Engenharia de Software e Ontologias: Contribuições ...
Software Engineering and Ontological Engineering: Contributions, Perspectives, Challenges and Learned Lessons Marcelo Jos Siqueira C. de Almeida [email protected] Summary
Introduction Motivation SE and OE close or too far away? Scenario Collaborations Basic Definitions Context Software Engineering Knowledge Engineering Ontological Engineering
Summary Similarities and Differences between SE and OE Reflection Open Issues Contributions of SE for OE Contributions of OE for SE
The Big Picture Conclusions References Introduction With the advent of Semantic Web, ontologies have gained interest from the mainstream of Computer Science in the development of different kinds of applications: Knowledge Management Natural Language Processing E-Commerce Intelligent Integration of Information Information Retrieval Data Base Integration and Project Computer Network and Distributed System Management
Motivation Growing demands for ontologies push the community of ontology developers and researchers to apply systematic engineering approaches. Important themes: Reuse Quality Market Standards SE and OE: close or too far away?? Despite of sharing a certain quantity of topics,
Software Engineering (SE) and Ontological Engineering (OE) communities have been working separately. Is there room for effort integration? What are the learned lessons? How can each part profit from this union? Scenario 1 Apply SE for Ontology development Ontological Engineering Scenario 2 (Ontology Based)
Collaborations Ontologies May support the Development of new SE approaches Software Engineering Inspirations for the OE development and maturing
Basic Definitions According to (IEEE 1990): Technique: a technical and managerial procedure used to achieve a given objective. Method: a set of orderly processes or procedures used in the engineering of a product or performing a service. Methodology: a comprehensive, integrated series of techniques or methods creating a general theory of how a class of thought-intensive work ought be performed. Context AI OE
KE SE Software Engineering SE is the application of a systematic, disciplined, quantifiable approach to the development, operation, and maintenance of software (Abran and Moore 2004). Mimic of traditional engineering to cope with the software complexities. SE pushed the efforts from low-level programming to high-level modeling efforts. Today SE is a reputed discipline for the market (gradual and slow - process). Software Engineering
Standardizations OMG (Object Management Group) JCP (Java Community Process) SWEBOK (Software Engineering Body Of Knowledge IEEE/COMSOC) Software Engineering Component-Based Development A higher level of abstraction than objects and as such they do not share state and communicate by exchanging messages carrying data (Wikipedia). High level encapsulation of functionalities DCOM (Microsoft), EJB (Sun), XPCOM (Mozilla), etc. Main Benefit: Reusability
Cheaper Faster Simpler Less errors documented Software Engineering Methodologies RUP (Rational Development Process) (IBM 2008) It is a comprehensive process framework that provides
industry-tested practices for software and systems delivery and implementation and effective project management. It is based on sound software engineering principles such as taking an iterative, requirements driven, and architecturecentric approach to software development (Kruchten 2004) Object Oriented and/or component based development UML documentation Software Engineering Software Engineering Agile Methods Agile Manifesto Requirements never stop to change Excessive control freeze the teams Results faster
Software Engineering Methodologies XP (eXtreme Programming) (Beck, 2004) It is an agile method to develop software faster, cheaper and less error-prone Focus on programming (code available all the time!) User-centered (user is a stakeholder) Principles and values Suited to not well known domains, poorly specified, simple and small problems. Small teams Software Engineering
Software Engineering Aspect Oriented Programming Simplify the modularization of common concerns amongst the different modules of an OO application. Allows separation of Cross-Cutting Concerns. Example: Transactions Real Time restrictions Fault Recovery
Aims to completely meet the user requirements: Time, money, errors, performance, etc. Main efforts: CMMI ISO/IEC 15504 MPS.BR Software Engineering CMMI (Capability Maturity Model Integration) Evaluates if an organization conducts its processes in a mature way in order to obtain better products Discipline, testing, documentation, quantification, optimization Integration of existing models:
ISO/IEC 15504 International standard for software process assessment. Originated in SPICE (Software Process Improvement and Capability dEtermination) Project Influenced by CMMI model and ISO/IEC 12207. Based on two dimensions: Process: observes the execution of tasks. Capacity: similar to CMMI. Needs ISO/IEC 12207 to be executed. Problem: excessive bureaucracy. Software Engineering MPS.BR Melhoria do Processo de Software Brasileiro
Introduces the SE principles focusing the micro, small and medium organizations according to Brazilian market reality. CMMI and ISO/IEC 15504 complaint Finer granularity Problem: valid only in Brazil Software Engineering MDA (Model Driven Architecture) OMG standard Model is the most important artifact of the software development Model Transformation in two phases: PIM (Plataform Independent Model) PSM (Plataform Specific Model)
Advantage: Reduction of time, cost and complexity during the software development and management Software Engineering Bussiness Requirements Reality Plataform Independent PIM
Software Engineering Patterns Tested and documented form to reach a objective (Fowler, 2002). SE community has hardly been looking for reusability in all software life cycle: Analysis Design Coding
Testing Architectural Probably the most used ones are the Design Patterns Classical book: Elements of reusable OO SW (Gamma et al., 1996) 23 patterns Extensions: JEE, JME, SMAs, etc. Knowledge Engineering Regards to the development of expert systems, evolving methodologies and techniques of knowledge representation (Happel and Seedorf, 2007).
