Semantic in Information Systems:Current Issues - New Trends : Semantic in Information Systems:Current Issues - New Trends Kokou Yétongnon
Professor
Summary of ideas : Summary of ideas semantic and Integration of information systems is a recurring problem
(new variations on an old problem)
Global semantic of multiple systems is hard to determine
(large number of information sources with varying levels of semantic) 2
Summary of ideas : Summary of ideas Dynamic environments require new ways of dealing with semantic and interoperability
Semantic must locally emerge from interactions among information systems
(Negotiations, agreements etc…) 3
Outline : Outline Introduction and Motivation
Data Integration
Modeling Semantic
Information System Interoperability
Semantic in Dynamic Environments
Conclusion 4
The Traditional View of Information Systems : 5 The Traditional View of Information Systems Where is the semantic of the system Instances / Occurrences Schema The traditional, safer and formal picture
of information systems: the simple life semantic partially represented in the
Schema
Occurrences conform or agree with
the schema
The Traditional View of Information Systems : 6 The Traditional View of Information Systems When do semantic issues arise? Schema Schema is created by several designers
Each handles a part of the system Schema1 … Schema n Integrated Schema Semantic reconciliation: easy
Make all designers agree on
Differences in terms or concepts
The Traditional View of Information Systems : 7 The Traditional View of Information Systems Main goal of the centralized system is to process company information Instances / Occurrences Schema Typical Applications:
Payroll
Managing enterprise project or
production system
Information Systems in Networked Environment : 8 Information Systems in Networked Environment Distributed systems and widespread resource sharing evolve from two major developments :
Powerful, efficient and smaller computers and processors
efficient communication concepts:
Internet
Wireless communication
Ad hoc, mobile networks (sensors, PDA, other devices …)
Information Systems in Networked Environment : 9 Information Systems in Networked Environment Data have also evolved in:
Complexity (image, multimedia, hypermedia)
Volume (and storage requirement)
Type
So the related semantic is also evolving in complexity
Information Systems in Networked Environment : Information Systems in Networked Environment The main IT goal is
Not only to process enterprise information
But also to sharing information
Global need: a system must handle information from a variety of sources (proprietary data, public information in web pages, information in web services) 10
Information Systems in Networked Environment : Information Systems in Networked Environment What are the new challenges? Discovering and extracting relevant information 11
Information Systems in Networked Environment : 12 Information Systems in Networked Environment How does the traditional view evolve when we have multiple information systems? Instances / Occurrences Schema May not be present
Multiple schema in distributed environments
Schema May be expressed using different models
Description levels, precision and power may be different
Formal description may co-exist with natural language descriptions
Information Systems in Networked Environment : 13 Information Systems in Networked Environment How does the traditional view evolve when we have multiple information systems? Instances / Occurrences Schema Occurrences distributed over multiple sites
Replicated or duplicated over sites
Fragmented over multiple sites
Information Systems in Networked Environment : 14 Information Systems in Networked Environment , ... , Schema Schema , ... , Schema , ... , High level Cooperation
or interoperation of IS:
Information sharing Low level
Communication Network
Information Systems in Networked Environment : Information Systems in Networked Environment If we could build a Global Semantics 15 15 , ... , Schema Schema , ... , Schema , ... , Global Semantics Query Virtual Integrated
Information System
Information Systems in Networked Environment What are the main issues? : 16 Information Systems in Networked Environment What are the main issues? , ... , Schema Schema , ... , Schema , ... , Semantics What it is?
How to best represent the semantics of data?
How to reconcile differences in semantics?
What about missing semantics?
Data IntegrationA Higher-Level Virtual View : Data IntegrationA Higher-Level Virtual View The basic Idea 17 Mediated Schema Query S1 S2 S3 SSN Name Category 123-45-6789 Charles undergrad 234-56-7890 Dan grad … … SSN CID 123-45-6789 CSE444 123-45-6789 CSE444 234-56-7890 CSE142 … CID Name Quarter CSE444 Databases fall CSE541 Operating systems winter … … Semantic Mappings Independence of: • source & location • data model, syntax • semantic variations • … The best of
Carreras
Pavarotti
Domingo
19.95
Application Areas : Application Areas Business applications 18 Single Mediated View Legacy Databases Services and Applications Enterprise Databases Portals … Ent. Integration
Applications Business analysis
Application Areas : Application Areas Science (Bioinformatics) 19 OMIMSwiss- Prot HUGO GO Gene- ClinicsEntrez Locus- LinkGEO Sequenceable Entity Gene Phenotype
Structured Vocabulary
Experiment ProteinNucleotide Sequence Microarray Experiment Hundreds of biomedical data sources available; growing rapidly!
