DOCSLIB.ORG

Automated Development of Semantic Data Models Using Scientific Publications Martha O

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
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. All through my childhood, I spent most of the time with my grandmother: Adela, who with proverbs and a kind smile revealed her honest, empathic, trustworthy, and wise persona to me. My mother, Clara, was always there for any people in need, and her personality full of cheer and encourage was contagious. She loved and enjoyed life deeply, while being resourceful, ethical, tolerant, and responsible under any circumstances. iii Acknowledgments First and foremost, I am profoundly thankful to my advisor and co-advisor Dr. Trilce Estrada and Dr. Soraya Abad-Mota, who appeared in my life when I truly needed academic guidance. Their ideas and human values inspire me to keep working in science. In addition, I sincerely thank the other two professors in my dissertation committee, Dr. Abdullah Mueen and Dr. Sarah Stith. In particular, the chair of this dissertation committee, Dr. Trilce Estrada, supported me with her valuable expertise and constant recommendations at each stage of the development of this investigation, from the search of this topic, to implementation, alignment of ideas with writing, and completion. The co-chair of this dissertation committee, Dr. Soraya Abad-Mota, always presented me relevant recommendations, positive encouragement, and constructive comments of every aspect of this work, from inception to conclusion. Dr. Abdullah Mueen provided me useful feedback and suggestions to improve my presentations and dissertation work, and Dr. Sarah Stith contributed with fruitful conversations about my dissertation research, writing, and job search. I give special thanks to Dr. Thomas Preston Caudell for teaching me the fundamentals on research work and the wonderful world of Neural Networks, as well as for being an exceptional advisor during my master’s degree. I express thanks to Dr. Dorian Arnold for his support and mentorship while searching for an advisor to complete my degree. I acknowledge the Visualization and Data Science Laboratories, whose members gave me friendship and useful feedback for research and presentations: Shan, Takeshi, Victor, Cameron, Jacob, Jeremy, Matt, Xin Yu, and Rudy. I thank Haleh, who collected the last dataset used in this work. I am grateful to my study partners for the comprehensive examination, particularly, Josh and Joe. I thank all my professors and classmates, especially those who helped me to learn either about Computer Science or about life. iv I recognize the National Council of Science and Technology (Consejo Nacional de Ciencia y Tecnología) for funding resources at the beginning of my studies and for strengthening the scientific investigation in Mexico. I am appreciative of the collaboration with all the people I had the opportunity to work at the Sandia National Laboratories, where I developed more problem solving and programming skills for issues in the field of Bioinformatics and Data Management. I appreciate two extraordinary women who appeared along my path during a search for tools to improve myself, Dr. Susan Cooper for beneficial observations on writing and presenting technical information; and Mable Orndorff-Plunkett for being a supportive mentor, helping with the search of an editor, and giving me excellent pointers for presentations as well. I am deeply thankful to my family and friends, especially to my daughter and husband for their patience and understanding on my continuous search of knowledge, equality, and justice. Tania Saraí was the major motivation to start this particular journey and shared her time with my studies. Aaron Andrew gave me the encouragement, strength, and emotional support to complete this important chapter in our pilgrimage. I express gratitude to Edmundo because he always encouraged me to pursue my passion for science and challenged me to search for the truth of life. I am grateful to the rest of my family that even in the distance supported me unconditionally: Arturo, Ernesto, Guadalupe, Joaquín, Meg, Nicholas, Norma, Patricia, Robert, Sayward, Sergio, Trinidad, and Verónica. Finally, I have gratitude to my dear friends Anne, Claudia, Elisa, Mariela, Mercedes’ daughter, and Mercedes’ mom, who gave me cheerful moments and brightened my path with their presence. v AUTOMATED DEVELOPMENT OF SEMANTIC DATA MODELS USING SCIENTIFIC PUBLICATIONS by MARTHA O. PEREZ-ARRIAGA M.S., Computer Science, University of New Mexico, 2008 Ph.D., Computer Science, University of New Mexico, 2018 Abstract The traditional methods for analyzing information in digital documents have evolved with the ever-increasing volume of data. Some challenges in analyzing scientific publications