Nancy Lawler U.S. Department of Defense ISO/IEC 11179 Part 2: Classification Schemes Metadata Registries — Part 2: Classification Schemes The revision.

Slides:



Advertisements
Similar presentations
Dr. Leo Obrst MITRE Information Semantics Information Discovery & Understanding Command & Control Center February 6, 2014February 6, 2014February 6, 2014.
Advertisements

1 ICS-FORTH & Univ. of Crete SeLene November 15, 2002 A View Definition Language for the Semantic Web Maganaraki Aimilia.
1 Metadata Registry Standards: A Key to Information Integration Jim Carpenter Bureau of Labor Statistics MIT Seminar June 3, 1999 Previously presented.
Direction of Proposals for New Edition (E3) of ISO/IEC 11179
Database Systems: Design, Implementation, and Management Tenth Edition
Edition 3 Metadata registry (MDR) Ray Gates May 12, /05/20151.
Analyzing Systems Using Data Dictionaries Systems Analysis and Design, 7e Kendall & Kendall 8 © 2008 Pearson Prentice Hall.
©Ian Sommerville 2004Software Engineering, 7th edition. Chapter 8 Slide 1 System modeling 2.
ISO/IEC 11179, Part 2: Classification Schemes Jim Carpenter Bureau of Labor Statistics Nancy Lawler Department of Defense Open Forum on Metadata Registries.
Helping people find content … preparing content to be found Enabling the Semantic Web Joseph Busch.
DDI 3.0 Conceptual Model Chris Nelson. Why Have a Model Non syntactic representation of the business domain Useful for identifying common constructs –Identification,
Copyright © 2011 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 8 The Enhanced Entity- Relationship (EER) Model.
Copyright © 2011 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 7 Conceptual Data Modeling Using Entities and Relationships.
Chapter 4 Relational Databases Copyright © 2012 Pearson Education, Inc. publishing as Prentice Hall 4-1.
Methodology Conceptual Database Design
Lecture Two Database Environment Based on Chapter Two of this book:
Modeling & Designing the Database
Chapter 4 Relational Databases Copyright © 2012 Pearson Education 4-1.
Copyright © 2007 Ramez Elmasri and Shamkant B. Navathe Slide 4- 1 EER stands for Enhanced ER or Extended ER EER Model Concepts Includes all modeling concepts.
Procedures to Develop and Register Data Elements in Support of Data Standardization September 2000.
SDC JE-xxxx. Bruce Bargmeyer EPA/OIRM/EIM Division Tel: (202) WWW URL:
Database Environment 1.  Purpose of three-level database architecture.  Contents of external, conceptual, and internal levels.  Purpose of external/conceptual.
Future of MDR - ISO/IEC Metadata Registries (MDR) Larry Fitzwater, SC 32 WG 2 Convener Computer Scientist U.S. Environmental Protection Agency May.
9 th Open Forum on Metadata Registries Harmonization of Terminology, Ontology and Metadata 20th – 22nd March, 2006, Kobe Japan. Commonalities and Differences.
Multilingual Issues in the Representation of International Bibliographic Standards for the Semantic Web Gordon Dunsire Independent Consultant; Chair of.
1 CS 456 Software Engineering. 2 Contents 3 Chapter 1: Introduction.
Status report of : Framework for generating ontologies ISO/IEC JTC 1/SC 32/WG 2 Interim Meeting, Redwood City, USA, November 17, 2010 Dongwon Jeong,
Environmental Terminology Research in China HE Keqing, HE Yangfan, WANG Chong State Key Lab. Of Software Engineering
INF 384 C, Spring 2009 Ontologies Knowledge representation to support computer reasoning.
Classification and the Metadata Registry Judith Newton NIST IRS XML Stakeholders/ XML Working Group May 18, 2004.
Metadata Registries Workshop April 15, 1998 Slide 1 of 20 ANSI X Douglas D. Mann Stewardship Naming & Identification Classification.
Tommie Curtis SAIC January 17, 2000 Open Forum on Metadata Registries Santa Fe, NM SDC JE-2023.
Ontology Summit2007 Survey Response Analysis Ken Baclawski Northeastern University.
9 th Open Forum on Metadata Registries Harmonization of Terminology, Ontology and Metadata 20th – 22nd March, 2006, Kobe Japan. Presentation Title: Day:
1 Analyzing the Definition of a Classification Scheme In ISO Part 2: Classification Jim Carpenter March 26, 2001 Last Update: Wednesday, June 14,
