DOCSLIB.ORG

Ontology Ontology & History Ontology Ontology Ontology

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Ontology Ontology & History Ontology Ontology Ontology ontology a branch of metaphysics relating to the nature and relations of being a particular theory about the nature of being or the kinds of existence lecture 2: ontology - basics of#39# ece#627,#winter'13# 2# of#39# ontology & history ontology tree of Porphyry generic Supreme: SUBSTANCE Differentiae: material immaterial Subordinate: BODY SPIRIT Differentiae: animate inanimate Subordinate: LIVING MINERAL Differentiae: sensitive insensitive Subordinate: ANIMAL PLANT Differentiae: rational irrational Subordinate: HUMAN BEAST Individuals: Socrates Plato Aristotle etc. ece#627,#winter'13# 3# of#39# ece#627,#winter'13# 4# of#39# ontology ontology domain-based domain-based the subject of ontology is the study of the categories the product of such a study, called an ontology, is a of things that exist or may exist in some domain catalog of the types of things that are assumed to exist in a domain of interest from the perspective of a person who uses a specific language for the purpose of talking about the domain ece#627,#winter'13# 5# of#39# ece#627,#winter'13# 6# of#39# ontology ontology domain-based – definition 1 domain-based conceptualization refers to an abstract model of a formal, explicit specification of a shared phenomena in the world by having identified the conceptualization relevant concepts of those phenomena explicit means that the type of concepts used, and [T.Gruber, 1993] the constraints on their use are explicitly defined ece#627,#winter'13# 7# of#39# ece#627,#winter'13# 8# of#39# ontology ontology domain-based domain-based – definition 2 (W3C) formal refers to the fact that the ontology should be machine readable ontology is a term borrowed from philosophy that refers to the science of describing the kinds of shared reflects that ontology should capture entities in the world and know they are related consensual knowledge accepted by the communities ece#627,#winter'13# 9# of#39# ece#627,#winter'13# 10# of#39# ontology ontology … description of … example ! classes (“things”) in the various domains of interest ! relationships among those “things” ! properties (attributes) that “things” should possess ece#627,#winter'13# 11# of#39# ece#627,#winter'13# 12# of#39# ontology ontology example vs taxonomy taxonomy the study of the general principles of scientific classification – systematics classification – especially – orderly classification of plans and animals according to their presumed natural relationships ece#627,#winter'13# 13# of#39# ece#627,#winter'13# 14# of#39# ontology ontology vs taxonomy vs taxonomy (example) - Linnaean living being taxonomy taxonomy classifies terms hierarchically, using Kingdom: animalia (generalization, is-a, or type-of) relationship Filo: chordata Subfilo: vertebrata - no other relationships Class: mamalia Subclass: theria - no attributes/features describing terms Order: primata Suborder: anthropoidea Family: hominidae Genera: homo Species: sapiens ece#627,#winter'13# 15# of#39# ece#627,#winter'13# 16# of#39# ontology ontology vs taxonomy (example) vs thesauri - directory structure in a personal computer thesauri contains a set of relationships among concepts, organized in a taxonomic way it is a taxonomy with a set of semantic (binary) relationships, such as, equivalence, inverse, and association ece#627,#winter'13# 17# of#39# ece#627,#winter'13# 18# of#39# ontology ontology vs thesauri vs thesauri - WordNet not sufficient to model other (part-of, member- group, cause-effect, …) aspects of real world the most popular thesaurus - WordNet ece#627,#winter'13# 19# of#39# ece#627,#winter'13# 20# of#39# ontology ontology vs thesauri - WordNet vs thesauri - WordNet ece#627,#winter'13# 21# of#39# ece#627,#winter'13# 22# of#39# ontology ontology unique properties – 1 unique properties – 2 strict subconcept hierarchy ambiguity-free interpretation of meanings and organization of terms must follow the generalization relationships relationship – is-a, type-of relationship users may define properties (with values restricted to certain domains) and more expressive relationships (part-of, ……………) ece#627,#winter'13# 