ISO TC37/SC4 N435 Nov 12, 2007 Presented by Miran Choi/ETRI Written by Jae Sung Lee/Chungbuk National Univ. Contents Motivation Ontology Construction for Web Contents Related Standards and Ontology Work Scope Limit of keyword search ambiguous keywords in query and docs retrieves too many redundant documents waste time on finding what we want Semantic search Ideally no ambiguity in query and text retrieve docs with more accuracy by use of semantic information need to solve technical problems Query method Semantic tagging Globally common ontology construction Various ontologies are being built in many areas and applications Classification of ontology is various Different levels of ontologies Lightweight ontology Simple metadata level, or concepts with hierarchy, or small number of axioms E.g. term list, MDR, ISOCat, Topic Map, SUMO Heavyweight ontology Delicate definitions and first order logic for inference E.g. BFO, DOLCE Ontologies are growing in their own ways Various collections of ontology Ontology library e.g. SWAG, DAML ontology library Ontology registry e.g. MDR, ISOCat Etc. For the global search Semantic harmonization methods are needed by sharing standard principle and methods Global coordination is needed for the various standards Needs To make ontologies consistent and compatible each other To promote reuse of existing ontologies To avoid conflicting definitions To lessen confusion in semantic contents search The sooner, the less confusion! Web contents characteristic Various topics and technical levels But, target to the general, less technical, not too specific contents. Ontology for semantic search Less strict meaning definition than the ontology for interoperability A little semantic difference is tolerable Lightweight ontology is enough Use controlled vocabulary or shared terms Include some basic relationship if needed But no complicated relationship A guide is needed to use existing ontologies May need a ontology for ontologies to look up. May need to filter out only lightweight features except other complex relations or axioms. Consulting existing ontologies ontology library E.g. Daml ontology library, Semantic Web Agreement Group ontology registry E.g. Meta data registry, Data category registry (ISOcat) ontology homepage/document E.g. SUMO General construction guide can be derived from other guides Terminology work(ISO 704) Building ontologies and knowledge elicitation (Rector et al) And others General steps of ontology construction Classify the target objects Define the concept of the objects Concepts are defined by only essential characteristics Define relationship between concepts Implementations and evaluation Standard Ontology Language Choose one of the languages RDF(S), OWL, UML, SCL, DL etc Or, define mapping between them by using the following standards Ontology Definition Metamodel (by OMG) Metamodel Framework (MMF) Naming convention (designations) Terms sometimes ambiguity problem Numbers E.g. Published Semantic Indicators Concatenated terms (ISO/IEC 11179) E. g. PersonGivenName Harmonized method? Central ontology registry All new ontologies should be known to applications and other users. Central registry keeps the ontologeis or reference points. Central registry can be accessed by all. Need to keep all other compatible ontologies list. 15 Represent semantic information using URI triples. A triple represents subject, property (relation) and object. Make inference based on the triples Notation 3: example _:x0. _:x0 Jae Sung Lee.http://xmlns.com/0.1/foaf/name Ontology is built by using URI triples. Interoperable among agents sharing ontology Problem: various ontology metadata use their own triples with various ontology (namespace) interoperable in a domain, but not in a global scale. 16 One universe common ontology for interoperability Ideal but not practical now No global ontology Web is not static Needs semantic reconciliation because... agreed standard only address a small body of knowledge must accommodate prior resources to standard new work will have to go beyond standard Consensus ontology if have sufficient overlap under the same universe of discourse then reconcile ontologies Merging ontology is difficult concept mapping will happen: 1-1, n-1, n-n value mapping is not always consistent 17 Ontology languages for standard format RDF represents graph for structure RDFS use specialized vocabulary and primitive classes and properties OWL more precise & expressive than RDFS Evolutionary ontology Keep systems interoperable using partial understanding and transformability For common understandable terms, third party databases are needed: eg. SWAG (Semantic Web Agreement Group) SWAG keeps namespaces: rdf, foaf, dc and etc. DAML ontology library keeps various ontologies. 18 IEEE SUO limited to concepts that are meta, generic, abstract and philosophical general enough to address (at a high level) a broad range of domain areas provide a structure and a set of general concepts upon which domain ontologies could be constructed. IEEE SUMO/MILO Largest formal public ontology Suggested Upper Merged Ontology MId-Level Ontology Communications, Countries and Regions, distributed computing, Economy, Finance, Engineering components, Geography, Government... domain ontologies could be constructed based on this. source: 19 For interoperability between ontologies Naive approach make a new vocabulary: not practical PSI approach (Published Subject Indicators) maps equivalent terms to a unique ID. OASIS builds large PSI database for interoperability source: ISO TC37 standards DCR keeps all data categories. Each app DCS can select subset of fields. Each apps DCS can select subset data units. MDR (meta data registry) stores data elements (both semantics and representations) The semantic areas describe precise definitions The representational areas define how the data is represented in a specific format such as XML ISO/IEC for data elements Registration guidelines Naming and Identification Principles Formulation of Data Definitions rules Classification Scheme SBVR is targeted at business rules and business vocabularies Model Driven Architecture SBVR meta model is automatically generated from SBVR vocabularies SBVR meta model provide standardized data interfaces and data interchange. A business vocabulary contains all the terms and concepts in business. source: SBVR adopted specification 23 Software engineering approach Ontology is represented in application model Model driven ontology build Goal to harmonize metamodel technology and contents of metamodel provide interoperability by using reference ontology Core model of the MMF provides a mechanism for describing each different metamodel in local registries enables registration of those to the registry MMF model mapping register mapping rules enable the federation among different registries 24 source: ISO/IEC CD :200x(E) 25 Includes metamodels and defines mapping between them RDFS, OWL, UML, SCL, ER, TM, DL metamodels Concentrates on most widely applicable and most readily achievable goals use case analysis to 3 major clusters of apps: business, analytic, engineering app. 26 source: OMG/RFP ad/ 27 Each ODM metamodel is registered to MMF source: OKABE, Masao presentation in KIPONTO 2005 MMF Ontology Registration Ontology Ontology Component Atomic_Onto_Construct OWL/RDFS Metamodel SCL Metamodel TM Metamodel DL Metamodel UML2 Metamodel ER Metamodel Ontology described in OWL/RDFS Ontology described in SCL Ontology described in TM Ontology described in DL Ontology described in UML2 Ontology described in ER Ontology that has a suitable interface 28 MMF ODM SUO/SUMO MILO TM Ontology work guide for web contents General and language based Application model driven Domain specific Meta level SBVR ISOCat MDR Thank you very much! !