DOCSLIB.ORG

United States Patent 19 11 Patent Number: 6,076,088 Paik Et Al

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
United States Patent 19 11 Patent Number: 6,076,088 Paik Et Al USOO6076088A United States Patent 19 11 Patent Number: 6,076,088 Paik et al. 45) Date of Patent: Jun. 13, 2000 54 INFORMATION EXTRACTION SYSTEM Liddy, Elizabeth D. et al., “An Overview of DR-LINK and AND METHOD USING CONCEPT Its Approach to Document Filtering.” Proceedings of the RELATION CONCEPT (CRC) TRIPLES ARPA Workshop On Human Language Technology, Princ eton, NJ, Mar. 21–24, 1993, pp. 358-362. 76 Inventors: Woojin Paik, 405 Crawford Ave., Syracuse, N.Y. 13224; Elizabeth D. Liddy, Elizabeth D. et al., “Development, Implementation Liddy, 104 Victoria Pl.; Jennifer and Testing of a Discourse Model for Newspaper Texts,” Heverin Liddy, 252 Buckingham Ave., ARPA Workshop On Human Language Technology, Princ both of Syracuse, N.Y. 13210; Ian eton, NJ, Mar. 21-24, 1993, pp. 1-6. Harcourt Niles, Nettleton Commons, Liddy, Elizabeth D., “DR-LINK: A System Update for 313 E. Willow St., Syracuse, N.Y. TREC -2,” Proceedings of Second Text Retrieval Confer 13203; Eileen E. Allen, 4 Dwight Ave., ence (TREC-2), National Inst. of Standards and Technology, Clinton, N.Y. 13323 Aug. 31-Sep. 2, 1993, pp. 1-15. Liddy, Elizabeth D., “Development and Implementation of 21 Appl. No.: 08/795,658 a Discourse Model for Newspaper Texts.” Proceedings of 22 Filed: Feb. 6, 1997 the AAAI Symposium On Empirical Methods in Discourse Interpretation and Generation, Stanford, CA, Dec. 14-17, Related U.S. Application Data 1993, pp. 1-6. 60 Provisional application No. 60/011,369, Feb. 9, 1996, and provisional application No. 60/015,512, Apr. 16, 1996. Liddy, Elizabeth DuRoss, “An Alternative Representation for Documents and Queries,” Proceedings of the 14th (51) Int. Cl. ................................................ G06F 17/30 National Online Meeting, 1993, pp. 279-284. 52 U.S. Cl. ................................................................... 707/5 58 Field of Search ..................................... 707/3, 4, 5, 6 (List continued on next page.) 56) References Cited U.S. PATENT DOCUMENTS Primary Examiner Jack M. Choules Attorney, Agent, or Firm Townsend and Townsend and 5,148,541 9/1992 Lee et al. .................................... 707/2 Crew LLP 5,175.814 12/1992 Anicket al. ...... 345/384 5,301,109 4/1994 Landauer et al. ... 704/9 57 ABSTRACT 5,386,556 1/1995 Hedin et al. ...... ... 707/4 5,418,948 5/1995 Turtle ........ ... 707/4 An information extraction System that allows users to ask 5,418,951 5/1995 Damashek ................................... 707/5 questions about documents in a database, and responds to queries by returning possibly relevant information which is OTHER PUBLICATIONS extracted from the documents. The System is domain Liddy et al. “DR-LINKSystem: Phase I Summary” Tipster: independent, and automatically builds its own Subject Proceedings of the First Workshop, confernce date Sep., knowledge base. It can be applied to any new corpus of text 1933, pp. 93-112, Apr. 1994. with quick results, and no requirement for lengthy manual Liddy et al. “DR-LINK Linguistic-Conceptual Approach to input. For this reason, it is also a dynamic System which can Document Detection”, TREC-1 pp. 113–130, Nov. 1992. acquire new knowledge and add it to the knowledge base Croft Bruce et al., “Applications of Multilingual Text immediately by automatically identifying new names, Retrieval,” Proceedings of the 29th Annual Hawaii Inter events, or concepts. national Conference On System Sciences, 1996, Vol. 5, pp. 98-107. 43 Claims, 12 Drawing Sheets - 110 15 Raw Document CRC CRCOKR KR Documents Processing Extraction Translation Database - 102 ( Of 112 External External CRC Extraction KR Conceptual Knowledge Extraction Rule Format Hierarchy Base Base Guideline 100' 105 110 Query KR to Query Query CRC CRC to KR Document KR Processing Extraction Translation Similarity Measure 20 Query 27 External Proper NameDatabase 6,076,088 Page 2 OTHER PUBLICATIONS Liddy, Elizabeth D., “The Promise of Natural Language Processing for Competitive Intelligence,” Proceedings of Liddy, Elizabeth D. et al., “Text Categorization for Multiple Users Based on Semantic Features from a Machine-Read 10th International Conference of the Society of Competitive able Dictionary,” ACM Transactions on Information Sys Intelligence Professionals, May 4-5, 1995, pp. 328-342. tems, vol. 12, No. 3, Jul. 1994, pp. 278-295. Paik, Woojin et al., “Interpretation of Proper Nouns for Liddy, Elizabeth D. et al., “Document Retrieval Using Information Retrieval,” Proceedings of the ARPA Workshop Linguistic Knowledge,” Proceedings of the RIAO '94 Con On Human Language Technology Princeton, NY, Mar. 21-24, ference, Oct. 11-13, 1994, pp. 106-114. 