Knowledge Acquisition Framework from Unstructured Biomedical Knowledge Sources Demeke Asres Ayele
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The Semantic Web in Action
INFORMATION TECHNOLOGY KEY CONCEPTS Corporate applications are well under way, ■ A wide variety of online Semantic Web applications are and consumer uses are emerging emerging, from Vodafone Live!’s mobile phone service to Boeing’s system for coordinating the work of vendors. ix years ago in this magazine, Tim Ber- that would make this vision come true: a com- ners-Lee, James Hendler and Ora Las- mon language for representing data that could ■ Scientific researchers are devel- sila unveiled a nascent vision of the be understood by all kinds of software agents; oping some of the most advanced S Semantic Web: a highly interconnected network ontologies—sets of statements—that translate applications, including a system that pinpoints genetic causes of of data that could be easily accessed and under- information from disparate databases into heart disease and another system stood by any desktop or handheld machine. common terms; and rules that allow software that reveals the early stages of in- They painted a future of intelligent software agents to reason about the information de- fluenza outbreaks. agents that would head out on the World Wide scribed in those terms. The data format, ontol- Web and automatically book flights and hotels ogies and reasoning software would operate ■ Companies and universities, working through the World Wide for our trips, update our medical records and like one big application on the World Wide Web Consortium, are developing give us a single, customized answer to a partic- Web, analyzing all the raw data stored in online standards that are making the Se- ular question without our having to search for databases as well as all the data about the text, mantic Web more accessible and information or pore through results. -
INFOCOMP / Datasys 2017 International Expert Panel: Challenges on Web Semantic Mapping and Information Processing
INFOCOMP / DataSys 2017 International Expert Panel: Challenges on Web Semantic Mapping and Information Processing INFOCOMP / DataSys 2017 International Expert Panel: Challenges on Web Semantic Mapping and Information Processing June 28, 2017, Venice, Italy The Seventh International Conference on Advanced Communications and Computation (INFOCOMP 2017) The Seventh International Conference on Advances in Information Mining and Management (IMMM 2017) The Twelfth International Conference on Internet and Web Applications and Services (ICIW 2017) INFOCOMP/IMMM/ICIW (DataSys) June 25{29, 2017 - Venice, Italy INFOCOMP, June 25 { 29, 2017 - Venice, Italy INFOCOMP / DataSys 2017 International Expert Panel: Challenges on Web Semantic Mapping and Information Processing INFOCOMP / DataSys 2017 International Expert Panel: Challenges on Web Semantic Mapping and Information Processing INFOCOMP Expert Panel: Web Semantic Mapping & Information Proc. INFOCOMP Expert Panel: Web Semantic Mapping & Information Proc. Panelists Claus-Peter R¨uckemann (Moderator), Westf¨alischeWilhelms-Universit¨atM¨unster(WWU) / Leibniz Universit¨atHannover / North-German Supercomputing Alliance (HLRN), Germany Marc Jansen, University of Applied Sciences Ruhr West, Deutschland Fahad Muhammad, CSTB, Sophia Antipolis, France Kiyoshi Nagata, Daito Bunka University, Japan Claus-Peter R¨uckemann, WWU M¨unster/ Leibniz Universit¨atHannover / HLRN, Germany INFOCOMP 2017: http://www.iaria.org/conferences2017/INFOCOMP17.html Program: http://www.iaria.org/conferences2017/ProgramINFOCOMP17.html -
A Survey of Top-Level Ontologies to Inform the Ontological Choices for a Foundation Data Model
A survey of Top-Level Ontologies To inform the ontological choices for a Foundation Data Model Version 1 Contents 1 Introduction and Purpose 3 F.13 FrameNet 92 2 Approach and contents 4 F.14 GFO – General Formal Ontology 94 2.1 Collect candidate top-level ontologies 4 F.15 gist 95 2.2 Develop assessment framework 4 F.16 HQDM – High Quality Data Models 97 2.3 Assessment of candidate top-level ontologies F.17 IDEAS – International Defence Enterprise against the framework 5 Architecture Specification 99 2.4 Terminological note 5 F.18 IEC 62541 100 3 Assessment framework – development basis 6 F.19 IEC 63088 100 3.1 General ontological requirements 6 F.20 ISO 12006-3 101 3.2 Overarching ontological architecture F.21 ISO 15926-2 102 framework 8 