23# of#39# ece#627,#winter'13# 24# of#39# ontology ontology unique properties – 3 classification according to semantic spectrum based on the internal structure and contents of ontolgoies the use of a controlled, finite, but extensible vocabulary depends on the complexity and sophistication of the elements the spectrum ranges from informal catalogues of terms to sophisticated ontologies ece#627,#winter'13# 25# of#39# ece#627,#winter'13# 26# of#39# ontology ontology classification according to semantic spectrum classification according to semantic spectrum - controlled vocabularies (finite lists of terms) - informal is-a hierarchies (hierarchies that use - glossaries (lists of terms whose meaning is generalization relationships in an informal way – described in natural language) not rigorously) - Thesauri (lists of terms … and specific relationships - formal is-a hierarchies (hierarchies that fully between the terms) respect the generalization relationships) ece#627,#winter'13# 27# of#39# ece#627,#winter'13# 28# of#39# ontology ontology classification according to semantic spectrum classification according to semantic spectrum - frames (models that include classes and properties; - ontologies that express value restrictions (contain the primitives of the frame model are classes, or constructs for restricting the values the class frames, that have properties called slots or properties can assume) attributes; slots may contain default values, refer - ontologies that express logical restrictions (allow to other frames, or contain different methods) first-order logic restrictions to be expressed) ece#627,#winter'13# 29# of#39# ece#627,#winter'13# 30# of#39# ontology ontology classification according to ontology generality classification according to ontology generality - upper-level ontologies (describe generic concepts, - task ontologies (describe vocabulary required to such as space, time, events …) perform generic tasks or activities, by specializing - domain ontologies (describe vocabulary pertaining the concepts provided by the upper-level ontology) to a given domain, by specializing the concepts - applications ontologies (describe vocabulary of a provided by the upper-level ontology) specific application, whose concepts correspond to the roles performed by entities in a given domain while performing some task or activity) ece#627,#winter'13# 31# of#39# ece#627,#winter'13# 32# of#39# ontology ontology classification according to represented info classification according to represented info based on orthogonal, to previous slides, - upper ontologies (describe classification general concepts, - knowledge-representation ontologies (provide for example SUMO) primitive modeling elements – classes, subclasses, value, …) - generic and common use ontologies (represent common-sense knowledge that can be used in different domains; vocabulary that relates classes, events, space, causality, and behavior) ece#627,#winter'13# 33# of#39# ece#627,#winter'13# 34# of#39# ontology ontology classification according to represented info classification according to represented info - domain ontologies (offer concepts that can be - domain-task ontologies (are task ontologies that reused in a specific domain – medical, law, …; sth can be reused in one specific domain) between upper and domain ontologies) - method ontologies (provide definitions for concepts - task ontologies (describe vocabulary related to a and relationships relevant to a process) task or activity) - application ontologies (contain all necessary concepts to model the application in question) ece#627,#winter'13# 35# of#39# ece#627,#winter'13# 36# of#39# ontology ontology description languages description languages 1967 – markup language (structure of documents RDF (Resource Markup Language) – representing with help of tags) information about resources in the web SGML – Standard Generalization Markup Language RDF Schema 1989 – HTML (HyperText Markup Language) SHOE (Simple HTML Ontology Extension) XML (Extensible Markup Language) Oil (Ontology Inference Layer) DAML (DARPA Agent Markup Language) ece#627,#winter'13# 37# of#39# ece#627,#winter'13# 38# of#39# ontology description languages 2001 – DMAL+Oil Feb 10th, 2004 – OWL (Web Ontology Language) ece#627,#winter'13# 39# of#39#.