1993, pp. 1–5. Liddy, Elizabeth et al., “Detection, Generation, and Expan Paik, Woojin et al., “Categorizing and Standardizing Proper sion of Complex Nominals.” Proceedings of the Workshop Nouns for Efficient Information Retrieval,” Corpus Process On Compound Nouns. Multilingual Aspects of Nominal ing for Lexicon Acquisition, MIT Press, Cambridge, MA, Composition, Dec. 2-3, 1994, Geneva, Switzerland, pp. 1995 (Boguraev, B. (ed)) pp. 1-10. 14-18. Liddy, Elizabeth D., “Development and Implementation of Paik, Woojin, Chronological Information Extraction System a Discourse Model for NewspaperTexts, Proceedings of the (CIES), Proceedings of the Dagstuhl On Summarizing Text ARPA Workshop on Human Language Technology, Princ for Intelligent Communication, Saarbruken, Germany, 1995, eton, NJ, Mar. 21–24, 1995, pp. 80-84. pp. 1-5. Liddy, Elizabeth et al., “A Natural Language Text Retrieval Wiener, Michael L. et al., “Intelligent Text Processing, and System With Relevance Feedback,” Proceedings of the 16th Intelligence Tradecraft,” The Journal of Association for National Online Meeting, May 2-6, 1995, pp. 259-261. Global Strategic Intelligence (AGSI), Jul. 1995, pp. 1-8. U.S. Patent Jun. 13, 2000 Sheet 1 of 12 6,076,088 DOCument Database and ASSOCiated Data (KRDatabase) 55 Query and DOCument Query-DOCument Processing Engines Similarity Measurer (CRC Extraction, CRC-KR Translation) 70 User Interface SW: Feedback ACCept Queries Provide Feedback ReSOurces for Ya ACCept Feedback Document & Query Reformulate Queries Processing Output Results Program and Data Storage 45 MOcemS and Processor(s) User interface NetWOrk input & Output Interface Devices SERVER Modem Or 30' User InterfaCe NetWOrk Program and input & Output interface Devices Processor(s) Data Storage Other Clients 10- FIG. U.S. Patent Jun. 13, 2000 Sheet 2 of 12 6,076,088 9 /| 99 ?uÐUn000 Áueno U.S. Patent Jun. 13, 2000 Sheet 4 of 12 6,076,088 OZZ /02 ZOZ 003 U.S. Patent Jun. 13, 2000 Sheet S of 12 6,076,088 U.S. Patent Jun. 13, 2000 Sheet 6 of 12 6,076,088 Z|| U.S. Patent Jun. 13, 2000 Sheet 8 of 12 6,076,088 DE Netscape: Chess Demo HomePage ED KY * Location: http://lucifer:8080/clar9.html What's New? What's Cool? Handbook NetSearch Net Directory Software Proper Noun Expansion/Clarification Word Senses X Rupert Murdoch X financial interest in something: "a stake in Bill Murdoch the company's future" O apole or stake set up to mark Something Charles Murdoch (as the start of a race track) William F. Murdoch a vertical post that a victim is tied to for HutchVision execution by burning X HutchVision O the money risked on a gamble HutchVision LTD HutchVision Channel Services Point in Time CHESS Request Preferences (CHRPS) Past O Analytical Information Present Cause/Effect Dimension Future Attributed Quotations O Predictions U.S. Patent Jun. 13, 2000 Sheet 9 of 12 6,076,088 U.S. Patent Jun. 13, 2000 Sheet 10 of 12 6,076,088 U.S. Patent Jun. 13, 2000 Sheet 11 of 12 6,076,088 O CN se - 3 N 6,076,088 1 2 INFORMATION EXTRACTION SYSTEM tion access Systems. Such as data mining Systems have AND METHOD USING CONCEPT become commercially available; however, until the present RELATION CONCEPT (CRC) TRIPLES invention, domain-independent question-answering Systems Still exist only as experimental prototypes. CROSS-REFERENCE TO RELATED Both types of Systems require a pre-constructed repository APPLICATIONS of information to find answers to users’ questions. Data This application claims priority from, and is a mining Systems commonly utilize Statistical procedures to continuation-in-part of the following provisional detect patterns in data, users are expected to interpret the applications, the disclosures of which are hereby incorpo patterns to find the answers. The current interests and Successful commercial uses of data mining Systems are due rated by reference: to the premise that these Systems are designed to use the Patent application Ser. No. 60/011,369, filed Feb. 9, 1996, Same Set of data which is already used by the legacy entitled “CHRONOLOGICAL INFORMATION EXTRAC database management Systems. TION SYSTEM (CIES),” to Woojin Paik; and In comparison, question-answering Systems are designed Patent application Ser. No. 60/015,512, filed Apr. 16, 15 to provide answers directly to users as if they were involved 1996, entitled “INFORMATION