Ontology-based Knowledge Management in the Steel Industry

1
Ontology-based Knowledge Management in the Steel
Industry

• Chapter 11
• B. Ramamurthy

2
Introduction

• An important aspect for businesses is knowledge
  and intelligence generation and management.
• Right knowledge and intelligence is important for
  right and timely decisions.
• We will discuss the approach used by steel
  industry to address knowledge and intelligence
  management.

3
Steel Industry Context

• Arcelor Mittal worlds number one steel company
• 330,000 employees
• 60 countries
• Geographical diversity Industrial activities in
  27 countries across Europe, Americas, Asia and
  Africa.
• Arcelor Research Knowledge Innovation (KiN)
  Center aims to classify, model and put into
  service the knowledge of this group.
• Knowledge-intensive tasks steer business
  processes (how?)
• Business processes are realized using services
  (WS) in the implementation (how?)

4
Critical Business areas

• Business optimizations supply chain, sales,
  purchasing, marketing
• Customer solutions based on knowledge (ex
  American relationship with Cuba has been
  improving steer business to pay attention to
  customer needs in this region).
• Industrial process support Factory-wide, line
  piloting, process models
• Cross-cutting service assistance (transversal
  service assistance) (ex services spanning
  multiple domains)

5
Solution basis

• Data mining
• Knowledge-based systems
• Simulations of optimization techniques
• Semantic web
• ArcelorMittal collaborates with CTIC Foundation
  (Center for the Development of Information and
  Communication Technologies) for semantic web
  related activities.
• Together they provide steel industry standard for
  W3C semantic web activity

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Motivation and Use Cases

• Knowledge capitalization tools
• Unified data description layer
• Supply chain management raw materials to
  finished products
• Ontologies are not new used for knowledge
  representation
• Ontologies will be used here to integrate?

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Ontologies for integration

• Structural clarity hierarchical structure vs.
  RDBMS
• Human understanding
• Maintainability
• Reasonability infer new knowledge
• Flexibility
• Interoperability (OWL suite)
• In summary, ontology is a powerful tool for
  knowledge management, information retrieval and
  extraction, and information exchange in
  agent-based as well as in interactive systems.

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Knowledge Capitalization

• Group of applications devoted to manage content,
  documents, and information, structured so that
  users can access knowledge, add and modify them.
• Content management systems, document management
  systems, wikis, dynamic web portals, search
  engines, etc.
• What is required?
• Ontologies and tools to exploit them
• tools semantic search, human resources
  networking and management

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Knowledge capitalization human resources and
networking

• Human resources in multinational company
• Departments need to exchange professional
  information contacts, employee profiles, etc.
• Typically reside in departments hard drive
• HRMS Human Resource Management System to
  describe people, job requirements
  qualifications.
• Extensive Ontologies and taxonomies are
  available
• Hierarchy
• E-recruitment
• Experts Assignment

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Unified data description layer

• Huge company built from many smaller companies
  incrementally
• All kinds of software widely varying levels of
  usages
• XML has emerged as a syntactical solution for
  inter-application data communication

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XML can dos and not

• Promotes reuse (XML parsers)
• XML instances can be checked for syntactical
  correctness against grammar (XML Schema)
• Can be queried (XQuery, XPath)
• Can be transformed (XSL)
• Can be wrapped using commodity protocols (web
  services)
• However they convey only structure they are
  meaningless (no semantics)
• Ontologies have the potential to fix this
  situation by providing precise machine-readable
  semantic descriptions of the data.

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Adding Semantics to content

• How to do it?
• Managing legacy DB
• Choice 1 transform into relational db to
  ontology collections (R2O) v
• Choice 2 Wrap relational databases with semantic
  interfaces
• Steel producers use models and simulation tools
  to predict or control impact of various events
  semantics can help in re-use of many existing
  models across departments, countries and
  organizations.
• Distributed searches can index multiple
  repositories, esp. in multilingual environments

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Supply Chain Management (SCM)

• Supply chain is a coordinated system of
  organizations, people, processes, and resources
  involved in moving a product or service from
  suppliers to customers.
• In AM (ArcelorMittal) is indeed quite complex
• Independent business units
• Mitigate delays in production process
• Variances in production times and product quality
• Managing orders and sub-orders
• Heterogeneous processes
• Supply chain modeling and simulation
• Highly dynamic
• Most data reside in heterogeneous systems
• Islands of automation
• Need to form a global model

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SCM Solution at AM

• Ontology engineering to support supply chain
  modeling
• Identify data and knowledge required for specific
  model
• Develop mechanisms to extract the above
  information
• Populate Ontologies with required knowledge
• Build simulation models and implant a generic
  procedure to fill the necessary input values

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A Business process Abstraction

• AM will use Supply Chain Operation Reference
  (SCOR) model developed by supply chain council.
• Ontology will be developed based on SCOR.
• SCOR is structured around five processes Plan,
  Source, Make, Deliver and Return
• All these can be semantic (composite) web
  services in the model
• Processes are decomposable

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Ontology for Business processes

• Ontology will address categories of the supply
  knowledge
• Process process cost, process quality
• Resource capacity of resource
• Inventory control policy
• Order demand or order quantity, due dates
• Planning forecast methods, order schedule
• Develop supply chain ontology help simulations
  and future system designs.

