Increasing information fluence in knowledge work

Transcription

1 Increasing information fluence in knowledge work Steffen Staab Institute for Web Science & Technologies - WeST

2 Motivation I have bought myself a 30 screen, because half of my work is re-typing existing material. Many other knowledge workers suffer likewise. Logistics, maintenance staff, (software) engineers, Even more so if working in some semi-defined process

3 Information fluence Information fluence the amount of information that can be effectively acted upon in an amount of time Constrained by: Decontextualization joining many information sources Contextualization presenting information in its context

4 Agenda Increase information fluence Decontextualize: Manage heterogeneous information sources WordNet Swoogle GeoNames Contextualize: Tracking information context

5 Managing Heterogeneous Multimedia Information GeoNames Links Location Persons xxxxx xxxx low- to midlevel features Knowledge Tags

6 SemaPlorer

7 Data Model

8 Billion triples challenge: Use Linked Open Data Common approach: Import dump to new data silo flexible extensible WordNet Swoogle GeoNames inflexible not scaleable RDFS rules geo... monolithic RDFS WordNet birthplace PlaceOfBirth birthplace Rules + + fulltext Swoogle + scaleable Geo querying + GeoNames>1Gt Since mid Nov 08: Flickr API

9 Architecture of Federated Infrastructure Endpoints: Autonomous RDF repositories Connected via SPARQL Dynamic (added by reconfiguring SourceFinder) 25 repositories, > 400GB including fulltext indices Inferencing and Querying: Can vary from repository to repository Here: SPARQL based Views RDFS transitivity rules for SKOS Geo range queries in SPARQL fulltext search in SPARQL via Lucene

10 Agenda Increase information fluence Decontextualize: Manage heterogeneous information sources Contextualize: Tracking information context Do you have any recent paper about semantic desktop developments? K-Cap B I attached a paper about a new ontology and integration approach... x-cosim.pdf A 1945: Memex, the mind machine. C, this paper might be interesting for you x-cosim.pdf C

11 Information to act upon?

12 The Computer Desktop: Information to act upon?

13 Personal Information Management (PIM) Scenario Do you have any recent paper about semantic desktop developments? K-Cap B I attached a paper about a new ontology and integration approach... A C, this paper might be interesting for you C x-cosim.pdf x-cosim.pdf

14 Contextualized Information B x-cosim.pdf K-Cap I attached a paper about a new ontology and integration approach... Storage context is lost!

15 Classifying Information A K-Cap Classification system is lost!

16 Communicating Decontextualization is necessary for use of information; re-contextualization of information is not supported! B A VoIP Communication context is lost!

17 Contextualizing information on the semantic desktop Do you have any recent paper about semantic desktop developments? body C, this paper might be interesting for you recipient sender A sender body recipient C B sender body I attached a paper about a new ontology and integration approach... recipient attachment x-cosim.pdf realization attachment annotation annotation K-Cap annotation

18 Knowledge about the linked information Ontologies describe formally What the different relations mean What the different subject, objects are Message Agent Instant Message Person MUA

19 Implicit Models and Data Formats Semantics of data hidden in applications` source code Data formats do not define the semantics of what is expressed by them????

20 Narrow Conceptualizations Attachment == Page? Bookmark == Folder? == File? Folder User File Page Attachment Form Bookmark Access Sender Recipient Dir Image

21 X-COSIMO: X-COSIM Ontology High quality reference model that describes information dealt with across desktop applications Uses RDF as flexible data structure that enables information linkage Formulated in OWL

22 Methodology: Step 1 Make Information Explicit X-COSIM Narrow Conceptualizations Missing Semantics Legacy Desktops Semantic Data Model + Foundational Ontology Legend Challenge Building Block

23 Aspects Supported by X-COSIM Modules Filing, Downloading, Modifying, Converting Descriptions and Situations Descriptions and Situations Sharing, Arguing, Explaining Re-introduce information context! Descriptions and Situations Structuring, Summarizing, User-defined Ontologies Descriptions and Situations Creating, Reusing, Copy & Paste

24 Methodology: Step 2 Reintroduce Context X-COSIM Contextualized Information Representation of Context Narrow Conceptualizations Missing Semantics Legacy Desktops Semantic Data Model + Foundational Ontology Legend Challenge Building Block

25 DOLCE for Developers of Clients? X-COSIM Architecture Map between contextualized representations and the reference model Apps, Domain Models Data Access, Mapping Reference Model, Storage File Manager, Browser, Mail Client, Task Manager, Text Processor,... Dublin Core, Calendar, Foaf,... RDF API X-COSIMA X-COSIMO RDF Store SPARQL Particular Perdurant Abstract Quality Endurant Description Situation

26 The X-COSIM API (X-COSIMA) // Create a conversation Conversation conversation = new Conversation(); // bikeimage transferred as attachment between thomas and // Carsten within the created conversation = new (conversation, persona, addressofa, personb, addressofb); .setbodytext("hi B, I attached a paper about ");

27 Methodology: Step 3 Make Useable X-COSIM Intuitive, context aware development interface Conceptual Architecture Contextualized Information Representation of Context Narrow Conceptualizations Missing Semantics Legacy Desktops Semantic Data Model + Foundational Ontology Legend Challenge Building Block

