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Ontology-based Knowledge Management

in the Steel Industry

Chapter 11

B. Ramamurthy

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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.

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Steel Industry Context 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

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)

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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 department’s 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 do’s 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) √

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

EquipmentToolLine

Products

Is composed of/is component of

Supplies/Supplied by

Performs/Performed by

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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)

ComponentLibrary

ComponentLibrary

LineModel

LineModel

LineModel

LineModel

Line specifics

Common/shared

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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

JSFWeb

Interface

App

licat

ion

serv

ices

Syntacticsearch

SemanticQueries

BusinessObjects

Viewengine

OntologyRepository

Ontologyaccess

Commentpersistence

Favoritepersistence

Ontologies

Data base

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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