RETRIEVAL SYSTEM in question-answering Sessions with other people. This AND METHOD USING CONCEPT-RELATION requires Systems to perform complex inferencing to draw CONCEPT TRIPLES,” to Woojin Paik and Elizabeth D. answers from organized knowledge bases. Over the years, Liddy. there has been Significant progreSS in the problem-Solving The following applications are also hereby incorporated aspect of AI research; however, there are no practical AI by reference: applications except the ones which are used in a few Patent application Ser. No. 08/696,701, filed Aug. 14, narrowly-defined domains. This is due to the lack of prac 1996, entitled “MULTILINGUAL DOCUMENT tical knowledge bases. Research has demonstrated that RETRIEVAL SYSTEMAND METHOD USING SEMAN building the requisite knowledge bases automatically is TIC VECTORMATCHING,” to Elizabeth D. Liddy, Woojin 25 extremely time consuming and expensive. Paik, Edmund S. Yu, and Ming Li (Attorney Docket No. For a number of years, both manual and automatic 17704-2.00); approaches to constructing knowledge bases have been Patent application Ser.
Load more

Recommended publications
  • 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
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Oracle White Paper May 19Th, 2015
    An Oracle White Paper May 19th, 2015 Oracle Metadata Management v12.1.3.0.2 New Features Overview Oracle Metadata Management version 12.1.3.0.2 – May 19th, 2015 New Features Overview Disclaimer This document is for informational purposes. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described in this document remains at the sole discretion of Oracle. This document in any form, software or printed matter, contains proprietary information that is the exclusive property of Oracle. This document and information contained herein may not be disclosed, copied, reproduced, or distributed to anyone outside Oracle without prior written consent of Oracle. This document is not part of your license agreement nor can it be incorporated into any contractual agreement with Oracle or its subsidiaries or affiliates. 1 Oracle Metadata Management version 12.1.3.0.2 – May 19th, 2015 New Features Overview Table of Contents Executive Overview ............................................................................ 3 Oracle Metadata Management 12.1.3.0.2 .......................................... 4 Data Model Diagram Visualizer ...................................................... 4 HTML5 redesign ..................................................................................... 4 New interactive search ........................................................................... 5 New diagram auto
    [Show full text]
  • Emergent Computing Joint ERCIM Actions: ‘Beyond the Horizon’ Anticipating Future and Emerging Information Society Technologies
    European Research Consortium for Informatics and Mathematics Number 64, January 2006 www.ercim.org Special: Emergent Computing Joint ERCIM Actions: ‘Beyond the Horizon’ Anticipating Future and Emerging Information Society Technologies European Scene: Routes to Open Access Publishing CONTENTS JOINT ERCIM ACTIONS THE EUROPEAN SCENE 4 Fourth ERCIM Soft Computing Workshop The Routes to Open Access by Petr Hajek, Institute of Computer Science, Academy of 16 Open Access: An Introduction Sciences / CRCIM, Czech Republic by Keith G Jeffery, Director IT, CCLRC and ERCIM president 4 Cor Baayen Award 2006 18 Publish or Perish — Self-Archive to Flourish: The Green Route to Open Access 5 Second ERCIM Workshop ‘Rapid Integration of Software by Stevan Harnad, University of Southampton, UK Engineering Techniques’ by Nicolas Guelfi, University of Luxembourg 19 The Golden Route to Open Access by Jan Velterop 5 Grid@Asia: European-Asian Cooperation Director of Open Access, Springer in Grid Research and Technology by Bruno Le Dantec , ERCIM Office 20 ERCIM Statement on Open Access 6 GFMICS 2005 — 10th International Workshop on Formal 21 Managing Licenses in an Open Access Community Methods for Industrial Critical Systems by Renato Iannella National ICT Australia by Mieke Massink and Tiziana Margaria 22 W3C at the Forefront of Open Access Beyond-The-Horizon Project by Rigo Wenning, W3C 7 Bits, Atoms and Genes Beyond the Horizon 23 Cream of Science by Dimitris Plexousakis, ICS-FORTH, Greece by Wouter Mettrop, CWI, The Netherlands 8 Thematic Group 1: Pervasive Computing and Communications SPECIAL THEME: by Alois Ferscha, University of Linz, Austria EMERGENT COMPUTING 24 Introduction to the Special Theme 9 Thematic Group 2: by Heather J.
