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: 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?)

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

6. 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:

7. 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.

8. 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

9. 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

10. 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

11. 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.

12. 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

13. 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

14. 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

15. 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

16. 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.

17. 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.

18. 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).

19. 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)

20. 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.

21. 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

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

23. 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.

24. Ontology Meta-model High-level ontology (meta-model) Component Library Component Library Common/shared Line specifics Line Model Line Model Line Model Line Model

25. 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.

26. 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.

27. 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.

28. 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

29. 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.

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