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Presentation by Yuri de Lugt. Presentation structure Definitions of knowledge management Forms of knowledge Knowledge infrastructure Collexis background.

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Presentation on theme: "Presentation by Yuri de Lugt. Presentation structure Definitions of knowledge management Forms of knowledge Knowledge infrastructure Collexis background."— Presentation transcript:

1 Presentation by Yuri de Lugt

2 Presentation structure Definitions of knowledge management Forms of knowledge Knowledge infrastructure Collexis background Collexis application examples Demonstrations (optional)

3 Definitions Knowledge Management

4 Collexis definitions on KM Data “Structured data” (data, stored set of a meaningful combination of characters and symbols) Information “Data with ADDED value for the receiver” Knowledge “Combined information and experience in the minds of people” “Knowing how to act to retrieve optimal added value” Competence Combination of Knowledge, skills and behavior that leads to an essential contribution to achieving the goals of the business

5 Collexis definitions on KM Knowledge management: “Creating an enviornment in wich knowledge will retrieve maximum added value” “Actions and Rules based on a consistent strategy witch enables an organisation and her employees to use the available knowledge as a strategic production factor for optimal performance.”

6 Forms of Knowledge To perform ‘Knowledge Management’ one needs to know what forms of Knowledge are there.

7 ‘Forms’ of Knowledge Explicit Knowledge –Stored, Transferable Knowledge. –Handbooks, Information Systems, Procedures, etc. –Information Tacit (implicit) Knowledge –In peoples minds –Improved by experience –Not stored, hard to store

8 Knowledge transformation Tacit Explicit From To Social Internal External Combine Copy by seeing Imitate Master-student Report Visualize Modelling Merge Recombine & systemize Learn by doing

9 Collexis definitions on KM Thus: Knowledge is not manageable Only the circumstances in which Knowledge can be explored best are manageable Only explicit knowledge can be stored Implicit knowledge can be made accessible ► Collexis facilitates the optimum circumstances to explore and develop knowledge.

10 The Knowledge infrastructure To find out where Collexis can be of use, we must explore the Knowledge infrastructure

11 Knowledge infrastructure Structure & processes IT ManagementCulture Knowledge flow Develop Share Use Evaluate

12 3 Instrument Groups © Human Connection 2000

13 Collexis & Knowledge management Collexis facilitates: Retrieving information (portals, search and retrieval) Merging information to support Knowledge processes (dynamic categorization, heat maps, content graphs, etc.) Access knowledge by identifying experts (expert finder) Analyzing information to retrieve knowledge (semantic web) Information mining (semantic web, heat maps, content graphs) ►How ?

14 Collexis Functions Content Resources Business Cases Tools Collexis Approach

15 Collexis Functions Business Cases Tools Collexis Approach Documents WebXML Databases content

16 Business Cases Tools Collexis Approach Documents WebXML Databases content Fingerprinting Aggregation Associative Concept Graphs Homonym detection Concept Maps Structured data

17 Collexis Approach Fingerprinting Aggregation Associative Concept Graphs Homonym detection Concept Maps Structured data Vocabulary search, textual search Who is Who Semantic Networks Specialized Search Dynamic Portals Gap analysis Competitor Analysis applications information mining information matching / searching Documents WebXML Databases content

18 Collexis Approach Fingerprinting Aggregation Associative Concept Graphs Homonym detection Concept Maps Structured data Vocabulary search, textual search Who is Who Semantic Networks Specialized Search Dynamic Portals Gap analysis Competitor Analysis applications information mining information matching / searching level 1: level 2: level 3: levels of information management Documents WebXML Databases content

19 Collexis Approach Fingerprinting Aggregation Associative Concept Graphs Homonym detection Concept Maps Structured data Vocabulary search, textual search Who is Who Semantic Networks Specialized Search Dynamic Portals Gap analysis Competitor Analysis applications information mining information matching / searching level 1: use of normalization, language independency, synonyms, matching, thesaurus / vocabulary level 2: use of explicit links, thesaurus hierarchy, contextual information level 3: use of cooccurrence, concept clustering, ontologies levels of information management Documents WebXML Databases content

20 What are we going to show? Documents WebXML Databases content Fingerprinting Aggregation Associative Concept Graphs Homonym detection Concept Maps Structured data Vocabulary search, textual search Who is Who Semantic Networks Specialized Search Dynamic Portals Gap analysis Competitor Analysis applications information mining information matching / searching level 1: use of normalization, language independency, synonyms, matching, thesaurus / vocabulary level 2: use of explicit links, thesaurus hierarchy, contextual information level 3: use of cooccurrence, concept clustering, ontologies levels of information management