    [Show full text]
  • Semantic Web OWL RDF and SPARQL
    Searching The Semantic Web Lecturer: Kyumars Sheykh Esmaili Semantic Web Research Laboratory Computer Engineering Department Sharif University of Technology Fall 2005 Table of Content Introduction Semantic web Search Engines Ontology Search Engines Meta Ontology Search Engines Crawler Based Ontology Search Engines Semantic Search Engines Context Based Search Engines Semantic Annotation Evolutionary Search Engines Semantic Association Discovery Engines Discussion and Evaluation References Semantic Web Research LaboratorySearching The Semantic Web Sharif University of Technology 2 Table of Content Introduction Semantic web Search Engines Ontology Search Engines Meta Ontology Search Engines Crawler Based Ontology Search Engines Semantic Search Engines Context Based Search Engines Semantic Annotation Evolutionary Search Engines Semantic Association Discovery Engines Discussion and Evaluation References Semantic Web Research LaboratorySearching The Semantic Web Sharif University of Technology 3 Before and After ? Semantic Web Research LaboratorySearching The Semantic Web Sharif University of Technology 4 Semantic Web Terminology A Term is a non-anonymous RDF resource which is the URI reference of either a class or a property. rdfs:Class foaf:Person An Individual refers to a non-anonymous RDF resource which is the URI reference of a class member. foaf:Person http://.../foaf.rdf#finin An Ontology contains mostly term definition (i.e. classes and properties). It corresponds to T-Box in Description Logic. An Annotation contains mostly class
    [Show full text]
  • Knowledge Acquisition Framework from Unstructured Biomedical Knowledge Sources Demeke Asres Ayele
    Knowledge Acquisition Framework from Unstructured Biomedical Knowledge Sources Demeke Asres Ayele To cite this version: Demeke Asres Ayele. Knowledge Acquisition Framework from Unstructured Biomedical Knowledge Sources. Information Retrieval [cs.IR]. Université d’Addis Abeba, 2016. English. tel-02087577 HAL Id: tel-02087577 https://tel.archives-ouvertes.fr/tel-02087577 Submitted on 18 Oct 2019 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. ADDIS ABABA UNIVERSITY SCHOOL OF GRADUATE STUDIES KNOWLEDGE ACQUISITION FRAMEWORK FROM UNSTRUCTURED BIOMEDICAL KNOWLEDGE SOURCES DEMEKE ASRES AYELE A THESIS SUBMITTED TO IT DOCTORAL PROGRAM ADDIS ABABA UNIVERSITY PRESENTED IN FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY IN INFORMATION TECHNOLOGY (LANGUAGE TECHNOLOGY) Jury: Prof. Luciano Serafìni External Examiner Dr. Solonron Tefera Internal Examiner Dr. Jean-Pierre Chevallet Supervisor Dr. Getnet Mitikie Co-Supervisor Dr. Milion Mcshesha Co-Supervisor Dr. Dida Midekso Chairman ADDIS ABABA, ETHIOPIA 9 August 2016 Knowledge Acquisition
    [Show full text]
  • Discovering Web Services Using Behavioural Constraints and Ontology
    Discovering Web Services Using Behavioural Constraints and Ontology Natenapa Sriharee and Twittie Senivongse Department of Computer Engineering Chulalongkorn University Phyathai Road, Pathumwan, Bangkok 10330 Thailand Tel. +66 2 2186991 Fax. +66 2 2186955 [email protected], [email protected] Abstract. The ability to locate useful on-line Web Services is becoming critical for today’s service-oriented business applications. A number of efforts have been put to enhance the service discovery process by using conceptualised knowledge, called ontology, of particular service domains to describe service characteristics. This paper presents an ontology-based approach to enhance descriptions of Web Services that are expressed in WSDL with ontology-based behavioural information, i.e. input, conditional/unconditional output, precondition, and conditional/unconditional effect of the services. Having a service ontology associated with each Web Service description, queries for services based on behavioural constraints can benefit from inferring semantics of the service from the service ontology. The service discovery process becomes closer to discovery by service semantics or behaviour, in contrast with discovery by matching of service attributes values – the mechanism that is supported currently by Web Services. 1 Introduction Web Services are networked applications that are able to interact using standard application-to-application Web protocols over well-defined interfaces [1]. Standard Web Service architecture provides UDDI as a registry for service providers to advertise information about themselves and their services so that service consumers can search for the required providers and services [2]. The information model in UDDI roughly defines attributes that describe the service provider (i.e.