Load more

Recommended publications
  • UNIVERSITY of CALGARY Concept-Learning Supported
    UNIVERSITY OF CALGARY Concept-Learning Supported Semantic Search using Multi-Agent System by Cheng Zhong A THESIS SUBMITTED TO THE FACULTY OF GRADUATE STUDIES IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING CALGARY, ALBERTA April, 2009 © Cheng Zhong 2009 ISBN: 978-0-494-51171-8 UNIVERSITY OF CALGARY FACULTY OF GRADUATE STUDIES The undersigned certify that they have read, and recommend to the Faculty of Graduate Studies for acceptance, a thesis entitled "Concept-Learning Supported Semantic Search using Multi-Agent System" submitted by Cheng Zhong in partial fulfilment of the requirements of the degree of Master of Science. Supervisor, Dr. B. H. Far Department of Electrical and Computer Engineering Dr. M. Moussavi Department of Electrical and Computer Engineering Dr. D. Krishnamurthy Department of Electrical and Computer Engineering Dr. Y. Hu Department of Electrical and Computer Engineering Dr. M. Ghaderi Department of Computer Science Date ii Abstract Currently, the mainstream of semantic search is based on both centralized networking that could be barrier to access trillions of dynamically generated bytes on individual websites, and group commitment to a common ontology that is often too strong or unrealistic. In real world, it is preferred to enable stakeholders of knowledge to exchange information freely while they keep their own individual ontology. While this assumption makes stakeholders represent their knowledge more independently and gives them more flexibility, it brings complexity to the communication among them. To solve this communication complexity, in this thesis, we present (1) a method for semantic search supported by ontological concept learning, and (2) a prototype multi-agent system that can handle semantic search and encapsulate the complexity of such process from the users.
    [Show full text]
  • Deep Semantics in the Geosciences: Semantic Building Blocks for a Complete Geoscience Infrastructure
    Deep Semantics in the Geosciences: semantic building blocks for a complete geoscience infrastructure Brandon Whitehead,1,2 Mark Gahegan1 1Centre for eResearch 2 Institute of Earth Science and Engineering The University of Auckland, Private Bag 92019, Auckland, New Zealand {b.whitehead, m.gahegan}@auckland.ac.nz Abstract. In the geosciences, the semantic models, or ontologies, available are typically narrowly focused structures fit for single purpose use. In this paper we discuss why this might be, with the conclusion that it is not sufficient to use semantics simply to provide categorical labels for instances—because of the interpretive and uncertain nature of geoscience, researchers need to understand how a conclusion has been reached in order to have any confidence in adopting it. Thus ontologies must address the epistemological questions of how (and possibly why) something is ‘known’. We provide a longer justification for this argument, make a case for capturing and representing these deep semantics, provide examples in specific geoscience domains and briefly touch on a visualisation program called Alfred that we have developed to allow researchers to explore the different facets of ontology that can support them applying value judgements to the interpretation of geological entities. Keywords: geoscience, deep semantics, ontology-based information retrieval 1 Introduction From deep drilling programs and large-scale seismic surveys to satellite imagery and field excursions, geoscience observations have traditionally been expensive to capture. As such, many disciplines related to the geosciences have relied heavily on inferential methods, probability, and—most importantly—individual experience to help construct a continuous (or, more complete) description of what lies between two data values [1].
