comprehensive survey is needed to gain categories. Further, we limit the scope of the survey to Web an overall view of current research trends in this field. We and Intranet searching and browsing systems (also including have investigated a number of pilot projects and corresponding some question answering and multimedia presentation gener- practical systems focusing on their objectives, methodologies ation systems). There are also few other survey studies of se- and most distinctive characteristics. In this paper, we report mantic search research, Makel¨ a¨ provides a short survey con- our study and findings based on which a generalised semantic cerning search methodologies [34]; Hildebrand et al discuss search framework is formalised. Further, we describe issues the related research from three perspectives: query construc- with regards to future research in this area. -
Automated Development of Semantic Data Models Using Scientific Publications Martha O
University of New Mexico UNM Digital Repository Computer Science ETDs Engineering ETDs Spring 5-12-2018 Automated Development of Semantic Data Models Using Scientific Publications Martha O. Perez-Arriaga University of New Mexico Follow this and additional works at: https://digitalrepository.unm.edu/cs_etds Part of the Computer Engineering Commons Recommended Citation Perez-Arriaga, Martha O.. "Automated Development of Semantic Data Models Using Scientific ubP lications." (2018). https://digitalrepository.unm.edu/cs_etds/89 This Dissertation is brought to you for free and open access by the Engineering ETDs at UNM Digital Repository. It has been accepted for inclusion in Computer Science ETDs by an authorized administrator of UNM Digital Repository. For more information, please contact [email protected]. Martha Ofelia Perez Arriaga Candidate Computer Science Department This dissertation is approved, and it is acceptable in quality and form for publication: Approved by the Dissertation Committee: Dr. Trilce Estrada-Piedra, Chairperson Dr. Soraya Abad-Mota, Co-chairperson Dr. Abdullah Mueen Dr. Sarah Stith i AUTOMATED DEVELOPMENT OF SEMANTIC DATA MODELS USING SCIENTIFIC PUBLICATIONS by MARTHA O. PEREZ-ARRIAGA M.S., Computer Science, University of New Mexico, 2008 DISSERTATION Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Computer Science The University of New Mexico Albuquerque, New Mexico May, 2018 ii Dedication “The highest education is that which does not merely give us information but makes our life in harmony with all existence" Rabindranath Tagore I dedicate this work to the memory of my primary role models: my mother and grandmother, who always gave me a caring environment and stimulated my curiosity. -
DELIVERABLE D3.2 Survey of Data Models, Ontologies and Standards in the Wider Energy Efficient Buildings Domain
Ref. Ares(2019)5483631 - 30/08/2019 Project Acronym: BIMERR Project Full Title: BIM-based holistic tools for Energy-driven Renovation of existing Residences Grant Agreement: 820621 Project Duration: 42 months DELIVERABLE D3.2 Survey of data models, ontologies and standards in the wider Energy Efficient Buildings domain Deliverable Status: Final File Name: D3.2. Survey of data models ontologies and standards v1.0.docx Due Date: 31/08/2019 (M8) Submission Date: 30/08/2019 (M8) Task Leader: UPM (T3.2) Dissemination level Public X Confidential, only for members of the Consortium (including the Commission Services) This project has received funding from the European Union’s Horizon 2020 Research and innovation programme under Grant Agreement n°820621 The BIMERR project consortium is composed of: Fraunhofer Gesellschaft Zur Foerderung Der Angewandten Forschung FIT Germany E.V. CERTH Ethniko Kentro Erevnas Kai Technologikis Anaptyxis Greece UPM Universidad Politecnica De Madrid Spain UBITECH Ubitech Limited Cyprus SUITE5 Suite5 Data Intelligence Solutions Limited Cyprus Hypertech (Chaipertek) Anonymos Viomichaniki Emporiki Etaireia HYPERTECH Greece Pliroforikis Kai Neon Technologion MERIT Merit Consulting House Sprl Belgium XYLEM Xylem Science And Technology Management Gmbh Austria GU Glassup Srl Italy Anonymos Etaireia Kataskevon Technikon Ergon, Emporikon CONKAT Greece Viomichanikonkai Nautiliakon Epicheiriseon Kon'kat BOC Boc Asset Management Gmbh Austria BX Budimex Sa Poland UOP University Of Peloponnese Greece EXE Exergy Ltd United Kingdom HWU Heriot-Watt University United Kingdom NT Novitech As Slovakia FER Ferrovial Agroman S.A Spain Disclaimer BIMERR project has received funding from the European Union’s Horizon 2020 Research and innovation programme under Grant Agreement n°820621. -
