SemTecBiz 2012: Corporate Semantic Web


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Presentation by Adrian Paschke about Corporate Semantic Web – The Semantic Meets the Enterprise, at SemTechBiz Berlin 2012, Feb, 6-7, 2012

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SemTecBiz 2012: Corporate Semantic Web

  1. 1. AG Corporate Semantic Web Freie Universität Berlin Corporate Semantic Web Prof. Dr. rer. nat. Adrian Paschke, Freie Universität Berlin, Corporate Semantic Web SemTech Conference, 6-7. February 2012, Berlin, Germany
  2. 2. 2 Agenda • About Corporate Semantic Web • Corporate Semantic Engineer • Corporate Semantic Search • Corporate Semantic Collaboration • Summary and Future
  3. 3. 3 Semantic Web – An Introduction • "The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation." • Tim Berners-Lee, James Hendler, Ora Lassila, The Semantic Web • „Make the Web understandable for machines“
  4. 4. 4 Semantic Technologies 1. Rules • Describe conclusions and reactions from given information (inference) • Declarative knowledge representation: “express what is valid, the responsibility to interpret this and to decide on how to do it is delegated to an interpreter / reasoner” 2. Ontologies • Ontologies described the common knowledge of a domain (semantics): • “An ontology is an explicit specification of a conceptualization “ T. Gruber  Semantics interoperability between (connected) vocabularies
  5. 5. 5 About Corporate Semantic Web 1. Application of Semantic Web technologies in enterprise information systems (Semantic Enterprise) • Collaborative workflows and (business) process management (e.g. e-Science workflows, Semantic Business Process Management) • Knowledge Management (e.g. Semantic Knowledge Management, Semantic Corporate Memory) 2. Corporate = Business Context • Application of Semantic Web technologies under economical considerations and business conditions (e.g. cost models, return on investment)
  6. 6. 6 Corporate Semantic Web for Semantic Enterprises Corporate Semantic Web •Semantic Applications •Semantic Knowledge •Semantic Content Front Office Back Office Customer Portals Call Center E-Commerce CRM SCM CSCWDBMSBPM ITSM ERP SRM
  7. 7. 7 Challenges for the Corporate Semantic Web Syntax Sematics Pragmatics Data Understanding Connectedness Information / Content Knowledge Intelligence / Wisdom Understanding relations Understanding patterns understanding principles
  8. 8. 8 Semantic Content (Semantic Data) 1. Automatic extraction of semantic from non-semantic data • Linked Data Extraction • Ontology Learning 2. (New) Semantic Data and Knowledge Engineering and Development • Manual (e.g. semantic text editor, semantic Wiki, semantic CMS, ontology-/rule-engineering) • Automated (e.g., user activity mining, text analysis)
  9. 9. 9 Semantic Knowledge Semantic Knowledge Management and “Semantic Organizational Memory" • Relevant knowledge • e.g. reuse of knowledge, faster search, faster knowledge transfer, efficient processes, etc. • Semantic archives and knowledge repositories • e.g. Linked Data, knowledge clouds, semantic Wikis, semantic knowledge bases such as triplestores, semantic personal CMS, etc. • Semantic integration of data from different heterogeneous sources of corporate knowledge • Analysis of the semantic data, in order to detect implicit knowledge and semantically represent it
  10. 10. 10 Semantic Applications (Semantic Intelligence) Semantic applications for • Corporate Semantic Engineering • Methods and tools for the management of corporate information and processes • Support for the development of semantic enterprise solutions and products/services • Semantic Corporate Search • Solutions for semantic search in information repositories • Semantic Corporate Collaboration • New semantic collaboration platforms with which information, processes and knowledge can be collaboratively share, used and managed
  11. 11. 11 • Learning and Training • Decision makers and employees • Economic considerations, • i.e. business context • Estimation of costs and benefits • Development and usage of new Corporate Semantic Web technologies • Incentives for adoption and use of semantic technologies Pragmatics
  12. 12. 12 Corporate Semantic Web Corporate Semantic Web Corporate Semantic Engineering Corporate Semantic Search Corporate Semantic Collaboration Public Semantic Web Corporate Business Information Systems Business Context www.corporate-
  13. 13. 13 Domains of the Corporate Semantic Web • Corporate Semantic Engineering • Methods and tools for the precise, high-quality and economical development and management of ontologies and rule bases for business information and processes • Semantic support for the software and process engineering • Semantic Corporate Search • Solutions for the semantic search in controlled information resources with defined quality of service improvements • Semantic Corporate Collaboration • New semantic collaboration and support platforms with which different enterprise domains or parts of virtual organizations can collaboratively collect, use and manage information, processes / services and knowledge
  14. 14. 14 • Ontology modularization and integration • Ontology versioning • Ontology cost estimation models for corporation • Ontology evaluation Corporate Semantic Engineering Corporate Semantic Engineering
  15. 15. 15 Example: Corporate Ontologies • Ontology supported Semantic Knowledge • Semantic Bridges between Heterogeneous Information Systems • Asynchronous evolution of the stand-alone systems and underlying corporate (background) knowledge Corporate Wikis Corporate Blogs Corporate Websites Corporate Ontologies CRM Corporate Structure