Application area : 20 Application area Scientific Data Grid (Physics)
CERN’s EDMS :
PDM (Product Data Management)
Engineering Data Management System
MDAS : Massive Data Analysis System
San Diego Supercomputer Center 95-97, DARPA financed
Manage resources in a heterogeneous distributed system
Metadata and data description
Detect available resources, storage spaces
Application Areas : Application Areas WEB
E-commerce (Amazon.com, Barnes and Nobles)
E-tickets reservation
Online hotel Reservation 21
The Semantic Web[Berners-Lee] : The Semantic Web[Berners-Lee] To allow knowledge sharing at the web scale (interaction between Machines or users)
Web resources must be described by ontologies (precise explicit semantics)
Need rich domain model
Powerful standards (RDF/OWL) 22
The Semantic Web[Berners-Lee] : The Semantic Web[Berners-Lee] Challenges:
Complex Semantic integration issues at the web level (This may be too complicated for non technical end user, unless fully automated)
Lack of convincing applications at the semantic web level 23
The Semantic Web[Berners-Lee] : The Semantic Web[Berners-Lee] Where are the real obstacles to the semantic integration?
Systems
Managing different platforms
Query processing across multiple platforms
Social
Locating and capturing relevant information in the enterprise
Convincing people to share data (privacy and performance reason
Logic
Schema and heterogeneity 24
Virtual EnterprisesWorkflow model : Virtual EnterprisesWorkflow model Challenges
Decentralized organizational structures
Variety of Information Sources and Services
Virtual EnterprisesWorkflow model : Virtual EnterprisesWorkflow model Issues: operational aspects of the business process
Interoperability
Autonomy
Openness and sharing information
Dynamic Participation, Mergers and Acquisition
On-the-Fly Integration 26
Workflow Management Syst. : Workflow Management Syst. WfMS Languages
WSDL : basis for many inter-organizational workflow specification language
Other languages: ebXML, WSCL, XLANG, BPML
Problems with languages
Advanced but lack of common taxonomy
Do not support different views of the workflow
Solution
Agreement based inter-organizational workflow
Agreement based Workflow Model : Agreement based Workflow Model Local Modeling view
Each org creates a local/personal view of its own workflow
Based on Loose IOW platform View-b WFLa WFAb View-c WFAc WFLa WFAb Global View WFAc WFLa WFAb Agent Negotiation Agent Negotiation Agent Negotiation Compatibility Analysis Compatibility Analysis Compatibility Analysis
Semantic Modeling perspectives: Ontologies : Semantic Modeling perspectives: Ontologies An ontology provides a shared representation and understanding of data and services in a common domain
Use ontology for interaction between people and application systems. 29
Semantic interoperability : Semantic interoperability Ability of a system component to provide information sharing and inter-application cooperative control
Ontologies are used as a comparaison reference
Ontologies are forms of a-priory agreement on concepts, therefore their use is insufficient in ad hoc dynamic environment where all possible interpretations are anticipated 30
Semantic interoperability : Semantic interoperability Interoperation of systems X and Y 31 Information
system X Information
system Y Request R Response Y Mutual understanding of R and Y Ontology :
Partial?
Global?
Predefined?
Semantic interoperabilityMining semantic from the instance : Semantic interoperabilityMining semantic from the instance 32 WEB pages containing
Instances Extract Semantic
Based on similarity
Of instances Missing values?
Requires an ontology?
Maintenance?
Extraction based on cluster
Similar instances?
Semantic interoperabilityMining semantic from the instance : Semantic interoperabilityMining semantic from the instance How to define similar instances in this case?
Bookstore Example (Amazon.com), consisting of :
Items (books, etc)
Customers (millions)
Each book is represented by a vector such that V[i] is 1 if customer i bought a copy of the book 33
Semantic interoperabilityMining semantic from the instance : Semantic interoperabilityMining semantic from the instance How to define similar instances in this case?
Books bought by the same groups of customers?
Use other clustering method
We plot the vector in an n dimensional space such that each dimension represents a customer and each point defines the customers who bought the same book 34
Semantic in Dynamic EnvironmentP2P - Self Organization Systems : Semantic in Dynamic EnvironmentP2P - Self Organization Systems Example of Semantic Overlay P2P Networks (SONs).
Peers with similar content are clustered together on content hierarchies (similar to ontologies)
Semantic Overlay Networksfor a music application : Semantic Overlay Networksfor a music application A B C D H F E G Rock Rap R o
Classification Hierarchiesof the semantic overlay network : Classification Hierarchiesof the semantic overlay network Music Rock Soft Dance Pop Sub style Jazz New Orleans Bop Fusion … … Music ≥10’s Now … 90’s Music Warm Sweet … Exiting Decade Tone
Generating semantic Overlay Networks : Generating semantic Overlay Networks SONs Query Classifier Query Node Classifier Document Classifier New Nodes SON Definition Concept hierarchy Data Distribution Query Result
Conclusion : Conclusion We have shown that
Semantics is ever pervasive in information systems
Semantic integration of information systems is a hard problem:
Because Schema when present never captures the intended meaning of information
Because automatic resolution of differences is not yet satisfactory
Because technological, social and logic obstacles still exist 39
Conclusion : Conclusion We have shown that
Ontologies are increasingly used in integration solutions
Ontologies based have limitations when used in ad hoc dynamic environments
One “modest” objective of integration solutions should be to limit the required human effort 40
That’s All : That’s All Thank You