Semantic Analytics Visualization
Wright State University CORE Scholar The Ohio Center of Excellence in Knowledge- Kno.e.sis Publications Enabled Computing (Kno.e.sis) 5-2006 Semantic Analytics Visualization Leonidas Deligiannidis Amit P. Sheth Wright State University - Main Campus, [email protected] Boanerges Aleman-Meza Follow this and additional works at: https://corescholar.libraries.wright.edu/knoesis Part of the Bioinformatics Commons, Communication Technology and New Media Commons, Databases and Information Systems Commons, OS and Networks Commons, and the Science and Technology Studies Commons Repository Citation Deligiannidis, L., Sheth, A. P., & Aleman-Meza, B. (2006). Semantic Analytics Visualization. Lecture Notes in Computer Science, 3975, 48-59. https://corescholar.libraries.wright.edu/knoesis/718 This Conference Proceeding is brought to you for free and open access by the The Ohio Center of Excellence in Knowledge-Enabled Computing (Kno.e.sis) at CORE Scholar. It has been accepted for inclusion in Kno.e.sis Publications by an authorized administrator of CORE Scholar. For more information, please contact library- [email protected]. Semantic Analytics Visualization. (To Appear in) Intelligence and Security Informatics, Proceedings of ISI-2006, LNCS #3975 Semantic Analytics Visualization Leonidas Deligiannidis1,2, Amit P. Sheth2 and Boanerges Aleman-Meza2 1Virtual Reality Lab and 2LSDIS Lab, Computer Science, The University of Georgia, Athens, GA 30602, USA {ldeligia,amit,boanerg}@cs.uga.edu Abstract. In this paper we present a new tool for semantic analytics through 3D visualization called “Semantic Analytics Visualization” (SAV). It has the capability for visualizing ontologies and meta-data including annotated web- documents, images, and digital media such as audio and video clips in a syn- thetic three-dimensional semi-immersive environment. -
1 Geospatial and Temporal Semantic Analytics 1. Introduction
Geospatial and Temporal Semantic Analytics Matthew Perry, Amit Sheth, Ismailcem Budak Arpinar, Farshad Hakimpour Large Scale Distributed Information Systems Lab, The University of Georgia, Athens, GA 1. Introduction The amount of digital data available to researchers and knowledge workers has grown tremendously in recent years. This is especially true in the geography domain. As the amount of data grows, problems of data relevance and information overload become more severe. The use of semantics has been proposed to combat these problems (Berners-Lee et al., 2001; Egenhofer, 2002). Semantics refers to the meaning of data rather than its syntax or structure. Systems which can understand and process data at a semantic level can achieve a higher level of automation, integration, and interoperability. Applications using semantic technologies fall into three basic categories: 1) semantic integration, 2) semantic search and contextual browsing, and 3) semantic analytics and knowledge discovery (Sheth & Ramakrishnan, 2003). This article concentrates on semantic analytics and knowledge discovery. Applications in this category provide tools and techniques for analyzing entities and relationships in semantic metadata. So far, research in this area has mainly focused on the thematic dimension of information and has largely ignored its spatial and temporal dimensions. At the same time, the Geographic Information Systems community has yet to integrate thematic knowledge of entities and their relationships with geospatial knowledge for purposes of semantic analysis and discovery. Next generation geoinformatics applications that can successfully combine knowledge of real world entities and relationships with knowledge of their interactions in space and time will have huge potential in areas such as national security and emergency response. -
