Ontology-based
Knowledge Management
in the Steel Industry
Chapter 11
B. Ramamurthy
1
Introduction
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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.
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Steel Industry Context
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Arcelor Mittal: world’s 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?)
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Critical Business areas
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Business optimizations: supply chain, sales,
purchasing, marketing
Customer solutions based on knowledge (ex: China
is hosting Olympics… 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)
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Solution basis
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Data mining
Knowledge-based systems
Simulations of optimization techniques
Semantic web
ArcelorMittal collaborates with CTIC
Foundation for semantic web related
activities.
Together they provide steel industry standard
for W3C semantic web activity
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Motivation and Use Cases
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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
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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 agentbased as well as in interactive systems.
7
Knowledge Capitalization
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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?
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Ontologies and tools to exploit them
tools: semantic search, human resources networking and
management
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Knowledge capitalization: human
resources and networking
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Human resources in multinational company
Departments need to exchange professional
information: contacts, employee profiles, etc.
Typically reside in department’s hard drive
HRMS: Human Resource Management System: to
describe people, job requirements & qualifications.
Extensive Ontologies and taxonomies are available:
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Hierarchy
E-recruitment
Experts Assignment
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Unified data description layer
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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
10
XML can do’s and not
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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.
11
Adding Semantics to content
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How to do it?
Managing legacy DB:
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Choice 1: transform into relational db to ontology
collections (R2O) √
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)
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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
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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
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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
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All these can be semantic (composite) web
services in the model
Processes are decomposable
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Ontology for Business
processes
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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
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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?
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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.
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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
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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 Serviceenabled Enterprise
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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
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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
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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
Line
Is composed of/
is component of
Tool
Equipment
Supplies/
Supplied by
Products
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Enhancing Ontology Reuse
and Interoperability
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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
Line
Model
Line
Model
Component
Library
Line
Model
Common/shared
Line
Model
Line specifics
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Usage of Ontologies
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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
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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
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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
Syntactic
search
Ontology
Repository
JSF
Web
Interface
Application
services
Ontologies
Semantic
Queries
Ontology
access
Business
Objects
Comment
persistence
View
engine
Favorite
persistence
Data base
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ARIADNE: Enrichment of
syntactic search
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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
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Development of large ontologies
Semantic web services
Combining ontologies and rules
Development of more tools for leveraging
knowledge base
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