Content

1. Smart Knowledge Management Platform: Toward Development and Application of Decisional DNAE Szczerbicki, C Sanin, C Toro, J Posada, J Vaquero

2. Content Knowledge Knowledge Representation Set of Experience A Shareable Set of Experience: Decisional DNA Ontology Application

3. Knowledge and Set of Experience

4. Knowledge • Decision-making is supported by information. • Knowledge is considered a valuable possession of incalculable worth. • Knowledge seems to be the only true source of a nation’s economical and political strength, as well as, the key source of competitive advantage of a company (Drucker, 1995). • The means and the ability of acquisition of knowledge, through efficient transformation of information, can make the difference between the success and failure of an organization in the competitive environment of global economy and knowledge society.

5. Managers entered into Knowledge Administration. They want technologies that facilitate control of all forms of knowledge.

6. Knowledge • Knowledge is “the fact or condition of knowing something with familiarity gained through experienceor association” (Merriam-Webster Dict. 2004). • “Knowledge refers to information that enables action and decisions, or information with direction” (Becerra-Fernandez and Gonzalez, 2004). • Knowledge “originates and is applied in the mind of knowers” (Mitchell 2003). • Lin et al. (2002) describe the concept of knowledge as an organized mixture of data, integrated with rules, operations, and procedures, and it can be only developed through experience and practice.

7. “The only source of knowledge is experience.”Albert Einstein (1879 - 1955)

8. Knowledge Representation • As knowledge should be collected, we have to represent it in some form. • One of the most complicated issues about knowledge is its representation. Representing knowledge determines how knowledge is acquired and transformed from tacit knowledge to explicit knowledge. • A Knowledge Representation (KR) is a substitute for a thing itself - formal decision events. • A KR contains simplifying assumptions and, possibly, extra elements.

9. Knowledge Representation (KR) • The most generalized techniques of KR use logic, rules, or frames. • LOGIC implicates understanding the world in terms of individual entities and associations between them. • RULE-BASED systems view the world in terms of attribute-object-value and the rules that connect them. • FRAMES comprisethinking about the world in terms of prototypical concepts.

10. ** OBSERVED STATISTICS REPORT for scenario TVANIM ** Label Mean Standard Number of Minimum Maximum Value Deviation Observations Value Value TIME IN SYSTEM 26.956 34.643 83 8.202 170.770 ** FILE STATISTICS REPORT for scenario TVANIM ** File Label or Average Standard Maximum Current Average Number Input Location Length Deviation Length Length Wait Time 1 QUEUE INSP 0.863 0.873 4 1 4.060 2 QUEUE ADJT 1.610 1.307 4 1 51.526 ** ACTIVITY STATISTICS REPORT for scenario TVANIM ** What to represent?… Multiple applications result in decisions in different environments, producing formal decision events, each with different elements. We have defined four basic components that surround decision-making events – formal decision events.

11. U V If X>70 then K = good R If W<2 then Z = 2 Z = 0.78 If G=blue then B = high K = average X = 100 H = good RtÈRlÈRtl W = 1.5 G = blue Y = 210 B = high V = 8451.54 C 2X+3Y-V <= 3450 Vl Vt H>=Excellent G<>blue AND Y+70X<2500 F Max P=3X-2Y+RQ CtÈClÈCtl Max K=Excellent Min C=YQ AND B=high FtÈFlÈFtl Formal Decision Events The four components arevariables, functions, constraints, and rules, and constitute the basis for the knowledge structure.

12. Set of Experience - SOE Graphic idea: Set of Experience Ei = (Vi, Fi, Ci, Ri)

13. Variables Rules Functions Constraints Set of Experience - SOE SOE comprises a series of mathematical concepts (a logical component), together with a set of rules (a ruled based component), and built upon a specific event of decision-making (a frame component).

14. Set of Experience - SOE • It is a structure that can be used for multiple technologies performing formal decision events. • Uniqueness • Adaptable • Dynamic • It assists at: • reducing information restrictions, • developing knowledge and applying it, and • implementing technologies that act as knower and decider.