The NIDDK Information Network: a Community Portal for Finding Data, Materials, and Tools for Researchers Studying Diabetes, Digestive, and Kidney Diseases
RESEARCH ARTICLE The NIDDK Information Network: A Community Portal for Finding Data, Materials, and Tools for Researchers Studying Diabetes, Digestive, and Kidney Diseases Patricia L. Whetzel1, Jeffrey S. Grethe1, Davis E. Banks1, Maryann E. Martone1,2* 1 Center for Research in Biological Systems, University of California, San Diego, San Diego, California, a11111 United States of America, 2 Dept of Neurosciences, University of California, San Diego, San Diego, California, United States of America * [email protected] Abstract OPEN ACCESS The NIDDK Information Network (dkNET; http://dknet.org) was launched to serve the needs Citation: Whetzel PL, Grethe JS, Banks DE, Martone of basic and clinical investigators in metabolic, digestive and kidney disease by facilitating ME (2015) The NIDDK Information Network: A access to research resources that advance the mission of the National Institute of Diabe- Community Portal for Finding Data, Materials, and Tools for Researchers Studying Diabetes, Digestive, tes and Digestive and Kidney Diseases (NIDDK). By research resources, we mean the and Kidney Diseases. PLoS ONE 10(9): e0136206. multitude of data, software tools, materials, services, projects and organizations available to doi:10.1371/journal.pone.0136206 researchers in the public domain. Most of these are accessed via web-accessible data- Editor: Chun-Hsi Huang, University of Connecticut, bases or web portals, each developed, designed and maintained by numerous different UNITED STATES projects, organizations and individuals. While many of the large government funded data- Received: February 17, 2015 bases, maintained by agencies such as European Bioinformatics Institute and the National Accepted: July 30, 2015 Center for Biotechnology Information, are well known to researchers, many more that have been developed by and for the biomedical research community are unknown or underuti- Published: September 22, 2015 lized. -
ADMS Issues 09 February 2012 Proposed Proposed Category JIRA Description Action Resolution
ADMS Issues 09 February 2012 Proposed Proposed Category JIRA Description Action Resolution Joinup issues ISACV-229 Publish specification in more accessible format than PDF Accept Move to Joinup issues ISACV-234 Don't ask for participation in survey for every anonymous download Accept Move to Joinup issues Editorial issues Simple text and diagram suggestions ISACV-187 UML diagram for customization may be confusing Accept Remove diagram. ISACV-224 Check spelling of "Licence" throughout Accept Align spelling. ISACV-236 Add column data type to tables in 5.4 and 5.5 Accept Add columns. ISACV-256 Improve terms and definitions Accept Check, align, add clarifications. ISACV-261 Change "eGovernment Data" to "eGovernment Primary Resources" Accept Change phrase. ISACV-262 Ordering of properties Reject ISACV-245 Align element names in mapping sheet with specifications Accept Align names. ISACV-266 Some relationships in the model diagram are not clearly visible Accept Redraft diagram. ISACV-269 Textual changes in section 5.6 Accept Change text. ISACV-212 Consider renaming release into artefact Decide Change name to "Artefact". ISACV-222 Status "Published" may be confusing Decide Change name to "Completed". Clarifications ISACV-235 Dates and version numbers on ADMS examples are confusing Accept Add clarifications. ISACV-255 Better define what material ADMS can be used for Accept Add clarifications. ISACV-259 Better define responsibility of Publisher Accept Add clarifications. General feedback ISACV-110 To provide further comments on ADMS use cases Keep Provide feedback. ISACV-120 To verify list of Asset Types in mapping exercise and public comment Keep Provide feedback. Use cases ISACV-258 Questioning assignment of importance to attributes in Use case Accept Add clarifications. -
Framework for Matching and Linking Large Ontologies