Taken from Schulze-Kremer Steffen Ontologies - What, why and how? Cartic Ramakrishnan LSDIS lab University of Georgia.
Semantic Data & Ontologies CMPT 455/826 - Week 5, Day 2 Sept-Dec 2009 – w5d21.
Database Systems: Enhanced Entity-Relationship Modeling Dr. Taysir Hassan Abdel Hamid.
©Ferenc Vajda 1 Semantic Grid Ferenc Vajda Computer and Automation Research Institute Hungarian Academy of Sciences.
Potential standardization items for the cloud computing in SC32 1 WG2 N1665 ISO/IEC JTC 1/SC 32 Plenary Meeting, Berlin, Germany, June 2012 Sungjoon Lim,
Rupa Tiwari, CSci5980 Fall  Course Material Classification  GIS Encyclopedia Articles  Classification Diagram  Course – Encyclopedia Mapping.
Semantic web course – Computer Engineering Department – Sharif Univ. of Technology – Fall Knowledge Representation Semantic Web - Fall 2005 Computer.
Proposed NWI KIF/CG --> Common Logic Standard A working group was recently formed from the KIF working group. John Sowa is the only CG representative so.
Week III  Recap from Last Week Review Classes Review Domain Model for EU-Bid & EU-Lease Aggregation Example (Reservation) Attribute Properties.
SKOS. Ontologies Metadata –Resources marked-up with descriptions of their content. No good unless everyone speaks the same language; Terminologies –Provide.
LoG: A Methodology for Metadata Registry-based Management of Scientific Data July 5, 2002 Doo-Kwon Baik
Metadata Common Vocabulary a journey from a glossary to an ontology of statistical metadata, and back Sérgio Bacelar
1 ISO/IEC 11179, Part 2: Classification Schemes Jim Carpenter Bureau of Labor Statistics Metatopia 2001 Conference September 20 – 21, 2001.
Overview of SC 32/WG 2 Standards Projects Supporting Semantics Management Open Forum 2005 on Metadata Registries 14:45 to 15:30 13 April 2005 Larry Fitzwater.
Issues in Ontology-based Information integration By Zhan Cui, Dean Jones and Paul O’Brien.
Metadata Registries Workshop Metadata Registries Workshop U.S. Bureau of Labor Statistics Conference Center April 15-17, 1998.
Knowledge Representation. Keywordsquick way for agents to locate potentially useful information Thesaurimore structured approach than keywords, arranging.
1 Open Ontology Repository initiative - Planning Meeting - Thu Co-conveners: PeterYim, LeoObrst & MikeDean ref.:
Working with XML. Markup Languages Text-based languages based on SGML Text-based languages based on SGML SGML = Standard Generalized Markup Language SGML.
ISO TC37/SC4 N435 Nov 12, 2007 Presented by Miran Choi/ETRI Written by Jae Sung Lee/Chungbuk National Univ.
Data Element Classification ISO/IEC 11179, Part 2
1 Chapter 2 Database Environment Pearson Education © 2009.
Improvement of Semantic Interoperability based on Metadata Registry(MDR) Doo-Kwon Baik Dept. of CSE Korea University.
Department of Mathematics Computer and Information Science1 CS 351: Database Management Systems Christopher I. G. Lanclos Chapter 4.
Ontologies COMP6028 Semantic Web Technologies Dr Nicholas Gibbins
IPDA Registry Definitions Project Dan Crichton Pedro Osuna Alain Sarkissian.
COMP6215 Semantic Web Technologies
Object Management Group Information Management Metamodel
The Re3gistry software and the INSPIRE Registry
Object Oriented Analysis and Design
Edition 3 Metadata registry (MDR)
Jim Carpenter March 26, 2001 Last Update: Wednesday, June 14, 2001
RDA Community and linked data
The new RDA: resource description in libraries and beyond
Semantic Interoperability in Digital Library Systems
Presentation transcript:

Nancy Lawler U.S. Department of Defense ISO/IEC Part 2: Classification Schemes Metadata Registries — Part 2: Classification Schemes The revision of Part 2 is not an ISO standard because it is in draft status, a project of the ANSI/NCITS L8 committee. Excerpts from the draft standard in this talk are copyright- protected by ISO Editors aim at consistency with other standards: ISO 704 Terminology work – principles and methods ISO Parts 1,3,4, 5,6 ISO 1087 Part 2: Computational aids in terminology ISO standards for monolingual and multilingual thesauri ISO/IEC :2001 UML Clarity goals: – Guidance should be practical, with useful distinctions, and the language should be translatable.