21 Collexis Why it was created and how it works

22 One Million Hit Syndrome Billions of gigabytes of information is available Is this bad news or good news? How to manage? –Human indexing is too expensive –Automatic indexing is usually not advanced enough –Keyword search provides too little or too much

23 Information Abundance Increasing amount of digital information in organizations Stored in different forms and different formats Spread over a variety of databases and archives Internet adds staggering volumes of information

24 The Human Touch Store Explicit Knowledge in (relational) thesauri Use free texts and content relations Explore and use linguistic techniques Searching for documents or knowledge? –Knowledge is embedded in people –Collexis behaves like a (human) expert –Collexis finds information, experts and organizations –Collexis supports knowledge exploration

25 The power of Fingerprints Collexis is based on the principle of Fingerprinting Fingerprint: a profile of a piece of information A Fingerprint contains a list of weighted concepts Concepts are derived from a Thesaurus Fingerprint characteristics: unique and small 100% Malaria 35% Agencies 30% Enthusiastic 28% Collaboration 27% Funding 27% Africa 25% Science 15% Dedications 15% Applaud 15% agenda 14% Inaccurate 14% advocacy 13% hope 13% research funding 13% Fund Raising

26 What is a thesaurus? A thesaurus is a specialized vocabulary (“repository of knowledge”) of a particular domain, such as medicine, energy or ICT. It contains selected words, terms and concepts with their semantic relations in a hierarchical structure and can also contain synonyms What is a thesaurus? Aircraft Airplane Means of transport Means of transport Train Automobile Car Truck Lorry Motor Vehicle Plane Simplified Thesaurus example

27 Collexis ®, the concept Word-based Searching What? Why? How? Who? indexing  Concept matching 

28 The magic of Fingerprinting content fingerprints Jobs CV’s, Skills Articles books Emails Word RFP’s people fingerprints average organization fingerprints average

29 Concept Fingerprints Free text Fingerprints Query text or document Multi concept & free text + Σ MATCH Result list

30 Collexis  characteristics Accurate and sensitive Collexis Fingerprints are highly sensitive and accurate, and can be manipulated to optimize search results –Precision: only relevant documents shown –Recall: all relevant documents shown, even when narrowing the search Performance even in millions of documents, search results should be provided instantly Human approach not only documents, but also experts and organizations can be the result of a search

31 Collexis  characteristics Open architecture Easy integration by the use of an API Omnivore Collexis processes structured and unstructured information; this is possible in one action Adaptable Collexis respects existing databases and does not require large hardware investments Fast in any language & language independent Collexis works across languages.

32 Some Collexis  markets and applications Publishing (portals) Scientific organizations (Referee finder) Biotechnology (Genes and protein identification) Pharmaceutical & Chemical (Research and development) Library (Research) Healthcare (Intranet) Legislation & Jurisdiction (Legal Intelligence) Universities (Research and Portals) International Authorities (Information Mining)

33 Collexis Application Examples

34 Search and Retrieval

35 Indexing

36 Search results

37 Experts retrieved

38 Refine a search

39 Use the thesaurus

40

41 Information sharing Add2Collexis

42 Find information and experts; Search assistance through so called “proposed concepts” Available for domains Life Sciences, ICT, food and agriculture (other knowledge domains on request); Supported formats: MS-Word, RTF,.txt, HTML and.pdf; Fully web enabled; Application based on Microsoft®.Net technology.

43 Add2Collexis

44 Search using thesaurus intelligence

45 e-Vamp Automatically enriches existing web pages with hyperlinks; Use thesaurus for concept recognition ; Allows the user to apply a Collexis match for related documents, experts or external database information; Outlinking to other search engines; Configured for different domains

46 Original document

47 e-Vamped document

48 Search using e-Vamp

49 ClipFinder small client application easy to use complete texts as query input makes use of clipboard fast

50 Meta-Analysis

51 Knowledge maps

52 Networks for Meta-Analysis

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57 Demonstrations (optional)

58 Thank you!

59 Choice of word and measured phrase above the reach of ordinary men. William Wordsworth Choice of word and measured phrase above the reach of ordinary men. (William Wordsworth)


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