28 Resulting Meta Data in Context Communication Aspect B A Realization Aspects Classification Aspect K-Cap

29 Evaluation: Are Semantic Desktops Better?

30 A Summative Evaluation Evaluation question: Does a Semantic Desktop provide better support for PIM tasks than a conventional one? Better means Increased effectiveness Increased efficiency Increased satisfaction * at a 95% confidence degree

31 Between-subject Test Two desktop systems (S1, S2) Semantic Desktop (KDE Desktop including COSIMail + COSIFile) (S1) Conventional KDE Desktop (Thunderbird, Dolphin) (S2) Two user groups working on either one of the systems 18 persons 3 grad. students in CS, 15 PhD students None of them ever used a Semantic Desktop before 10 Tasks (same for G1, G2) Group G1 Group G2

32 Tools of the Evaluation: COSIMail Semantic Meta Data Access to further PIM tools

33 Tools of the Evaluation: COSIFile Additional Semantic Meta Data, e.g. sender, subject Access to further PIM tools

34 Scenario and Data Corpus Real data selected from the organizers of the Night of Computer Science in Koblenz > 140 s 66 Files (40 came by ) Tasks of the organizers: Creation of leaflets, flyers Assignment of print shops, design offices Planning of guided tours Scheduling of the event program

35 Task Name Rationale T1 Organization Familiarization T2, T3 Lookup Baseline T4 - T6 Multi Item Evaluation T7 T9 DocumentDriven Collaboration Evaluation T10 Evaluation Information Collation Increasing Complexity Task Set

36 Results: Effectiveness Measured by means of Evaluation wizard Measurements: Number of completed tasks Amount of correct solutions Results: None of all 18 test persons indicated to be unable to solve any of the tasks 3 wrong answers, 2 by users of the Semantic Desktop

37 Results: Efficiency Measured by means of Screen recordings Mouse observation Evaluation wizard Measurements: Task execution time Mouse movements Mouse clicks Window switches

38 Results: Execution Times per Group (G1,G2) Conventional Desktop Semantic Desktop

39 Satisfaction Measured by answers to questionnaire remarks given in feedback interview Design of the questionnaire and rating scale based on IsoMetrics S Questionnaire contains positive and negative statements Rating Scale: 1 = strongly disagree 2 = disagree 3 = so-so 4 = agree 5 = strongly agree

40 Subjective Feedback Among Groups S9: Working on the tasks was tedious/cumbersome. S10: The tasks correspond to task types that I also need to do for my work. S11: Tasks were significantly harder to do, since I was required to work with unknown data. Conventional Semantic Desktop Difference T-Test Avg Median Avg Median Avg Median S S S

41 Are Semantic Desktops better? Nominated for Best Paper at ACM K-Cap 2009

42 Conclusion: Increase information fluence Decontextualize: Manage heterogeneous information sources Decontextualization by flexible, scaleable, lightweight federator Further challenges: Ranking of ontologies and data Networked ontologies and data Contextualize: Tracking information context Information context benefits the knowledge worker Potential for many further tasks beyond and File management Further challenges: Tracking information provenance Events (e.g. in emergency response)

43 Short bibliography Decontextualization Simon Schenk; Carsten Saathoff; Steffen Staab; Ansgar Scherp. SemaPlorer---Interactive Semantic Exploration of Data and Media based on a Federated Cloud Infrastructure. In Journal of Web Semantics,Elsevier, 7(4), S. Schenk, S. Staab. Networked Graphs: A Declarative Mechanism for SPARQL Rules, SPARQL Views and RDF Data Integration on the Web. In: Proc. of WWW-2008, 17th Int. World Wide Web Conference, Bejing, China, April 21-25, 2008, pp Ranking T. Franz, A. Schultz, S. Sizov, S. Staab. TripleRank: Ranking Semantic Web Data by Tensor Decomposition. In: Proc. of ISWC-2009 International Semantic Web Conference, Westfield, USA, Oct 25-29, Contextualization T. Franz, A. Scherp, S. Staab. Are Semantic Desktops Better? Summative Evaluation Comparing a Semantic against a Conventional Desktop. In: Proc. of K-Cap Fifth Int. Conference on Knowledge Capture. ACM Press. Sep 1-4, 2009, Redondo Beach, CA, USA. Provenance R. Dividino, S. Sizov, S. Staab, B. Schüler. Managing RDF with Meta Knowledge Awareness. In: Journal of Web Semantics. Special issue on "The Web of Data". Elsevier, 7(3), R. Dividino, S. Schenk, S. Sizov, S. Staab. Provenance, Trust, Explanations - and all that other Meta Knowledge. Kuenstliche Intelligenz. 23(2), Events A. Scherp, T. Franz, C. Saathoff, S. Staab. F A Model of Events based on the Foundational Ontology DOLCE+ Ultra Light. In: Proc. of K-Cap Fifth Int. Conference on Knowledge Capture. ACM Press. Sep 1-4, 2009, Redondo Beach, CA, USA.

44 Thank you for your attention!