    [Show full text]
  • Matching Ontologies for Context: the Neon Alignment Plug-In
    Matching ontologies for context: The NeOn Alignment plug-in Chan Le Duc, Mathieu d’Aquin, Jesús Barrasa, Jérôme David, Jérôme Euzenat, Raul Palma, Rosario Plaza, Marta Sabou, Boris Villazón-Terrazas To cite this version: Chan Le Duc, Mathieu d’Aquin, Jesús Barrasa, Jérôme David, Jérôme Euzenat, et al.. Matching ontologies for context: The NeOn Alignment plug-in. [Contract] 2008, pp.59. hal-00793546 HAL Id: hal-00793546 https://hal.inria.fr/hal-00793546 Submitted on 22 Feb 2013 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. NeOn-project.org NeOn: Lifecycle Support for Networked Ontologies Integrated Project (IST-2005-027595) Priority: IST-2004-2.4.7 — “Semantic-based knowledge and content systems” D3.3.2: Matching ontologies for context: The NeOn Alignment plug-in Deliverable Co-ordinator: Chan Le Duc Deliverable Co-ordinating Institution: INRIA Other Authors: Mathieu d’Aquin (OU), Jesus Barrasa (UPM), Jérôme David (INRIA), Jérôme Euzenat (INRIA), Raul Palma (UPM), Rosario Plaza (UPM), Marta Sabou (OU), Boris Villazón-Terrazas (UPM) This deliverable presents the software support provided by the NeOn toolkit for matching ontolo- gies, and in particular, recontextualise them. This support comes through the NeOn Alignment plug-in which integrate the Alignment API and offer access to Alignment servers in the NeOn toolkit.
    [Show full text]
  • Wissensorganisation Und -Repräsentation Mit Digitalen Technologien
    Zurich Open Repository and Archive University of Zurich Main Library Strickhofstrasse 39 CH-8057 Zurich www.zora.uzh.ch Year: 2014 Wissensorganisation und -repräsentation mit digitalen Technologien Edited by: Keller, Stefan A ; Schneider, René ; Volk, Benno Abstract: Im Sammelband werden die sehr unterschiedlichen konzeptionellen und technologischen Ver- fahren zur Modellierung und digitalen Repräsentation von Wissen in Wissensorganisationen (Hochschulen, Forschungseinrichtungen und Bildungsinstitutionen) sowie in Unternehmen anhand von praxisorientierten Beispielen in einer Zusammenschau vorgestellt. Dabei werden sowohl grundlegende Modelle der Organisa- tion von Wissen als auch technische Umsetzungsmöglichkeiten sowie deren Grenzen und Schwierigkeiten in der Praxis insbesondere in den Bereichen der Wissensrepräsentation und des Semantic Web ausgelotet. Good practice Beispiele und erfolgreiche Anwendungsszenarien aus der Praxis bieten dem Leser einen Wis- sensspeicher sowie eine Anleitung zur Realisierung von eigenen Vorhaben. Folgende Themenfelder werden in den Beiträgen behandelt: Hypertextbasiertes Wissensmanagement, digitale Optimierung der erprobten analogen Technologie des Zettelkastens, innovative Wissensorganisation mittels Social Media, Such- prozessvisualisierung für Digitale Bibliotheken, semantische Event- und Wissensvisualisierung, ontologis- che Mindmaps und Wissenslandkarten, intelligente semantische Wissensverarbeitungssysteme, Grundla- gen der computergestützten Wissensorganisation und -integration, das Konzept von Mega-Regions
    [Show full text]