Framework for Matching and Linking Large Ontologies Kow Weng Onn 1, Michelle Lim Sien Niu 1, Gudrun Johannsen 2, Johannes Keizer 2, Dickson Lukose 1, 1MIMOS Berhad Technology Park Malaysia, Kuala Lumpur, Malaysia 57000 {kwonn | michelle.lim | dickson.lukose}@mimos.my 2The Food and Agricultural Organization of UN (FAO), Rome, Italy {Gudrun.Johannsen| Johannes.Keizer}@fao.org Abstract. The Linked Open Data (LOD) initiative is the first large-scale attempt at realizing the Semantic Web vision of Tim Berners-Lee. As of September 2011, there are almost 300 knowledge bases linked together and available for public access through both web browsers as well as semantic applications. However, the task of building the links between knowledge bases is still a laborious manual task and as the LOD cloud continues to grow rapidly, the task becomes more daunting. This paper looks at the AGROVOC thesaurus developed and maintained by the United Nations Food and Agriculture Organization (UN FAO), and how a framework can be developed to semi- automatically discover links between AGROVOC and other knowledge bases on the LOD. Keywords: Linked Open Data, Ontology Matching, AGROVOC Thesaurus 1 Introduction The Linked Open Data (LOD) initiative is the first large-scale attempt at fulfilling the Semantic Web vision of Tim Berners-Lee [25].However, despite having a large number of datasets and knowledge in the form of RDF triples, there is still a lack of links between the various datasets. According to the State of the LOD Cloud website [24], the number of datasets as at September 2011 is 295 with over 31 billion triples. -
Etsi Tr 103 509 V1.1.1 (2019-10)
ETSI TR 103 509 V1.1.1 (2019-10) TECHNICAL REPORT SmartM2M; SAREF extension investigation; Requirements for eHealth/Ageing-well 2 ETSI TR 103 509 V1.1.1 (2019-10) Reference DTR/SmartM2M-103509 Keywords ageing, eHealth, IoT, oneM2M, ontology, SAREF, semantic ETSI 650 Route des Lucioles F-06921 Sophia Antipolis Cedex - FRANCE Tel.: +33 4 92 94 42 00 Fax: +33 4 93 65 47 16 Siret N° 348 623 562 00017 - NAF 742 C Association à but non lucratif enregistrée à la Sous-Préfecture de Grasse (06) N° 7803/88 Important notice The present document can be downloaded from: http://www.etsi.org/standards-search The present document may be made available in electronic versions and/or in print. The content of any electronic and/or print versions of the present document shall not be modified without the prior written authorization of ETSI. In case of any existing or perceived difference in contents between such versions and/or in print, the prevailing version of an ETSI deliverable is the one made publicly available in PDF format at www.etsi.org/deliver. Users of the present document should be aware that the document may be subject to revision or change of status. Information on the current status of this and other ETSI documents is available at https://portal.etsi.org/TB/ETSIDeliverableStatus.aspx If you find errors in the present document, please send your comment to one of the following services: https://portal.etsi.org/People/CommiteeSupportStaff.aspx Copyright Notification No part may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying and microfilm except as authorized by written permission of ETSI. -
Farm Data Management, Sharing and Services for Agriculture Development ©Adobe Stock/Only Kim Farm Data Management, Sharing and Services for Agriculture Development
Chapter 0: Name of chapter A Farm data management, sharing and services for agriculture development ©Adobe Stock/only_kim Farm data management, sharing and services for agriculture development Food and Agriculture Organization of the United Nations Rome, 2021 Required citation: The designations employed and the presentation of material in this information FAO. 2021. product do not imply the expression of any opinion whatsoever on the part of Farm data management, the Food and Agriculture Organization of the United Nations (FAO) concerning sharing and services the legal or development status of any country, territory, city or area or of its for agriculture authorities, or concerning the delimitation of its frontiers or boundaries. Dashed development. Rome. lines on maps represent approximate border lines for which there may not yet be https://doi.org/10.4060/ full agreement. The mention of specific companies or products of manufacturers, cb2840en whether or not these