Nancy Lawler U.S. Department of Defense What is a Classification Scheme? Clearly includes: Artifacts which support controlled vocabularies for definitions, formal specifications and descriptions – Taxonomies – Terminologies, Thesauri – Ontologies: Generalization hierarchies of concepts or classes Linguistic Ontologies Axiomatized Ontologies – Keyword lists – Data models Less obvious: Any Composite Element of a System – System documentation – Mappings between data models – Data types

Nancy Lawler U.S. Department of Defense Part of a registered Information System may be used as a Classification Scheme When a relationship is maintained between one of its parts and another administered item. – Examples: a class in a data model is derived from a registered data element originally defined within another model a data model is generated from a cluster or subdomain of an ontology. – In practice: if you need classification scheme attributes for a diagram, documentation or value domain, you may register it as a classification scheme.

Nancy Lawler U.S. Department of Defense Why Include Classification Schemes in a Metadata Registry? Improve metadata quality by managing use of controlled vocabularies for definitions Manage captured domain knowledge which persists independently of applications and IT trends, and is less compromised by such considerations as cost and performance. – The meaning of the sentence "a customer buys a book from a sales clerk" could be conveyed in a variety of knowledge and data representations: UML class diagram or use case IDEF entity relationship diagram or process diagram The formal specification standard Z, the logic programming language Prolog, KIF, Conceptual Graphs, or even a labelled sketch.

Nancy Lawler U.S. Department of Defense Part 2 does not mandate utility or content It provides a structure which supports the difficult work of semantic reconciliation, using ’ s definitions, management of status, versions and authorities – Axiomatized ontologies represent an understanding of a part of the world related to an information system. They may include business rules, theories, and constraints, and serve as formal specifications for automatically generated database schema and applications. – Linguistic ontologies may support natural language interfaces for queries of structured and unstructured data, extraction of information from text, and translation systems – Mappings between models are also models Support heterogenous database access, mediators, agents, identification of re-usable components

Nancy Lawler U.S. Department of Defense Classification Scheme Classification Scheme Definition: the descriptive information for an arrangement or division of objects into groups based on characteristics which the objects have in common. Examples: a key word list, a taxonomy, a data model, a network, an ontology. A list of terms might be taken from the "leaf level" of a taxonomy or from object classes in a data model. Attributes: classification_scheme_type_name –Definition: the name of the type of classification scheme. (There is no standard list of structure types and it is possible that the type used to describe a scheme may vary). Other attributes are those of any administered item.

Nancy Lawler U.S. Department of Defense Classification Scheme Item These represent individual items within Classification Schemes. They are not required to be recorded. Attributes: – csi_type_name Definition: the name of the type of classification scheme item. Example: –Taxon term (if the value attribute is Drosophilae) –Taxon identifier (if the value attribute is 5411) ( Phylum, Class, Order, Family, Species are levels, not item types) – csi_value A designator of a classification scheme item Value should be unique within a classification scheme. Together with the administration_record_identifier, this provides a globally unique namespace for classification scheme items. Example: Drosophilae OR: 5411

Nancy Lawler U.S. Department of Defense Classification Scheme Item Relationships – Classification_scheme_item_relationships are also not required to be recorded. – Each classification_scheme_item_relationship requires exactly two distinct classification_scheme_items and they must both be recorded. – Examples: Faculty is a specialization of Employee A Drug Order IS-A Pharmacy Order Component Attributes: – csi_item_relationship_type_description Examples: – Broader Than / Narrower Than – Hyponym / Synonym / Hypernym – Subclass / Superclass

Nancy Lawler U.S. Department of Defense Classification Scheme Region

Nancy Lawler U.S. Department of Defense Thank you for your attention and hospitality! Reviews, experiences, issues and comments on Part 2 are very welcome. Contact: Some Related Conferences: – KAWS Knowledge Acquisition Workshop – KRDB Knowledge Representation and Databases – ICCS International Conference on Conceptual Structures – FOIS Formal Ontology in Information Systems