F.22 KKO: KBpedia Knowledge Ontology 103 4 Ontological commitment overview 11 F.23 KR Ontology – Knowledge Representation 4.1 General choices 11 Ontology 105 4.2 Formal structure – horizontal and vertical 14 F.24 MarineTLO: A Top-Level 4.3 Universal commitments 33 Ontology for the Marine Domain 106 5 Assessment Framework Results 37 F. 25 MIMOSA CCOM – (Common Conceptual 5.1 General choices 37 Object Model) 108 5.2 Formal structure: vertical aspects 38 F.26 OWL – Web Ontology Language 110 5.3 Formal structure: horizontal aspects 42 F.27 ProtOn – PROTo ONtology 111 5.4 Universal commitments 44 F.28 Schema.org 112 6 Summary 46 F.29 SENSUS 113 Appendix A F.30 SKOS 113 Pathway requirements for a Foundation Data F.31 SUMO 115 Model 48 F.32 TMRM/TMDM – Topic Map Reference/Data Appendix B Models 116 ISO IEC 21838-1:2019 -
May 28, 2016 3:46 WSPC/INSTRUCTION FILE Output
May 28, 2016 3:46 WSPC/INSTRUCTION FILE output International Journal of Semantic Computing c World Scientific Publishing Company Methods and Resources for Computing Semantic Relatedness Yue Feng Ryerson University Toronto, Ontario, Canada [email protected] Ebrahim Bagheri Ryerson University Toronto, Ontario, Canada [email protected] Received (Day Month Year) Revised (Day Month Year) Accepted (Day Month Year) Semantic relatedness (SR) is defined as a measurement that quantitatively identifies some form of lexical or functional association between two words or concepts based on the contextual or semantic similarity of those two words regardless of their syntactical differences. Section 1 of the entry outlines the working definition of semantic related- ness and its applications and challenges. Section 2 identifies the knowledge resources that are popular among semantic relatedness methods. Section 3 reviews the primary measurements used to calculate semantic relatedness. Section 4 reviews the evaluation methodology which includes gold standard dataset and methods. Finally, Section 5 in- troduces further reading. In order to develop appropriate semantic relatedness methods, there are three key aspects that need to be examined: 1) the knowledge resources that are used as the source for extracting semantic relatedness; 2) the methods that are used to quantify semantic relatedness based on the adopted knowledge resource; and 3) the datasets and methods that are used for evaluating semantic relatedness techniques. The first aspect involves the selection of knowledge bases such as WordNet or Wikipedia. Each knowledge base has its merits and downsides which can directly affect the accurarcy and the coverage of the semantic relatedness method. The second aspect relies on different methods for utilizing the beforehand selected knowledge resources, for example, methods that depend on the path between two words, or a vector representation of the word. -
Theory-Based Knowledge Acquisition for Ontology Development
UNIVERSITÄT HOHENHEIM Theory-based Knowledge Acquisition for Ontology Development A dissertation submitted to the Faculty of Business, Economics and Social Sciences of the University of Hohenheim in partial fulfillment of the requirements for the degree of Doctor of Economics (Dr. oec.) by Andreas Scheuermann July 2016 Principal advisor: Prof. Dr. Stefan Kirn Co-advisor: Prof. Dr. Ralph Bergmann Chair of the examination committee: Prof. Dr. Christian Ernst Dean: Prof. Dr. Dirk Hachmeister Date of oral examination: 7. July 2016 Abstract iii Abstract This thesis concerns the problem of knowledge acquisition in ontology development. Knowledge acquisition is essential for developing useful ontologies but it is a complex and error-prone task. When capturing specific knowledge about a particular domain of interest, the problem of knowledge acquisition occurs due to linguistic, cognitive, modelling, and methodical difficulties. For overcoming these four difficulties, this research proposes a theory-based knowledge acquisition method. By studying the knowledge base, basic terms and concepts in the areas of ontology, ontology development, and knowledge acquisition are defined. A theoretical analysis of knowledge acquisition identifies linguistic, cognitive, modelling, and methodical difficulties, for which a survey of 15 domain ontologies provides further empirical evidence. A review of existing knowledge acquisition approaches shows their insufficiencies for reducing the problem of knowledge acquisition. As the underpinning example, a description of the domain of transport chains is provided. Correspondingly, a theory in business economics, i.e. the Contingency Approach, is selected. This theory provides the key constructs, relationships, and dependencies that can guide knowledge acquisition in the business domain and, thus, theoretically substantiate knowledge acquisition. -