It refers to the building, maintaining and development of knowledge-based systems (Kendal, 2007). Great deal in common with SE, knowledge management, ontologies and conceptual modeling; Knowledge Engineering Phases of KE (Allen, 1982; Freitas, 2007) Knowledge Level Logical Level Implementation Level
Acquisition Formalization Implementation Refinement Natural Language Knowledge Representation Language Programming Language
KE KB Knowledge Engineering Methodologies CommonKADS TOGA (Top-down Object-based Goal-oriented Approach) Ontological Engineering Refers to (Corcho et al., 2006): the set of activities that concerns the ontology development process; the ontology life cycle; the principles, methods and methodologies for
building ontologies; the tool suites and languages that support them. Ontological Engineering Principles for constructing ontologies (Gruber, 1993): Clarity: Effective communication. Extendibility: Monotonic reasoning. Coherence: logical soundness. Minimal Encoding Bias: independence of a particular symbol-level encoding. Minimal Ontological Commitment: consistent use of a vocabulary. Ontological Engineering
Methods and methodologies Cyc (Leena and Guto, 1990) Its goal was to create a broad knowledge base with general information of common sense. Specifically developed to an ontology of same name. TOVE (Grninger and Fox, 1995) TOronto Virtual Enterprise (University of Toronto) Inspiration came from the development of knowledgebased systems using FOL Ontological Engineering Methontology (Gomz-Prez et al., 1997) Build ontologies from scratch, from other existing ones and also support reengineering. Based on the standardized software development process of IEEE
(IEEE 1996) and KE methods. Ontological Engineering Methontology Ontological Engineering 101 (Noy and McGuinness, 2001) Developed in KSL (Knowledge Systems Laboratory) of Stanford University Interactive and very simple approach Closer to be a set of guidelines formulated by experienced persons, but far from being a process Tool-centered (Protg, Ontolingua and Chimaera) Ontological Engineering
OntoAgile (Parente, 2008) Based on the agile methodologies XP and Scrum. Less documentation and more user interaction Limitation: Intended to development of small-size ontologies Ontological Engineering Construction Concept Acquisition Existing Ontologies Relationship (Table of Concepts)
Planning Meeting Delivery Meeting Integration Codification Consistency Verification Ontological Engineering RapidOWL (Auer and Herre, 2006) Based on the idea of iterative refinement, annotation and structuring of a knowledge base. A central paradigm is the focus on smallest information chunks (RDF chunks).
Influenced by XP.K (eXtremme Programming of Knowledge-based systems) and the wiki approach. Ontological Engineering Values Transparency Courage Simplicity Community Incremental
Organic Uniform Open Obsevable Convergent WYSIWYM Rapid Feedback
Principles Simple KM Interactive Cooperation Modeling Standards Joint Ont Design View Generation
Cons. Checking Ont .Evol. Short Releases Communit Modeling Information Integration Practices
Ontological Engineering Tools (Corcho et al., 2006) Specific Language Ontology Environments Ontolingua Server (Ontolingua and KIF) OntoSaurus (Loom) WebOnto(OCML) OilEd (OIL and DAML+OIL) SWOOP and KAON2 (OWL)
Ontological Engineering Tools (cont.) Language Independent Integrated Environments Protg WebODE OntoEdit KAON1 Ontological Engineering State of Art
Modularization Requirements Traceability Development Patterns Ontological Engineering Modularization (Spaccapietra et al., 2005) The main aim is to provide scalability in design, use and maintenance of ontologies (Stuckenschimidt and Klein, 2003) Distributed ontologies Large Ontologies Reusability Ontology composition and decomposition
Ontological Engineering Module 1 Resultant Ontology Module 2 Module 3 Ontological Engineering Alignment Process of determining correspondences among concepts (Euzenat, 2004) Distinct vocabularies and structures Ontology 1
Ontology 2 Car Vehicle + = ?