include the lack of a unified vocabulary and a defined context, different standards and formats in presenting information, various types of data, and diverse areas of knowledge. These challenges hinder detecting, understanding, comparing, sharing, and querying information rapidly. I design a dynamic conceptual data model with common elements in publications from any domain, such as context, metadata, and tables. To enhance the models, I use related definitions contained in ontologies and the Internet. Therefore, this dissertation generates semantically-enriched data models from digital publications based on the Semantic Web principles, which allow people and computers to work cooperatively. Finally, this work uses a vocabulary and ontologies to generate a structured characterization and organize the data models. This organization allows integration, sharing, management, and comparing and contrasting information from publications. vi Specifically, this work develops automated methods for Information Extraction, Semantic Analytics, Information Modeling, and Information Retrieval. I research how to deal with the diversity of tabular and document layouts, and detect, ex- tract, and organize information from tables embedded in digital publications using an adaptive method. To understand relevant information in documents, I interpret and enrich tables, disambiguating concepts and finding unique definitions from a general ontology and the Internet with an unsupervised learning method, while keeping the provenance information for each publication. Furthermore, I apply Natural Language Processing methods to discover and extract semantic relationships from text with non-standard vocabulary and various writing styles. Applying these methods to a variety of digital publications, I formally characterize semantic data models in a machine-readable format. These models allow us to create a network of publications to integrate, manage, and analyze information not only in a specific domain, but also among disciplines. vii Contents List of Figures xii List of Tables xiv Glossary xv 1 Introduction 1 1.1 Overview . 2 1.2 Thesis and Summary of Contributions . 7 1.2.1 Contributions . 8 1.2.2 Development of Methods for Semantic Data Models . 9 2 Literature Review 12 2.1 Table Analysis . 12 2.2 Semantic Relationships . 18 2.3 Semantic Data Integration . 20 viii Contents 3 Table Detection and Information Extraction 25 3.1 Discovery of Table Information . 26 3.2 Table Identification . 27 3.2.1 Document Conversion . 29 3.2.2 Table Detection . 32 3.2.3 Table Extraction . 34 3.3 Evaluation of Table Identification . 41 3.3.1 Baseline and Datasets of Table Identification . 41 3.3.2 Results and Discussion of Table Identification . 42 4 Table Interpretation to Synthesize Documents 46 4.1 Understanding Tables and Documents . 46 4.2 The Semantic Web and Sources of Knowledge . 48 4.3 Table Interpretation . 51 4.3.1 Discovery of Metadata and Context . 53 4.3.2 Entity Recognition, Annotation, and Disambiguation . 56 4.3.3 Discovery of Relationships . 60 4.3.4 Working Example to Synthesize a Publication . 68 4.4 Evaluation of Table Interpretation . 73 4.4.1 Dataset and Experiments of Table Interpretation
Load more

Recommended publications
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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].
    [Show full text]
  • Improving Data Identification and Tagging for More Effective Decision Making in Agriculture Pascal Neveu, Romain David, Clement Jonquet
    Improving data identification and tagging for more effective decision making in agriculture Pascal Neveu, Romain David, Clement Jonquet To cite this version: Pascal Neveu, Romain David, Clement Jonquet. Improving data identification and tagging for more effective decision making in agriculture. Leisa Armstrong. Improving Data Management and Decision Support Systems in Agriculture, 85, Burleigh Dodds Science Publishing, 2020, Series in Agricultural Science, 978-1-78676-340-2. hal-02164149 HAL Id: hal-02164149 https://hal.archives-ouvertes.fr/hal-02164149 Submitted on 11 May 2020 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. Improving data identification and tagging for more effective decision making in agriculture Pascal Neveu and Romain David, INRA, France and Clement Jonquet, LIRMM, France Abstract Data integration, data analytics and decision support methods can help increase agriculture challenges such as climate change adaptation or food security. In this context, smart data acquisition systems, interoperable information systems and frameworks for data structuring are required. In this chapter we describe methods for data identification and provide some recommendations. We also describe how to enrich data with semantics and a way to tag data with the relevant ontology.