    [Show full text]
  • Introduction to Ontologies Part I
    EMMC The European Materials Modelling Council Introduction to Ontologies Part I Alexandra Simperler On-line 29.4.2019 https://emmc.info/ EMMC The EMMO round table Emanuele Ghedini (University of Bologna) Gerhard Goldbeck Adham Hashibon (Goldbeck Consulting) (Fraunhofer Institut) Georg J. Schmitz Jesper Friis (Access) (SINTEF) EMMC Outline • Taxonomy vs Ontology • The value of ontologies • Semantic Technologies • Representation of Ontologies What’s the difference between an EMMC ontology and a taxonomy? TAXONOMY ONTOLOGY • Like a tree with branches • Like a spiderweb • Parent – Child relation, • Manifold of relations, is_a adds non is_a relations • Generally limited to a • Not limited to a specific specific subject area subject area • Hierarchy of (simple) • Complex relations with concepts complex concepts EMMC The Value of Semantic Technologies • Natural perspective of human communication • Greater expressivity than a database • Improved logical structure • Knowledge layer is separated from data layer • Flexibility, reusability, interoperability • Hierarchies, relationships and annotation • Search patterns can be stored, share, reused • Reasoning – answers to what-if, if-then questions • Accessible to Artificial Intelligence EMMC The Value of Ontology in the Materials Field Artificial Intelligence Materials Ontology will contribute to: Semantic Web Systems Engineering • High throughput experiments Biomedical Informatics • High throughput characterization Library Science • Cost reduction Enterprise Bookmarking Information Architecture • Reliable results • Standard operation procedures (SOPs) • Design of materials with improved characteristics All these fields create Ontologies to limit • Classification of techniques and complexity and acceleration of results organize information. The Ontology can then • Uniform query interface be applied to problem solving. EMMC Examples/Use of Ontologies • Database integration – Connected data! Discover new trends – Takahashi, et al (2018).
    [Show full text]
  • Ech-0205 V1.0 Linked Open Data
    E-Government Standards Page 1 of 49 eCH-0205 Linked Open Data Name Linked Open Data eCH-number eCH-0205 Category Accessory Document Quality stage Defined Version 1.0 Status Approved Decision on 2018-03-06 Date of issue 2018-03-13 Replaces version - Requirements - Annexes - Languages English (original) Authors Members of the eCH Specialized Group “Open Government Data” Main Author: Beat Estermann, Berner Fachhochschule [email protected] For a list of further contributors, see Annex B. Editor / Distribution eCH registered association, Mainaustrasse 30, Postfach [P.O. Box], 8034 Zürich T 044 388 74 64, F 044 388 71 80 www.ech.ch / [email protected] eCH registered association www.ech.ch / [email protected] eCH-0205 Linked Open Data / 1.0 / Approved / 2018-03-13 E-Government Standards Page 2 of 49 Summary This document provides the Swiss Linked Data community with a shared vision of the state of linked open data publication in the public and heritage sectors in Switzerland and gives people who are new to the community a first overview of previous and ongoing activities in the area of data publication, data use, and know-how exchange. The document contains a short introduction to linked (open) data, gives a detailed account of what linked data publica- tion is about, provides an overview of the present state of linked data publication by Swiss public and heritage sector organizations, and presents a series of exemplary use cases that serve as test and study cases to tackle current challenges and demonstrate the usefulness of linked (open) data in practice.
    [Show full text]
  • On the Move to Semantic Web Services”, 6Th International Enformatika Conference
    Cardoso, J., “On the move to Semantic Web Services”, 6th International Enformatika Conference. Transactions on Enformatika, Systems Sciences and Engineering, Vol. 8, pp. 13-24, Budapest, Hungary, October 26-28, 2005. ISBN: 975-98458-7-3 On the move to semantic Web services Jorge Cardoso resources. A graphic presentation of the syntactic Web is Abstract—Semantic Web services will enable the semi- given to the left of Figure 1. Resources are linked together automatic and automatic annotation, advertisement, discovery, forming the Web. There is no distinction between resources or selection, composition, and execution of inter-organization business the links that connect resources. To give meaning to resources logic, making the Internet become a common global platform where and links, new standards and languages are being investigated organizations and individuals communicate with each other to carry out various commercial activities and to provide value-added and developed. The rules and descriptive information made services. There is a growing consensus that Web services alone will available by these languages allow the type of resources on not be sufficient to develop valuable solutions due the degree of the Web and the relationships between resources to be heterogeneity, autonomy, and distribution of the Web. This paper characterized individually and precisely, as illustrated to the deals with two of the hottest R&D and technology areas currently right of Figure 1. associated with the Web — Web services and the Semantic Web. It presents the synergies that can be created between Web Services and Semantic Web technologies to provide a new generation of e- services. Keywords—Semantic Web, Web service, Web process, WWW.