15. Competitor’s payment Level = $30 • Working Condition = GOOD • Firing = 10 • Competitor’s Firing = 14 • Promotional Chance = EQUAL • X1 = 2 • X2 = 9 • RESULTS • X1 = 5 • X2 = 7.5 • Payment Level = $30 • Status of Payment = COMPETITIVE • Status of Firing = VERY GOOD • Status of Promotion = VERY GOOD • Worker’s Morale = VERY GOOD Constraints 6X1+X2>=21 X1+2X2>=20 X1>=0 X2>=0 Functions Min Payment Level=3X1 + 2X2 Worker’s Morale >= GOOD X1 X2 Payment Level Competitor’s Payment level Status of Payment Working Condition Promotional Chance Worker’s Morale Firing Competitor’s Firing Status of Firing Status of Promotion Variables IF Payment Level>=Competitor’s Payment Level THEN Status of Payment=COMPETITIVE Rules IF Firing<= 1.2*Competitor’s Firing THEN Status of Firing=VERY GOOD IF Promotional Chance=EQUAL THEN Status of Promotion=VERY GOOD IF Working Condition>=GOOD &Status of Payment=COMPETITIVE & Status of Firing=VERY GOOD & Status of Promotion=VERY GOOD THEN Worker’s Morale=VERY GOOD Example of SOE

16. Applicable and Usable SOE

17. A Shareable SOE • The Set of Experience works with formal decision events from multiple applications. All of them having different languages, formats and structures, and therefore, being an obstacle to the continuous flow of information and knowledge. • A unique language facilitates the integration practice.

18. A Shareable SOE • Web technologies have developed several tools for integration of disparate systems and distributed applications, including standards or protocols. • Languages with a defined vocabulary, structure, and constraints for expressing information and knowledge. • Standards aim for a common language.

19. Standards HTML • XBEL • xCBL • XCES • XCFF • Xchart • xCIL • xCML • Xdelta • XDF • XForms • XGF • XGL • XHTML • XIOP • XLF • XLIFF • XLink • XMI • XML • XML Court • XML EDI • XML F • XML Key • XML MP • XML News • XML P7C • XML Query • XML RPC • XML Schema • XML Sign • XML TP • XML XCI • XMLife • XMLVoc • XMSG • XMTP • xNAL • XNS • XOL • XSBEL • XSIL • XUL • CBML • CDA • CDF • CDISC • CELLML • CFML • ChessGML • ChordML • ChordQL • CIDS • CIDX • CIM • CIML • CLT • CML • CNRP • Coins • ComicsML • Covad xLink • CP eXchange • CPL • CSS • CVML • CWMI • CXML • CycML • DaliML • DAML • DaqXML • DAS • DASL • DCMI • DDI • DeltaV • DESSERT • DIG35 • DLML • DML • DMML • DMTF • DocBook • DocScope • DoD XML • DOI • DPRL • DRI • DSD • DSML • DTB • DXS • EAD • eBIS-XML • ebXML • ECML • eCo • EcoKnow • eCX • ECIX • edaXML • EML • EMSA • eosML • ESML • ETD-ML • FieldML • FINML • FITS • FIXML • FLBC • FLOWML • FPML • FSML • GAME • GBXML • GDML • GEDML • GEML • GEN • GeoLang • GIML • GML • GXD • GXL • GXML • HEML • HITIS • HRMML • HR-XML • HTML • HTTP-DRP • HTTPL • HumanML • Hy XM • HyTime • ICE • ICML • IDE • IDML • IDWG • IEEE DTD • IFX • IML • IMPP • IMS Global • InTML • IOTP • IRML • IXML • IXRetail • JabberXML • JDF • JDox • JECMM • JigXML • JLife • JScoreML • JSML • KBML • LACITO • LandXML • LEDES • LegalXML • Life Data • LitML • LMML • LogML • LTSC XML • MAML • MathML • MatML • MBAM • MCF • MDDL • MDSI-XML • Metarule • MFDX • MISML • MIX • ML • MML • MMLL • MoDL • MOS • MPML • MPXML • MRML • MSAML • MTML • MusicXML • NAA Ads • NAML • Navy DTD • NewsML • NFF • NISO DTB • NITF • NLMXML • NML • NuDOC • NVML • OAGIS • OAMS • OBI • OCF • OCS • ODF • ODRL • OeBPS • Office XML • OFX • OIL • OIM • OLifE • OML • ONIX DTD • OODT • OOPML • OpenMath • OPML • OPX • OSD • OTA • P3P • PARLML • PCIS • PDML • PDX • PEF XML • PetroML • PGML • PhysicsML • PICS • PML • PMML • PNG • PNML • PrintML • PrintTalk • ProductionML • PSI • PSL • QAML • QML • QuickData • RBAC • RDDl • RDF • RDL • RecipeML • RELAX • RELAX NG • REPML • ResumeXML • RETML • REXML • RFML • RightsLang • RIXML • RoadmOPS • RosettaNet PIP • RSS • RuleML • SABLE • SAE J2008 • SAML • SBML • Schemtron • SDML • SearchDM-XML • SGML • SHOE • SIF • SMBXML • SMDL • SMI • SMIL • SML • SMML • SOAP • SODL • SOX • SpeechML • SPML • SSML • STEP • STEPML • STML • SVG • SWAP • SWMS • SyncML • TalkML • TaxML • TDL • TDML • TEI • ThML • TIM • TML • TMML • TMX • TP • TPAML • TREX • TxLife • UBL • UCLP • UDDI • UDEF • UIML • ULF • UML • UMLS • UPnP • URI/URL • UXF • vCalendar • vCard • VCML • VHG • VIML • VISA XML • VML • VMML • VocML • VoiceXML • VRML • WAP • WDDX • WebDAV • WebML • WeldingXMLXGM • WellML • Wf-XML • WIDL • WITSML • WML • WorldOS • WSIA • WSML • XACML • XAML • XBL • XBN • XBRL RuleML OptML • 4ML • ABML • ACAP • ACML • ACS X12 • ADML • AECM • AFML • AGML • AHML • AIF • AIML • AL3 • AML • ANATML • ANML • ANNOTEABGML • ANZLIC • APML • APPEL • APPML • AQL • ARML • ASML • ASTM • ATML • AWML • AXML • BannerML • BCXML • BEEP • BGML • BHTML • BIBLIOML • BIOML • BIPS • BizCodes • BLM XML • BML • BPML • BRML • BSML • CaseXML • CaXML SGML AIML PMML SNOML XML RDF LPFML XRML RFML MathML