    [Show full text]
  • Big Data in the Healthcare System: a Synergy with Artificial Intelligence
    Journal of Integrative Bioinformatics 2021; aop Review article Reyes-González Juan Pablo, Díaz-Peregrino Roberto, Soto-Ulloa Victor, Galvan-Remigio Isabel, Castillo Paul and Ogando-Rivas Elizabeth* Big data in the healthcare system: a synergy with artificial intelligence and blockchain technology https://doi.org/10.1515/jib-2020-0035 Received November 5, 2020; accepted July 23, 2021; published online August 18, 2021 Abstract: In the last decades big data has facilitating and improving our daily duties in the medical research and clinical fields; the strategy to get to this point is understanding how to organize and analyze thedatain order to accomplish the final goal that is improving healthcare system, in terms of cost and benefits, quality of life and outcome patient. The main objective of this review is to illustrate the state-of-art of big data in healthcare, its features and architecture. We also would like to demonstrate the different application and principal mechanisms of big data in the latest technologies known as blockchain and artificial intelligence, recognizing their benefits and limitations. Perhaps, medical education and digital anatomy are unexplored fields that might be profitable to investigate as we are proposing. The healthcare system can be revolutionized using these different technologies. Thus, we are explaining the basis of these systems focused to the medical arena in order to encourage medical doctors, nurses, biotechnologies and other healthcare professions to be involved and create a more efficient and efficacy
    [Show full text]
  • Book Chapter: a Review of Tools for Iot Semantics and Data Streaming
    6 A Review of Tools for IoT Semantics and Data Streaming Analytics Martin Serrano and Amelie Gyrard Insight Center for Data Analytics, National University of Ireland, Galway, Ireland 6.1 Introduction The Internet of Things is having a fast adoption in the industry and with no doubt, in today’s increasingly competitive innovation-driven market- place, individuals need skills to transform the growing amount of industry, product, and customer (behavior) data into actionable information to support strategic and tactical decisions at the organisations. The Internet of Things (Devices) and the web are every day more and more crucial elements in this data generation, consolidating the need for interpreting data produced by those devices or “things”, physical or virtual, connected to the Internet. In the other hand analytics is a time-consuming process and constantly redesigned in every application domain. The identification of methods and tools to avoid that data analytics over IoT Data is every time re-designed is a constant need. In this chapter, a review of tools for IoT data analytics is reviewed. We provide an overall vision on best practices and trends to perform analytics, we address innovative approaches using semantics to facilitate the integration of different sources of information [1]. The Semantic Web community plays a relevant role when interoperability of data and integration of results are required. The semantic analytics is an emerging initiative talking about Linked Open Data (LOD) reasoning, provides a vision on how to deduce meaningful information from IoT data, aiming to share the way to interpret data in a interoperable way to produce new knowledge [2].
    [Show full text]
  • Semantic Analytics
    Whitepaper Semantic Analytics: An integrated approach for pharmacovigilance teams to achieve total awareness Semantic Analytics: An integrated approach for pharmacovigilance teams to achieve total awareness The World Health Organization (WHO) Programme for International Drug Monitoring (PIDM) was established in 1968 to facilitate the detection, assessment, understanding and prevention of adverse eff ects and other drug related problems, such as lack of effi cacy. Despite increasingly stringent regulations, pre-marketing clinical trials designed to ensure safety do not involve a suffi ciently large or diverse population and are not long enough to detect all potential safety issues, such as adverse events that have a low incidence rate1 or those resulting from drug-drug interactions, which are thought to be responsible for up to 30% of adverse events2. In most countries, adverse event reporting is now a increasingly stringent, globally diverse regulations. The regulatory requirement for pharmaceutical companies exponentially growing amount of data and increasingly and has resulted in more than 100 drugs being removed diverse range of sources make it almost impossible to from the market due to safety issues in the past 40 years. maintain a comprehensive and up-to-date understanding. However, many of these, such as Vioxx (Rofecoxib) and The result is that the legacy approach to signal detection, Meridia (sibutramine), had already been prescribed to involving manually scanning biomedical sources, is no millions of patients worldwide and adverse events remain longer practical. one of the leading cause of death in the US3. This whitepaper describes how Semantic Analytics enables Clearly more comprehensive, systematic monitoring is an integrated approach to Pharmacovigilance, unlocks required in order to detect, validate and act upon new the potential of biomedical content and will enable adverse events as early as possible.