An Extensible Platform for Semantic Classification and Retrieval of Multimedia Resources
An extensible platform for semantic classification and retrieval of multimedia resources Paolo Pellegrino, Fulvio Corno Politecnico di Torino, C.so Duca degli Abruzzi 24, 10129, Torino, Italy {paolo.pellegrino, fulvio.corno}@polito.it Abstract and seem very promising, but are still mostly exploited for textual resources only. In effect, text is the simplest form This paper introduces a possible solution to the problem of knowledge representation which can be both of semantic indexing, searching and retrieving heterogeneous understood by humans and quickly processed by resources, from textual as in most of modern search engines, machines. So, while text itself is already efficaciously to multimedia. The idea of “anchor” as information unit is used as knowledge representation for machines, digital here introduced to view resources from different perspectives multimedia resources like images and videos cannot in and to access existing resources and metadata archives. general be used as they are for efficient classification and Moreover, the platform uses an ontology as a conceptual retrieval. Some intensive processing or manual metadata representation of a well-defined domain in order to creation is necessary to extract and associate semantic semantically classify and retrieve anchors (and the related information to a given multimedia resource. resources). Specifically, the architecture of the proposed platform aims at being as modular and easily extensible as In this context, the proposed work aims at providing a possible, in order to permit the inclusion of state-of-the-art flexible, lightweight and ready-to-use platform for the techniques for the classification and retrieval of multimedia semantic classification, search and retrieval of resources. -
Chaudron: Extending Dbpedia with Measurement Julien Subercaze
Chaudron: Extending DBpedia with measurement Julien Subercaze To cite this version: Julien Subercaze. Chaudron: Extending DBpedia with measurement. 14th European Semantic Web Conference, Eva Blomqvist, Diana Maynard, Aldo Gangemi, May 2017, Portoroz, Slovenia. hal- 01477214 HAL Id: hal-01477214 https://hal.archives-ouvertes.fr/hal-01477214 Submitted on 27 Feb 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Chaudron: Extending DBpedia with measurement Julien Subercaze1 Univ Lyon, UJM-Saint-Etienne, CNRS Laboratoire Hubert Curien UMR 5516, F-42023, SAINT-ETIENNE, France [email protected] Abstract. Wikipedia is the largest collaborative encyclopedia and is used as the source for DBpedia, a central dataset of the LOD cloud. Wikipedia contains numerous numerical measures on the entities it describes, as per the general character of the data it encompasses. The DBpedia In- formation Extraction Framework transforms semi-structured data from Wikipedia into structured RDF. However this extraction framework of- fers a limited support to handle measurement in Wikipedia. In this paper, we describe the automated process that enables the creation of the Chaudron dataset. We propose an alternative extraction to the tra- ditional mapping creation from Wikipedia dump, by also using the ren- dered HTML to avoid the template transclusion issue. -
Semantic Computing
SEMANTIC COMPUTING 10651_9789813227910_TP.indd 1 24/7/17 1:49 PM World Scientific Encyclopedia with Semantic Computing and Robotic Intelligence ISSN: 2529-7686 Published Vol. 1 Semantic Computing edited by Phillip C.-Y. Sheu 10651 - Semantic Computing.indd 1 27-07-17 5:07:03 PM World Scientific Encyclopedia with Semantic Computing and Robotic Intelligence – Vol. 1 SEMANTIC COMPUTING Editor Phillip C-Y Sheu University of California, Irvine World Scientific NEW JERSEY • LONDON • SINGAPORE • BEIJING • SHANGHAI • HONG KONG • TAIPEI • CHENNAI • TOKYO 10651_9789813227910_TP.indd 2 24/7/17 1:49 PM World Scientific Encyclopedia with Semantic Computing and Robotic Intelligence ISSN: 2529-7686 Published Vol. 1 Semantic Computing edited by Phillip C.