    [Show full text]
  • A Thesaurus-Guided Method for Smart Manufacturing Diagnostics Farhad Ameri May 2019, NIST
    A Thesaurus-guided Method for Smart Manufacturing Diagnostics Farhad Ameri May 2019, NIST July 9, 2019 1 Landgrebe, J., & Smith, B. (2019). Making AI meaningful again. arXiv preprint arXiv:1901.02918. #MakingAIMeaningfulAgain July 9, 2019 2 General Limitations of Machine Learning • Need for huge amount of data for training • Need for stable environment • Unstable against variations in input • Lack of transparency (blackbox operation mode) • Output of all stochastic models is approximative “It is naïve to believe stochastic models alone can mimic human’s decisions without benefiting form prior knowledge” “AI applications today can simulate only a small fragment of the semantics underlying human language use”. July 9, 2019 3 Proposed Solution • Incorporating Prior Knowledge into AI Algorithms • Combining formal logic (semantic representation) and stochastic models in AI applications. July 9, 2019 4 Minimal Requirements for AI language processing systems • Exactness • Information security • Robustness • Data parsimony • Semantic fidelity • Inference • Prior knowledge usage July 9, 2019 5 Semantic Spectrum of Knowledge Organization Systems Ontology Conceptual Model OWL, Common Logic Taxonomy RDFS, UML Expressivity Thesaurus RDFS Semantic interoperability Informal Syntactic/Structural Hierarchy SKOS interoperability List XML Glossary, Dictionary, Controlled Vocabulary Development Cost July 9, 2019 6 Thesaurus vs. Ontology Ontology Thesaurus Logic-based semantics Relational semantics Structural semantics Lexical semantics Text → Logic-based
    [Show full text]
  • LB2CO: a SEMANTIC ONTOLOGY FRAMEWORK for B2C ECOMMERCE TRANSACTION on the INTERNET Akanbi Adeyinka K
    International Journal of Research in Computer Science eISSN 2249-8265 Volume 4 Issue 1 (2014) pp. 1-9 www.ijorcs.org, A Unit of White Globe Publications doi: 10.7815/ijorcs.41.2014.075 LB2CO: A SEMANTIC ONTOLOGY FRAMEWORK FOR B2C ECOMMERCE TRANSACTION ON THE INTERNET Akanbi Adeyinka K. Institute of Science & Technology, Jawaharlal Nehru Technological University, Hyderabad 500085, A.P, India. Email: [email protected] Abstract: Business ontology can enhance the However, the current way of information organization successful development of complex enterprise system; and expression is defective, in that it was designed for this is being achieved through knowledge sharing and user decision making needs, rather than to provide the ease of communication between every entity in the semantic information that computer can process domain. Through human semantic interaction with the automatically, thereby limiting the computer’s web resources, machines to interpret the data capacity of automatic analysis and further intelligent published in a machine interpretable form under web. process in information retrieval, that enhances However, the theoretical practice of business ontology information and data sharing across various platforms. in eCommerce domain is quite a few especially in the section of electronic transaction, and the various However, two extremely important factors that can techniques used to obtain efficient communication contribute to this effective non-human communication across spheres are error prone and are not always are: (1) a common language in which the resources guaranteed to be efficient in obtaining desired result implied in the communication can be specified, and (2) due to poor semantic integration between entities.