  16. 16. 16 Selection/Integration/Development EvaluationValidation Feedback Tracking Population Deployme nt Reporting ENGINEERING USAGE Corporate Ontology Lifecycle Model (COLM) Example: Ontology Engineering and Life Cycle
  17. 17. 17 Example: Modularization and Integration Integrated View Modul 1 … … Modul n Modul 2 Modul n-1 Core Ontology Domain Ontology Application Ontology Domain 1 Domain 2
  18. 18. 18 Semantic Corporate Search • Search in non-semantic data • Search personalization • Multimedia search • Search contextualization Corporate Semantic Search
  19. 19. 19 Example Personalized Search Skill Ontology Example: Query „Java“ (+ Personal Skill Profile (Java + C++ Knowledge) ) d (Java, C++) = d (Java, Object Oriented) + d (C++, Object Oriented) = (0.25-0.0.0625) + (0.25-0.0625) = 0.375 sim(Java, C++) = 1 – 0.375 = 0.625 (Semantic Similarity) => also propose job offers for C++ programmer
  20. 20. 20 Semantic Search Iterative search by the user. Advantage: low entry costs Challenege: query strategy Text corpus is fact base. Advantage: unstructured content accessible Challenge: ask a valid question Background-knowledge used during search. Advantage: captures all latent answers Challenge: Ontology design
  21. 21. 21 Semantic Corporate Collaboration • Knowledge extraction by mining user activities • Collaborative tools for modeling ontologies and knowledge • Dynamic access to distributed knowledge • Evolution of ontologies and knowledge by collaborative work Corporate Semantic Collaboration
  22. 22. 22  Information Sources: Knowledge Management: Workflows Knowledge Semantik Information  Events & Process Context Relations & Interpretation  Content BPM BPMBPM BPM Work flow Workflow Literature Colleagues Databases Experts Product Contents Example: Semantic Collaboration Workflows and BPM Business Processes
  23. 23. 23 Example: Mediated Semantic Business Process Modeling Heterogeneous Corporate/Domain Ontologies
  24. 24. 24 Example: Semantic Business Process Management % receive query and delegate it to another party rcvMsg(CID,esb, Requester, acl_query-ref, Query) :- responsibleRole(Agent, Query), sendMsg(Sub-CID,esb,Agent,acl_query-ref, Query), rcvMsg(Sub-CID,esb,Agent,acl_inform-ref, Answer), ... (other goals)... sendMsg(CID,esb,Requester,acl_inform-ref,Answer). •Paschke, Rule Responder BPM / ITSM Project •Barnickel, Böttcher, Paschke, Semantic Mediation of Information Flow in Cross-Organizational Business Process Modeling, 5th Int. Workshop on Semantic Business Process Management at ESWC 2010 •Adrian Paschke and Kia Teymourian, Rule Based Business Process Execution with BPEL+ , i-Semantics 2009, Graz • Paschke, A., Kozlenkov, A.: A Rule-based Middleware for Business Process Execution, at MKWI'08, München, Germany, 2008. Rules-enabled BPEL+ Application BPEL run- time BRMS (Business Rules Management System) events , facts results CEP Logic Reaction Logic Decision Logic Constraints Rule Inference Service SBPMN -> BPEL+ Prova Rule Engine Oryx SBPM
  25. 25. 25 Corporate Semantic Web What comes next?
  26. 26. 26 Corporate Semantic Web Corporate Semantic Web (CSW) focuses on the application of Semantic Web technologies and semantic Knowledge Management methodologies in corporate environments.
  27. 27. 27 Corporate vs. Public Semantic Web • Closed information systems / Intranet solutions with often known interfaces between systems, services and domains • Known user groups within enterprise network(s) • Usage of the existing enterprise IT infrastructure, information, and knowledge is constrained by the existing business rules, policies and workflows/processes • Data view: closed, often structured data with known data models (e.g., relational, object-oriented, XML, …) • Logic view: partial closed world assumption, partial unique name assumption, scoped constructive views
  28. 28. 28 Social Semantic Web vs. Corporate Semantic Web • Social Semantic Web = Web of collective knowledge systems • Focus: Tools in which the central social interactions on the Web plays a role. These tools lead to the development of explicit semantic representations • Combines technologies, strategies and methods of the Semantic Web, Social Software and Web 2.0 • Finds applications in Corporate Semantic Web as well as Public Semantic Web
  29. 29. 29 Pragmatic Web The Pragmatic Web consists of the tools, practices and theories describing why and how people use information. In contrast to the Syntactic Web and Semantic Web the Pragmatic Web is not only about form or meaning of information, but about interaction which brings about e.g. understanding or commitments.
  30. 30. 30 Pragmatic Web Vision: Ubiquitous Pragmatic Web 4.0 Monolithic Systems Era Desktop Computing Desktop World Wide Web 1.0 Connects Information Syntactic Web Semantic Web 2.0 Connects Knowledge Social Semantic Web 3.0, Web of Services & Things, Corporate Semantic Web Connects People, Services and Things Ubiquitous Pragmatic Web 4.0 Connects Intelligent Agents and Smart Things Semantic Web Ubiquitous autonomic Smart Services and Things Pragmatic Agent Ecosystems Machine Understanding Ubiquitous Next Generation Agents and Smartl Connections Syntactic Web Semantic Web Pragamtic Web HTML XML RDF Smart Agents Content Producer Passive Active Consumer
  31. 31. AG Corporate Semantic Web Freie Universität Berlin
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