-Y. Sheu Catherine-D-Ong - 10651 - Semantic Computing.indd 1 22-08-17 1:34:22 PM Published by World Scientific Publishing Co. Pte. Ltd. 5 Toh Tuck Link, Singapore 596224 USA office: 27 Warren Street, Suite 401-402, Hackensack, NJ 07601 UK office: 57 Shelton Street, Covent Garden, London WC2H 9HE Library of Congress Cataloging-in-Publication Data Names: Sheu, Phillip C.-Y., editor. Title: Semantic computing / editor, Phillip C-Y Sheu, University of California, Irvine. Other titles: Semantic computing (World Scientific (Firm)) Description: Hackensack, New Jersey : World Scientific, 2017. | Series: World Scientific encyclopedia with semantic computing and robotic intelligence ; vol. 1 | Includes bibliographical references and index. Identifiers: LCCN 2017032765| ISBN 9789813227910 (hardcover : alk. paper) | ISBN 9813227915 (hardcover : alk. paper) Subjects: LCSH: Semantic computing. Classification: LCC QA76.5913 .S46 2017 | DDC 006--dc23 LC record available at https://lccn.loc.gov/2017032765 British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library. -
Propositional Knowledge: Acquisition and Application to Syntactic and Semantic Parsing
Thesis for the Degree of Doctor of Philosophy 2017 Propositional Knowledge: Acquisition and Application to Syntactic and Semantic Parsing Bernardo Cabaleiro Barciela University Master's Degree in Languages and Computer Systems (National Distance Education University) Doctoral Programme in Intelligent Systems Academic advisor: Anselmo Peñas Padilla Associate Professor in the Languages and Computer Systems Department (National Distance Education University) Thesis for the Degree of Doctor of Philosophy 2017 Propositional Knowledge: Acquisition and Application to Syntactic and Semantic Parsing Bernardo Cabaleiro Barciela University Master's Degree in Languages and Computer Systems (National Distance Education University) Doctoral Programme in Intelligent Systems Academic advisor: Anselmo Peñas Padilla Associate Professor in the Languages and Computer Systems Department (National Distance Education University) A mi familia Acknowledgements Esta tesis pone punto final a mis estudios de doctorado, y, a la vez, supone el fin de una bonita etapa de mi vida. A lo largo de este tiempo me han acompañado muchas personas que han hecho que esta experiencia sea inolvidable. Estas líneas son para intentar agradecerles todo lo que han hecho por mí. En primer lugar, me gustaría mostrar mi agradecimiento a Anselmo Peñas por su dirección tanto del trabajo de fin de máster como de esta tesis doctoral. Durante todo este tiempo Anselmo ha puesto mucho empeño para formarme, no sólo en el campo del PLN sino también como investigador. Quiero agradecerle la libertad que me ha dado para trabajar en lo que me interesaba, y el especial cuidado que ha puesto en corregir y mejorar esos trabajos. Me gustaría dar las gracias también a mis compañeros en el departamento de lenguajes y sistemas, tanto a estuvieron como a los que están. -
AI/ML Finding a Doing Machine
Digital Readiness: AI/ML Finding a doing machine Gregg Vesonder Stevens Institute of Technology http://vesonder.com Three Talks • Digital Readiness: AI/ML, The thinking system quest. – Artificial Intelligence and Machine Learning (AI/ML) have had a fascinating evolution from 1950 to the present. This talk sketches the main themes of AI and machine learning, tracing the evolution of the field since its beginning in the 1950s and explaining some of its main concepts. These eras are characterized as “from knowledge is power” to “data is king”. • Digital Readiness: AI/ML, Finding a doing machine. – In the last decade Machine Learning had a remarkable success record. We will review reasons for that success, review the technology, examine areas of need and explore what happened to the rest of AI, GOFAI (Good Old Fashion AI). • Digital Readiness: AI/ML, Common Sense prevails? – Will there be another AI Winter? We will explore some clues to where the current AI/ML may reunite with GOFAI (Good Old Fashioned AI) and hopefully expand the utility of both. This will include extrapolating on the necessary melding of AI with engineering, particularly systems engineering. Roadmap • Systems – Watson – CYC – NELL – Alexa, Siri, Google Home • Technologies – Semantic web – GPUs and CUDA – Back office (Hadoop) – ML Bias • Last week’s questions Winter is Coming? • First Summer: Irrational Exuberance (1948 – 1966) • First Winter (1967 – 1977) • Second Summer: Knowledge is Power (1978 – 1987) • Second Winter (1988 – 2011) • Third Summer (2012 – ?) • Why there might not be a third winter! Henry Kautz – Engelmore Lecture SYSTEMS Winter 2 Systems • Knowledge is power theme • Influence of the web, try to represent all knowledge – Creating a general ontology organizing everything in the world into a hierarchy of categories – Successful deep ontologies: Gene Ontology and CML Chemical Markup Language • Indeed extreme knowledge – CYC and Open CYC – IBM’s Watson Upper ontology of the world Russell and Norvig figure 12.1 Properties of a subject area and how they are related Ferrucci, D., et.al. -
