Interlinking Personal Semantic Data on the Semantic Desktop and the Web of Data
Upcoming SlideShare
Loading in...5
×
 

Interlinking Personal Semantic Data on the Semantic Desktop and the Web of Data

on

  • 773 views

My viva presentation, 23 Nov 2012.

My viva presentation, 23 Nov 2012.
The slides without numbers were hidden during the talk.

Statistics

Views

Total Views
773
Views on SlideShare
773
Embed Views
0

Actions

Likes
1
Downloads
18
Comments
1

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as OpenOffice

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
  • My favorites are 39-42 where we see HCI/visualisation: 3 successive versions of SemNotes.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment
  • And then of course the 2 directions can and should and are combined
  • But the semantic desktop, as efficient as it might become with semantic tools and interconnected data, is no longer the only repository or even the main one some would say of personal data.

Interlinking Personal Semantic Data on the Semantic Desktop and the Web of Data Interlinking Personal Semantic Data on the Semantic Desktop and the Web of Data Presentation Transcript

  • Digital Enterprise Research Institute deri.ie Interlinking Personal Semantic Data on the Desktop and the Web Laura Drǎgan
  • OutlineDigital Enterprise Research Institute www.deri.ie // Introduction  Background and motivation  Research questions // Directions and results  Within the Semantic desktop  To the Web of Data  A use case to rule them all // Conclusion  Research answers  Future work 1
  • OutlineDigital Enterprise Research Institute www.deri.ie // Introduction  Background and motivation  Research questions // Directions and results  Within the Semantic desktop  To the Web of Data  A use case to rule them all // Conclusion  Research answers  Future work 1
  • OutlineDigital Enterprise Research Institute www.deri.ie // Introduction  Background and motivation  Research questions // Directions and results  Within the Semantic desktop  To the Web of Data  A use case to rule them all // Conclusion  Research answers  Future work 1
  • BackgroundDigital Enterprise Research Institute www.deri.ie Personal Information Management 2
  • BackgroundDigital Enterprise Research Institute www.deri.ie Personal Information Management 1962 1968 1945 1965 2
  • BackgroundDigital Enterprise Research Institute www.deri.ie Personal Information Management Web 2
  • BackgroundDigital Enterprise Research Institute www.deri.ie Personal Information Management Semantic Web 2
  • BackgroundDigital Enterprise Research Institute www.deri.ie Personal Information Management Semantic Web Semantic Desktop 2
  • BackgroundDigital Enterprise Research Institute www.deri.ie Personal Information Management Semantic Web Semantic Desktop 2
  • MotivationDigital Enterprise Research Institute www.deri.ie Use the framework provided by the Semantic Desktop to build useful applications and services 3
  • Research questionsDigital Enterprise Research Institute www.deri.ie Q1. How to build semantic applications and tools for the Semantic Desktop to provide the best experience for the users, while creating reusable semantic data? 4
  • Research questionsDigital Enterprise Research Institute www.deri.ie Q1. How to build semantic applications and tools for the Semantic Desktop to provide the best experience for the users, while creating reusable semantic data? 4
  • Research questionsDigital Enterprise Research Institute www.deri.ie Q1. How to buildsemantic applications and tools for the Semantic Desktop? 4
  • Research questionsDigital Enterprise Research Institute www.deri.ie Q1. How to buildsemantic applications and tools for the Semantic Desktop? Q2. How to expand the scope of the Semantic Desktop into the realm of the Web of Data, to benefit the users and enhance their experience? 4
  • Research questionsDigital Enterprise Research Institute www.deri.ie Q1. How to buildsemantic applications and tools for the Semantic Desktop? Q2. How to expand the scope of the Semantic Desktop into the realm of the Web of Data, to benefit the users and enhance their experience? 4