    [Show full text]
  • Approaches to Developing Semantic Web Services
    INTERNATIONAL JOURNAL OF COMPUTER SCIENCE VOLUME 1 NUMBER 1 2006 ISSN 1306-4428 Approaches to Developing Semantic Web Services Jorge Cardoso global infrastructure for sharing both documents and data, Abstract—It has been recognized that due to the autonomy and which make searching for and reusing information easier and heterogeneity, of Web services and the Web itself, new approaches more reliable as well. RDF is a standard for creating should be developed to describe and advertise Web services. The descriptions of information, especially information available most notable approaches rely on the description of Web services on the World Wide Web. What XML is for syntax, RDF is for using semantics. This new breed of Web services, termed semantic Web services, will enable the automatic annotation, advertisement, semantics. The latter provides a clear set of rules for providing discovery, selection, composition, and execution of inter- simple descriptive information. OWL provides a language for organization business logic, making the Internet become a common defining structured Web-based ontologies which allows a global platform where organizations and individuals communicate richer integration and interoperability of data among with each other to carry out various commercial activities and to communities and domains. provide value-added services. This paper deals with two of the Along with the development of the semantic Web, the hottest R&D and technology areas currently associated with the Web — Web services and the semantic Web. It describes how semantic Internet has created new organizational and working models Web services extend Web services as the semantic Web improves the which are based on electronic transactions.
    [Show full text]
  • Oberkampf-Allotrope-Framework.Pdf
    V.2.4 Allotrope Framework: Semantischer Datenstandard für das Lab 4.0 Heiner Oberkampf, PhD November 15th 2018, Duisburg iuta 3. AnalytikTag WHO IS OSTHUS? Global organization 120+ employees and growing rapidly 16+ big pharma customers + many chemicals and lab-based companies. Allotrope Framework Architect Our approach technology: Connecting data, people and organizations Slide 2 DATA AS AN ASSET DIGITALIZATION Slide 3 Current Situation in the Lab increase in automation connected devices heterogeneous IT landscape increased complexity more data is generated stronger regulations high cost pressure Digitalization is often still just “paper on glass”! Slide 4 Value of Data is Realized by Usage in Decision Making and Insights Use data to answer business questions. Data Assets Slide 5 Value of Data is Mostly Not Realized “Only 3% of Companies’ Data Meets Basic Quality Standards” Harvard Business Review: https://hbr.org/2017/09/only- ? 3-of-companies-data-meets-basic-quality-standards bad data quality, findability, accessibility, “For most large enterprises, the interoperability, reusability root of this problem lies in years of treating the data generated by their operational systems as a form of exhaust rather than as a fuel to deliver great services, build better products, and create competitive advantage.” Data Assets Database Trends and Applications: http://www.dbta.com/Editorial/Trends-and-Applications/The- Enterprise-Data-Debt-Crisis-123008.aspx Slide 6 Finding Information is time-consuming Example Questions: “Which chromatograms did we make this year for molecule X?” minimize this “Did we already analyze compound X?” “In which of our labs can I run experiment X?” focus here “Can I trust the data that was generated by someone else in my company?” Source: International Data Corporation (IDC); McKinsey Global Institute analysis.
    [Show full text]
  • Specification and Is Currently in the Finalization Phase