Conceptualization and Visual Knowledge Organization: a Survey of Ontology-Based Solutions
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Repository of the Academy's Library CONCEPTUALIZATION AND VISUAL KNOWLEDGE ORGANIZATION: A SURVEY OF ONTOLOGY-BASED SOLUTIONS A. Benedek1, G. Lajos2 1 Hungarian Academy of Sciences (HUNGARY) 2 WikiNizer (UNITED KINGDOM) Abstract Conceptualization and Visual Knowledge Organization are overlapping research areas of Intellect Augmentation with hot topics at their intersection which are enhanced by proliferating issues of ontology integration. Knowledge organization is more closely related to the content of concepts and to the actual knowledge items than taxonomic structures and ‘ontologies’ understood as “explicit specifications of a conceptualization” (Gruber). Yet, the alignment of the structure and relationship of knowledge items of a domain of interest require similar operations. We reconsider ontology-based approaches to collaborative conceptualization from the point of view of user interaction in the context of augmented visual knowledge organization. After considering several formal ontology based approaches, it is argued that co-evolving visualization of concepts are needed both at the object and meta-level to handle the social aspects of learning and knowledge work in the framework of an Exploratory Epistemology. Current systems tend to separate the conceptual level from the content, and the context of logical understanding from the intent and use of concepts. Besides, they usually consider the unification of individual contributions in a ubiquitous global representation. As opposed to this God's eye view we are exploring new alternatives that aim to provide Morphic-like live, tinkerable, in situ approaches to conceptualization and its visualization and to direct manipulation based ontology authoring capabilities. -
D2.9 Dissemination Plan Version C
Ref. Ares(2019)2902574 - 30/04/2019 C3-Cloud “A Federated Collaborative Care Cure Cloud Architecture for Addressing the Needs of Multi-morbidity and Managing Poly-pharmacy” PRIORITY Objective H2020-PHC-25-2015 - Advanced ICT systems and services for integrated care D2.9 Dissemination Plan version c Work Package: WP2 Dissemination, Exploitation and Innovation Related Activities” Due Date: 30 April 2019 Actual Submission Date: 30 April 2019 Project Dates: Project Start Date: 01 May 2016 Project End Date: 30 April 2020 Project Duration: 48 months Deliverable Leader: EuroRec Project funded by the European Commission within the Horizon 2020 Programme (2014-2020) Dissemination Level PU Public CO Confidential, only for members of the consortium (including the Commission Services) X EU-RES Classified Information: RESTREINT UE (Commission Decision 2005/444/EC) EU-CON Classified Information: CONFIDENTIEL UE (Commission Decision 2005/444/EC) EU-SEC Classified Information: SECRET UE (Commission Decision 2005/444/EC) Document History: Version Date Changes From Review V0.1 20-04-2019 Initial document, most of content EuroRec - V0.2 27-04-2019 Further partner inputs plus web site EuroRec All screenshots V0.3 29-04-2019 Further review and addition of new WARWICK - material V1.0 30-04-2019 Final checks and editing by the WARWICK Coordinating team Contributors Geert Thienpont (EuroRec), Sarah N. Lim Choi Keung (WARWICK), (Beneficiary) Theodoros N. Arvanitis (WARWICK), George Despotou (WARWICK), Marie Sherman (RJH), Marie Beach (SWFT), Veli Stroetmann (empirica), Malte von Tottleben (empirica), Gokce Banu Laleci Erturkmen (SRDC), Mustafa Yuksel (SRDC), Göran Ekestubbe (CAMBIO), Mattias Fendukly (CAMBIO), Pontus Lindman (MEDIXINE), Dolores Verdoy (KG/OSAKI), Esteban de Manuel Keenoy (KG/OSAKI), Lamine Traore (INSERM), Marie- Christine Jaulent (INSERM) Responsible Author Dipak Kalra Email [email protected] Beneficiary EuroRec D2.9 Version 1.0, dated 30 April 2019 Page 2 of 71 TABLE OF CONTENTS 1.