    [Show full text]
  • James A. Hendler, Phd
    James A. Hendler, PhD Tetherless World Professor & Director, Rensselaer Institute for Data Exploration and Applications Rensselaer Polytechnic Institute [email protected] US citizen, Security Clearance (on request) 518-276-4401 phone Social Security # (on request) 518-276-4464 Fax http://www.cs.rpi.edu/~hendler Twitter: @jahendler Education May, 1986 Department of Computer Science, Brown University, Providence, Rhode Island. PhD -- Computer Science, Artificial Intelligence. Thesis title: Integrating Marker-Passing and Problem-Solving: A Spreading Activation Approach to Improved Choice in Planning. May, 1983 Department of Computer Science, Brown University, Providence, Rhode Island. ScM -- Computer Science, Artificial Intelligence. Thesis project: Implementation of a pseudo-parallel marker-passing system within a frame representation language. May, 1982 Psychology Department, Southern Methodist University, Dallas, Tx. MS -- Cognitive Psychology, Human Factors Engineering. Thesis title: The Effects of a System-Imposed Grammatical Restriction on Interactive Natural Language Dialog May, 1978 Department of Computer Science, Yale University, New Haven, Ct. BS -- Computer Science, Artificial Intelligence. Experience in Higher Education 2007–pres. Rensselaer Polytechnic University (RPI) 1/07 – pres, Tetherless World Constellation Chair (Endowed), joint positions in computer and cognitive science departments 9/10 – 7/12, Program Director, Information Technology and Web Science 7/12 – 9/13, Department Head, Computer Science 9/13 –pres, Director, Institute for Data Exploration and Applications Affiliate faculty, Experimental Media and Performing Arts Center (2009- ) Affiliate, Industrial and System Engineering (2015 - ) Affiliate faculty, Center for Materials, Devices, and Integrated Systems (2015- ) 1 1986-12/06 University of Maryland, College Park 8/99-12/06. Full Professor, 8/92-7/99. Associate Professor 1/86-8/92, Assistant Professor Director, Joint Institute for Knowledge Discovery, 1/04-12/06.
    [Show full text]
  • Geospatial Ontology Development and Semantic Analytics
    Handbook of Geographic Information Science, Eds: J. P. Wilson and A. S. Fotheringham, Blackwell Publishing (in print 2004) Geospatial Ontology Development and Semantic Analytics I. Budak Arpinar, Amit Sheth & Cartic Ramakrishnan Large Scale Distributed Information Systems (LSDIS) Lab Computer Science Department, University of Georgia Athens, GA 30602-7404 {budak,amit,[email protected]} E. Lynn Usery & Molly Azami Department of Geography, University of Georgia Athens, GA 30602 {usery,meazami}@uga.edu Mei-Po Kwan Department of Geography, Ohio State University Columbus, OH 43210-1361 [email protected] Abstract. Geospatial ontology development and semantic knowledge discovery addresses the need for modeling, analyzing and visualizing multimodal information, and is unique in offering integrated analytics that encompasses spatial, temporal and thematic dimensions of information and knowledge. The comprehensive ability to provide integrated analysis from multiple forms of information and use of explicit knowledge make this approach unique. This also involves speci- fication of spatiotemporal thematic ontologies and populating such ontologies with high quality knowledge. Such ontologies form the basis for defining the meaning of important relations and terms, such as near or surrounded-by, and enable computation of spatiotemporal thematic prox- imity measures we define. SWETO (Semantic Web Technology Evaluation Ontology) and its geospatial extension SWETO-GS are examples of these ontologies. Two enabler for what we term geospatial analytics (GSA) are (a) the ability to automatically and semi-automatically ex- tract metadata from syntactically (including unstructured, semi-structured and structured data) and semantically heterogeneous and multimodal data from diverse sources, and (b) analytical processing that exploits these ontologies and associated knowledge bases, with integral support for what we term spatiotemporal thematic proximity (STTP) reasoning and interactive visualiza- tion capabilities.
    [Show full text]