20. A Shareable Set of Experience Among all of the languages, XML was chosen because: • Simplicity, • Transmits not just format, but also meaningful information, • Easy to understand, read, and write, • Allows to structure information and knowledge from a graph structure as labelled trees, • Permits defining restrictions for the document (XSD), • Supported by the W3C as a language for knowledge representation, and • XML is the leader method for application integration.

21. A Shareable Set of Experience Set of experience knowledge structure is able to be implemented in XML. <?xml version="1.0" encoding="UTF-8" standalone="no" ?> <!-- Set of Experience Knowledge Structure --> • <set_of_experience xmlns: xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:noNamespaceSchemaLocation="set of experience model.xsd"> <date>2004-11-11</date> <hour>14:10:00</hour> <creation> <application> Excel</application> <application> System</application> <filename> payroll.xls</filename> <filename> payroll.ces</filename> <comment> Example of set of experience</comment> <comment> Company Expert System </comment> </creation> <category> <!-- Category encloses this set of experience into a determined chromosome of the company --> <area>Human Resources</area> <subarea>Salary Office</subarea> <subject>Payment Level</subject> <subject>Worker's Morale</subject> </category>

22. Extending SOE as a KR

23. SOE Additional Considerations • The knowledge structure should be re-evaluated or adjusted by users. • It is the day-to-day experience and generation of formal decision events that make the knowledge structure more accurate. • The knowledge structure should acts as a trained element of “life” - Genetic History.

24. Extending SOE as a KR • Experience is acquired while decision-making is executed. Thus, new knowledge is produced while solving problems. • It can be compared to the process of construction of the psychological space of an organization. • Based upon Kelly’s theory of psychological space, we develop a knowledge structure to administer formal decision events, a structure that builds up this space with formal decision experiences. Then, this psychological space can be used for future decision-making processes based upon previous decision events.

25. Image credit U.S. Department of Energy Human Genome Program (http://www.ornl.gov/hgmis). Variables Rules Functions Constraints Extending SOE as a KR • Gene provides aPhenotype • Categorized  Chromosomes • Each SOE provides a value • Categorized according to type of decision

26. Set of Experience Knowledge Structure

27. Similarity Metric of SOEKS Let C be a subset of the universe U of sets of experience named context set. A boolean expression θ containing one or many restrictions on elements of the head of the set of experience is called a selection condition on sets of experience, e.g. θ = (area = “Human Resources” AND subarea = “Salary Office“ AND aim function = “Payment Level”).

28. Similarity Metric of SOEKS

29. Similarity Metric of SOEKS Because each set of experience has different elements that comprise it and each element has its own characteristics, similarity is examined separately for each of the elements; afterwards, a unique similarity measure is offered by combining their separate results. VARIABLES FUNCTIONS CONSTRAINTS RULES

30. Similarity Metric of SOEKS: Variables • Qualitative variables are prearranged. • Euclidean Metric with normalization. • Similar = 0, Non-similar = 1.