Ontological Engineering Development Patterns (Blomqvist, 2007; Gangemi, 200?) Patterns have been successfully applied as a means for facilitating reuse and managing complexity in many areas. Reusability has gained increasing interest by the ontology community Complexity is a serious problem in the management of ontologies. Ontological Engineering Proposal of a Pattern Classification(Blomqvist and Sandkuhl, 2007):
Application Patterns Architecture Patterns Design Patterns Semantic Patterns Syntactic Patterns Similarities between SE and OE Historical contexts Necessity for systematic approaches in order to obtain better, cheaper and quickly developed products . Development based on a well defined set of steps: Viability, specification, analysis, implementation,
integration, tests, etc. Reusability as a driving force Differences between SE and OE The result of SE is a running software and the result of an OE is an ontology. SE community is very concerned to quality questions. SE is a very mature discipline, but OE is still in its early years. Differences between SE and OE Topic
SE OE Modularization Mature State of Art Documentation Mature Still Amateur
Standardization Efforts Good (despite of chaos) Inexistent Tools Big diversity Good evolution Patterns
Mature Beginning Quality Assessment Process and Product Product (Early steps) Methodology Good acceptance Little acceptance
Multidimensional Modeling Yes No Heavy Weight Methodologies RUP, Clean Room, OpenUP Methontology, TOVE, Cyc Light Weight Methodologies
XP, Scrum, Crystal OntoAgile, RapidOWL Reflections Why not simply to apply Software Engineering methodologies to ontology development? Ontologies are software But ontologies arent applications! Knowledge and semantic requirements
Lack of competence from the SE community to deal with KBS Application Ontology Data Reflections Is OE just a branch of SE? Ontologies as a software artifact Will the traditional SE approaches really adopt OE or are we just surfing in a hype? SE OE Conclusions
The AI community has given an important step forward into the market: Necessity to develop an Engineering approach. Knowledge-Based Systems for the masses (?) OE and SE communities have benefited mutually Benefits could be broader if efforts could be integrated in common projects. Market developers woke up to ontology power but there is still a long way to walk away to achieve a large adoption. Despite all OE advance there are many gaps yet! Standardization Component-based development Quality assessment
Conclusions No existing methodology approaches the recent advances in OE. The real understanding of differences and similarities between OE and SE must comes with the understanding of differences and similarities between ontologies and software applications. Maybe in the future there wont be a line between them: Semantic web as a development platform similar to what happens today in Web 1.0 and Web 2.0. References
Allen (1982), The Knowledge Level, Abran and Moore (2004), Auer and Herre (2006), Beck (2004), Programao Extrema Aplicada, Ed. Bookman, 2004. Blomquist (2007),
Blomquist and Sandkuhl (2007), Patterns in Ontology Engineering: Classification of Ontology Patterns. Calero et al. (2006), Ontologies for Software Engineering and Software Technology, Springer, 2006. Corcho et al. (2006), Euzenat (2004). An Ontology Alignement API. Fred (2007), Engenharia do Conhecimento. Notas de Aula. Avaliable on http://www.cin.ufpe.br/. Last Access on 08 october 2008. Fowler (2002), References
Gamma et al. (1996), Elements of Reusable Object-Oriented Software, Addison-Wesley, 1996. Gmez-Prez et al. (1997), Gomz-Prez et. al. (2004), Ontological Engineering, Springer, 2004. Jrg and eedorf, Applications of Ontologies in Software Engineering, Kendal, Simon & Creen, Malcolm (2007). An Introduction to Knowledge Engineering. Springer. IBM, Rational Unified Process, avaliable at http://www-01.ibm.com/software/awdtools/rup/. Last access in 8 october 2008.8 References
Gruber (1993), Towards principles for the design of ontologies used for knowledge sharing. Originally in N. Guarino and R. Poli, (Eds.), International Workshop on Formal Ontology, Padova, Italy. Revised August 1993. Published in International Journal of Human-Computer Studies, Volume 43 , Issue 5-6 Nov./Dec. 1995, Pages: 907-928, special issue on the role of formal ontology in the information technology. Gruninger and Fox (1995), Happel and Seedorf (2007), IEEE (1990), Kendal (2007), Kosciansky and Soares (2007), Leena and Guto (1990) Natalya F. Noy and Deborah L. McGuinness. Ontology Development 101: A Guide to Creating Your First Ontology. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05 and Stanford Medical Informatics Technical Report SMI-2001-0880, March 2001.
References Parente (2008), Pressman (2006), Software Engineering, Mc-Graw Hill, 2006.
Spaccapietra et al. (2005), Soren Auer and Heinrich Herre. Rapidowl - an agile knowledge engineering methodology. In Irina Virbitskaite and Andrei Voronkov, editors, Ershov Memorial Conference, volume 4378 of Lecture Notes in Computer Science, pages 424430. Springer, 2006 Stvilia (2008), Nicola Guarino and Chris Welty, Conceptual Modeling and Ontological Analysis, AAAI-2000 Tutorial on Conceptual Modeling and Ontological Analysis (MP-2). July 31, 2000. Tempiche and , Turnban and Aronson (2008),
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