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Modeled Factory and Metallurgical Routes

• Application of ontology design and semantic web.
• A metallurgical route involves set of processes
  (realized using web services) from order to
  production.
• How can it help? What was the situation before
  introduction of semantics?
• Lack of modularity
• Lack of standards
• Lack of integration between business models and
  production rules
• Solution formal description of the concepts that
  occur in metallurgical routes.
• All concepts are formalized as ontology classes.
• These concepts or blueprints have to be agreed
  upon by different plants.
• This framework represents a common understanding
  of the products and production lines.

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Semantic Metallurgical route HotRollingMill

• Maximum/minimum entrance width
• Maximum/minimum exit width
• Productivity
• Thickness reduction capacity
• Input material is of type Slab
• Output material is of type HotRoll
• Adding semantic enabled each facility to add
  values to a semantic instance of the concept.
• Web services could query the facilities before
  processing orders (p.255) that is HotRollingMill
  will be available via a web service to the
  applications that need its information details.
• Ontology is centrally developed, and instances
  are kept at decentralized locations and served by
  WS.
• More intelligence is embedded in WS through
  addition of semantic to data results in less
  number of rules.
• Here is an example of services-enabled enterprise
  (AM).

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AM, The Ultimate Service-enabled Enterprise

• Semantic search Ontologies, metadata, thesauri
  and taxonomies (ARIADNE project)
• H.R. and networking Ontologies, international
  classifications and rules
• Unified data description layer Ontologies and
  data mediation
• Expert knowledge and industry process modeling
  Ontologies and rules
• Supply chain management Ontologies, SCOR model,
  semantic web services, rules
• Modeled factory Ontologies and rules
  (metallurgical routes, Visonto)

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Practical Experiences

• Ontologies are powerful mechanisms to capture
  knowledge.
• Knowledge is key factor in productivity.
• Sharing knowledge among employees perform similar
  tasks
• Overall productivity can be improved by transfer
  of knowledge from experienced employees to
  inexperienced ones.
• This is needed for spanning the gap in
  multilingual world, to improve understanding and
  productivity and to avoid industrial accidents
  and to provide best practices.

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Expert Knowledge and Industrial Process Modeling

• Metal working and factory modeling how to manage
  bottlenecks, solve inventory, and work in
  progress problems like line stoppages, and
  material defects, optimize production rates,
  determine plant capacity etc.
• Solution build a shared ontological abstraction
  of metallurgical concepts and to use it as an
  interoperable framework in production lines and
  product life cycle management.
• An ontology that focuses on process, equipments,
  problematic and best practices of continuous
  annealing line has been built.
• Different models are developed at different
  production lines which share many concepts there
  is need for reuse and interoperability.
• Solution ontology based services-enabled
  framework

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Generic Production Line (p.2527-258)
Process
Performs/ Performed by
Is composed of/ is component of
Equipment
Tool
Line
Supplies/ Supplied by
Products
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Enhancing Ontology Reuse and Interoperability

• Ontology language (OWL-Full, OWL-DL, OWL-Lite)
• OWL-DL (Description Language) was chosen for its
  expressiveness and for its support of
  computational completeness and decidability.
• Common semantics need to share same vocabulary
  and points of view.
• Meta-modeling multi-layering of concepts
  highest level described more general concepts and
  the lowest specific for each line intermediate
  layers describe common processes and equipment
  and tools.

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Ontology Meta-model
High-level ontology (meta-model)
Component Library
Component Library
Common/shared
Line specifics
Line Model
Line Model
Line Model
Line Model
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Usage of Ontologies

• Used for streamlining industrial equipment to
  perform steel fabrication
• Also help staff to maintain devices, control of
  processes, test product quality and other
  operations involving human intervention.
• RDF model allows information (from experts) as
  web resources.
• OWL has a annotation feature to add metadata
  information to any resource of an ontology.
• Ex rdfs comment, rdfs seeAlso
• Also applying a social network enhances the
  utility of the factory ontology.
• Experts share the same model of the whole process
  and they can interchange information and
  documents by means of the ontology.

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Visonto A tool for ontology visualization

• Ontology authoring protégé?
• No, they developed their own in collaboration
  with CTIC foundation.
• Can be customized within the ontology.
• View tree view heavily linked to web pages for
  knowledge dissemination
• Multilinguism is a key feature
  language-agnostic for domain knowledge with
  annotation in multiple languages, other subtle
  details such as units of measurement, monitory
  units and dates/time etc.
• Simple string-search based search query-based
  search based on SPRQL.
• Query by example interface a good choice
• Filter of information through points of view and
  other filters.

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Visonto Architecture

• Visonto is a web application, without any
  substantial software installed by the client.
• Knowledge sharing and collaborative environment.
  A common pool of Ontologies and comments.
• Long term plan involves adding reasoners,
  semantic web services.

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Visonto Architecture
Application services
JSF Web Interface
Syntactic search
Ontology Repository
Ontologies
Semantic Queries
Ontology access
Data base
Business Objects
Comment persistence
View engine
Favorite persistence
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ARIADNE Enrichment of syntactic search

• Another internal project
• Verity/autonomy K2 product
• Indexing spider gathers and builds repositories
  of all internal documents
• J2EE web user interface was built on top of the
  search engine API.
• Result is a powerful capitalization of company
  information.
• Web interface in Java and Jena framework.
• Search comparison in multiple languages.

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Open Issues

• Development of large ontologies
• Semantic web services
• Combining ontologies and rules
• Development of more tools for leveraging
  knowledge base