have been patented, does not imply that these have been endorsed or recommended by FAO in preference to others of a similar nature that are not mentioned. The views expressed in this information product are those of the author(s) and do not necessarily reflect the views or policies of FAO. ISBN 978-92-5-133837-7 © FAO, 2021 Some rights reserved. This work is made available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO licence (CC BY-NC-SA 3.0 IGO; https://creativecommons.org/licenses/by-nc-sa/3.0/igo/legalcode). Under the terms of this licence, this work may be copied, redistributed and adapted for non-commercial purposes, provided that the work is appropriately cited. -
Domain-Specific Knowledge Graphs: a Survey
Domain-specific Knowledge Graphs: A survey Bilal Abu-Salih King Abdullah II School of Information Technology The University of Jordan [email protected] Abstract Knowledge Graphs (KGs) have made a qualitative leap and effected a real revolution in knowledge representation. This is leveraged by the underlying structure of the KG which underpins a better comprehension, reasoning and interpretation of knowledge for both human and machine. Therefore, KGs continue to be used as the main means of tackling a plethora of real-life problems in various domains. However, there is no consensus in regard to a plausible and inclusive definition of a domain-specific KG. Further, in conjunction with several limitations and deficiencies, various domain-specific KG construction approaches are far from perfect. This survey is the first to offer a comprehensive definition of a domain-specific KG. Also, the paper presents a thorough review of the state-of-the-art approaches drawn from academic works relevant to seven domains of knowledge. An examination of current approaches reveals a range of limitations and deficiencies. At the same time, uncharted territories on the research map are highlighted to tackle extant issues in the literature and point to directions for future research. Keywords: Knowledge Graph; Domain-specific Knowledge Graph; Knowledge Graph Construction; Knowledge Graph Embeddings; Knowledge Graph evaluation; Domain Ontology; Survey. 1. Introduction KGs, one of the key trends which are driving the next wave of technologies [1], have now become a new form of knowledge representation, and the cornerstone of several applications ranging from generic to specific industrial usage cases [2]. -
February 2009 Vol
Spatial Data Infrastructure – Africa Newsletter SDI-Africa Newsletter February 2009 Vol. 8, No. 2 Spatial Data Infrastructure - Africa (SDI-Africa) is a To subscribe to SDI-Africa, please do so online at: free, electronic newsletter for people interested in http://www.gsdi.org/newslist/gsdisubscribe.asp GIS, remote sensing, and data management in Africa. Published monthly since May 2002, it raises To unsubscribe, or change your email address: awareness and provides useful information to http://www.gsdi.org/newslist/gsdisunsubscribe.asp strengthen SDI efforts and support synchronization Please mention SDI-Africa as a source of of regional activities. ECA/CODIST-Geo, information in correspondence you may have RCMRD/SERVIR, RECTAS, AARSE, H EIS-AFRICA H, about items in this issue. SDI-EA, and MadMappers are some of the other regional groups promoting SDI development. The SDI-Africa newsletter is prepared for the GSDI Association by the Regional Centre for Mapping of Resources for Development (RCMRD) in Nairobi, Kenya. RCMRD builds capacity in surveying and mapping, remote sensing, geographic information systems, and natural resources assessment and management. RCMRD has been active in SDI in Africa through its contributions to the African Geodetic Reference Frame (AFREF) and SERVIR-Africa, a regional visualization and monitoring system initiative. RCMRD also implements projects on behalf of its member States and development partners. If you have news or information related to GIS, remote sensing, and spatial data infrastructure that you would like to highlight (e.g., workshop announcements, publications, reports, websites of interest, etc.), kindly send them in by the 25th of each month. I’d be happy to include your news in the newsletter.