31. Similarity Metric of SEKS S1 = 0.68 S5 = 0.53 S2 = 0.31 S6 = 0.12 S3 = 0.74 S7 = 0.07 S7 = 0.07 S4 = 0.26

32. Groups of SOE by area are: Decisional Chromosomes Groups of chromosomes are = DECISIONAL DNA Variables Variables Variables Variables Variables Variables Variables Variables Variables Variables Variables Variables Variables Variables Variables Rules Rules Rules Rules Rules Rules Rules Rules Rules Rules Rules Rules Rules Rules Rules Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Constraints Constraints Constraints Constraints Constraints Constraints Constraints Constraints Constraints Constraints Constraints Constraints Constraints Constraints Constraints Extending SOE as a KR AREA 1 (marketing) AREA 2 (finances) AREA 3 (design)

33. Vi Decisional Chromosome Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Vi Decisional DNA Vi Vi Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Ct Decisional Gene or SOEKS Ct Ct Ct Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Rk Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Fj Decisional DNA Decisional DNA

34. Decisional DNA • It keeps: • the decisional history of a company, • the decisional experience of a company, • information that could be read by future generations of decision makers, • knowledge in an explicit way, and • knowledge that could be shared and commercialized.

35. Decisional DNAOntology-based

36. Ontology In philosophy: It is the most fundamental branch of metaphysics. It studies being or existence. Tries to find out what entities and what types of entities exist. In Computer Science: It is the explicit specification of a conceptualization. It is a description of the concepts and relationships in a domain. (Tom Gruber’s widespread accepted definition)

37. Ontology-based Technology • It is the field in which computer-based semantic tools and systems are developed • Main focus is in information sharing and knowledge management for querying and classification purposes. • Several domains of application: medical, chemical, legal, cultural, etc. • Commonly used in AI and KR.

38. Ontology-based Technology • Computer programs can use ontologies for a variety of purposes: inductive reasoning, classification, and problem solving techniques are the most common • Communication and sharing of information among different systems • Emerging semantic web systems use ontologies for a better interaction and understanding between different agent web-based systems

39. Modeling ontologies • Ontologies can be modelled using several languages; RFD and OWL are both expressed in eXtensible Markup Language-XML • OWL (Ontology Web Language) is a W3C Recommendation. • OWL facilitates machine interpretability of web content by providing additional vocabulary along with formal semantics. • Set of experience ontology-based can be a scenario for exploitation of semantic data.

40. SOE ontology-based • From the SOE in XML, an Ontology modelling process was performed using the Protégé editor (publications available) • Relationships among the different classes of the Ontology can be seen using a plug-in for Ontology visualization.

41. Modeling Set of Experience Ontology-based Relationships among the different classes of the Ontology can be seen using a plug-in for Ontology visualization.

43. Growing System • Decisional DNA is shared in this system • It is Community of Practice distributing knowledge • Development based on ontology web technology e-Decisional Community

44. Internal Analyzer layer Experience Creator Se1=Le1 Se2=Le2 BS1(r,f) PRIORITIES Si1=Li1 . . . . . . Si2=Li2 Ser=Ler BSk(r,f) . . . Li1 (r,f) Sim=Lim M1 Li2 (r,f) . . . M2 External Analyzer layer Intuition Creator . . . Lim (r,f) BS (r,f) I1(BSi)(l) Mn Le1 (r,f) . . . Le2 (r,f) Is(BSi)(l) . . . Ler (r,f) Ruler Creator R1(BSi,Vj) . . . Rq(Bsi,Vj) Knowledge-base layer Integration layer Risk Analyzer layer DIAGNOSIS SOLUTION KNOWLEDGE PROGNOSIS To sum up: Platform Process

45. Proposed Platform… • Understands that Knowledge is NOW an Administrative and Technological Matter • Reduces Information Restrictions • Develops Knowledge and Applies it • A System which is Knower and Decider • Technology able to capture and store formal decision events as explicit knowledge

46. Implementations Australia: From LIMS to LKMSPriority Research Centre for Energy, Decisional DNA for Smart Use of Energy Spain: SEMTEK, VicomTechSOEK and Decisional DNA for industrial maintenance Poland: GUT, Decisional DNA for banking sector .. coming evaluation of IBM IT applications: Rational Unified Process, Method Composer, and Portfolio Manager.

47. Where to from here?

48. ? KNOWLEDGE INFORMATION DATA

50. Reflexive Ontologies