    Date: April 2021 Application Programming Interfaces for Knowledge Platforms (API4KP) Version 1.0 – Beta 1 OMG Document Number: ptc/2021-04-02 Standard Document URL: https://www.omg.org/spec/API4KP/1.0/Alpha2/ Normative reference: https://www.omg.org/spec/API4KP/1.0/KnowledgeArtifactRepository.html https://www.omg.org/spec/API4KP/1.0/KnowledgeAssetRepository.html https://www.omg.org/spec/API4KP/1.0/KnowledgeAssetTransrepresentation.html https://www.omg.org/spec/API4KP/1.0/KnowledgeBaseConstruction.html https://www.omg.org/spec/API4KP/1.0/KnowledgeReasoning.html Machine readable file(s): https://www.omg.org/API4KP/20210201/ Normative: https://www.omg.org/spec/API4KP/20210201/api4kp.xmi https://www.omg.org/spec/API4KP/20210201/api4kp_uml_profiles.xmi https://www.omg.org/spec/API4KP/20210201/vocabs.xmi https://www.omg.org/spec/API4KP/20210201/knowledgeArtifactRepository.oas.yaml https://www.omg.org/spec/API4KP/20210201/knowledgeAssetRepository.oas.yaml https://www.omg.org/spec/API4KP/20210201/knowledgeAssetTransrepresentation.oas.yaml https://www.omg.org/spec/API4KP/20210201/knowledgeBaseConstruction.oas.yaml https://www.omg.org/spec/API4KP/20210201/knowledgeReasoning.oas.yaml https://www.omg.org/spec/API4KP/20210201/api.inference.idl https://www.omg.org/spec/API4KP/20210201/api.knowledgebase.idl Application Programming Interfaces for Knowledge Platforms (API4KP), 1.0 Beta 1 1 https://www.omg.org/spec/API4KP/20210201/api.repository.artifact.idl https://www.omg.org/spec/API4KP/20210201/api.repository.asset.idl https://www.omg.org/spec/API4KP/20210201/api.transrepresentation.idl
    [Show full text]
  • FAIR Semantics
    FAIR Semantics Building FAIR Ontologies Yann Le Franc, PhD ORCiD: 0000-0003-4631-418X CEO – e-Science Data Factory, Paris, France Second EC workshop on Materials and Manufacturing Ontology - June 6th 2019 - Brussels About the speaker • Background in Neuroscience, Computational Neurosciences and Machine Learning • Expert in Semantic Web and Ontology design (10 years) • Chairman of EUDAT Semantic Web Working Group • Co-chairman of RDA Vocabulary and Semantic SerVices Interest Group • Member of GoFAIR Implementation Network INTER – Cross-disciplinary Semantic Interoperability • Task leader – FAIR Semantics What is FAIR? FAIR Principles haVe been internationally endorsed (G8, G20, European Commission, NIH, …) What is FAIR? 14 basic principles FAIR Principles haVe been internationally endorsed (G8, G20, European Commission, NIH, …) Increasing interest from governments and industry stakeholders How to implement FAIR? Leveraging graph data • Different formats for data • Tabular databases: SQL/RDBMS • CSV (Comma Separated Values) and spreadsheets • PDF, Text (txt, docx,…) • The adVantages of graph data • Faster than using SQL and associated JOIN operations • Better suited to integrating data from heterogeneous sources and with heterogeneous formats • Better suited to situations where the data model is eVolVing • Emerging role for knowledge graphs as the basis for integration and goVernance across the enterprise DeriVed from: “Open Markets of SerVices and the Sentient Web” (2019), D. Ragget – Web of Things Open Day – June 3rd 2019 - Munich Different types of directed graphs Named Graph Property Graph From https://www.w3.org/TR/rdf11-primer/ From https://neo4j.com/deVeloper/graph-database/ W3C’s suite of standards for graph data • Binary named directed relationships between concepts • <subject, predicate, object>, e.g.
    [Show full text]
  • Data Technology in Materials Modelling
    SPRINGER BRIEFS IN APPLIED SCIENCES AND TECHNOLOGY Martin Thomas Horsch Silvia Chiacchiera Welchy Leite Cavalcanti Björn Schembera Data Technology in Materials Modelling SpringerBriefs in Applied Sciences and Technology SpringerBriefs present concise summaries of cutting-edge research and practical applications across a wide spectrum of fields. Featuring compact volumes of 50 to 125 pages, the series covers a range of content from professional to academic. Typical publications can be: • A timely report of state-of-the art methods • An introduction to or a manual for the application of mathematical or computer techniques • A bridge between new research results, as published in journal articles • A snapshot of a hot or emerging topic • An in-depth case study • A presentation of core concepts that students must understand in order to make independent contributions SpringerBriefs are characterized by fast, global electronic dissemination, standard publishing contracts, standardized manuscript preparation and formatting guidelines, and expedited production schedules. On the one hand, SpringerBriefs in Applied Sciences and Technology are devoted to the publication of fundamentals and applications within the different classical engineering disciplines as well as in interdisciplinary fields that recently emerged between these areas. On the other hand, as the boundary separating fundamental research and applied technology is more and more dissolving, this series is particularly open to trans-disciplinary topics between fundamental science and engineering.
    [Show full text]