  • Research questionsDigital Enterprise Research Institute www.deri.ie Q1. How to buildsemantic applications and tools for the Semantic Desktop? Q2. How to expand the scope of the Semantic Desktop into the Web of Data? 4
  • Q1 sub-questionsDigital Enterprise Research Institute www.deri.ie semantic applications for the Semantic Desktop Q1.1. How to create semantic data that is complete, correct, safe, and provides a high degree of interlinking with the already existing network of semantic data on the desktop? Q1.2. How to reuse existing Semantic Desktop data in an application? Q1.3. How to design the human-computer interaction in an application for the Semantic Desktop? Q1.4. How to correctly evaluate a semantic application? 5
  • Q1 sub-questionsDigital Enterprise Research Institute www.deri.ie semantic applications for the Semantic Desktop Q1.1. How to create semantic data that is complete, correct, safe, and provides a high degree of interlinking with the already existing network of semantic data on the desktop? Q1.2. How to reuse existing Semantic Desktop data in an application? Q1.3. How to design the human-computer interaction in an application for the Semantic Desktop? Q1.4. How to correctly evaluate a semantic application? 5
  • Q2 sub-questionsDigital Enterprise Research Institute www.deri.ie connect the Semantic Desktop with the Web of Data Q2.1. How to find Web instances representing the same real- world thing described by a Semantic Desktop resource? Q2.2. How to use the Web information which is related to a desktop resource? Q2.3. How to make desktop data available online safely? 6
  • Q2 sub-questionsDigital Enterprise Research Institute www.deri.ie connect the Semantic Desktop with the Web of Data Q2.1. How to find Web instances representing the same real-world thing described by a Semantic Desktop resource? Q2.2. How to use the Web information which is related to a desktop resource? Q2.3. How to make desktop data available online safely? 6
  • DirectionsDigital Enterprise Research Institute www.deri.ie 7
  • DirectionsDigital Enterprise Research Institute www.deri.ie 1. 7
  • DirectionsDigital Enterprise Research Institute www.deri.ie 1. 7
  • DirectionsDigital Enterprise Research Institute www.deri.ie 7
  • DirectionsDigital Enterprise Research Institute www.deri.ie 2. 7
  • DirectionsDigital Enterprise Research Institute www.deri.ie 2. 1. 7
  • Within the Semantic DesktopDigital Enterprise Research Institute www.deri.ie 8
  • SemNotesDigital Enterprise Research Institute www.deri.ie Challenges described by Q1  create new semantic data – Data representation – Data management  reuse existing Semantic Desktop data – Interlinking  design the human-computer interaction – Visualisation  correctly evaluate a semantic application – Task-based comparison to Evernote 9
  • Data representationDigital Enterprise Research Institute www.deri.ie <nepomuk:/a_note> 10
  • Data representationDigital Enterprise Research Institute www.deri.ie <nepomuk:/a_note> a pimo:Note ; 10
  • Data representationDigital Enterprise Research Institute www.deri.ie <nepomuk:/a_note> a pimo:Note ; nao:prefLabel "holiday plans" ; 10
  • Data representationDigital Enterprise Research Institute www.deri.ie <nepomuk:/a_note> a pimo:Note ; nao:prefLabel “holiday plans” ; nao:created “2010-09-16T21:08:54.29Z”ˆˆxsd:dateTime ; nao:lastModified “2010-09-17T10:59:01.58Z”ˆˆxsd:dateTime ; nao:numericRating “9”ˆˆxsd:int ; 10
  • Data representationDigital Enterprise Research Institute www.deri.ie <nepomuk:/a_note> a pimo:Note ; nao:prefLabel “holiday plans” ; nao:created “2010-09-16T21:08:54.29Z”ˆˆxsd:dateTime ; nao:lastModified “2010-09-17T10:59:01.58Z”ˆˆxsd:dateTime ; nao:numericRating “9”ˆˆxsd:int ; nao:description “<html >... </ html>”ˆˆxsd:string ; 10
  • Data representationDigital Enterprise Research Institute www.deri.ie <nepomuk:/a_note> a pimo:Note ; nao:prefLabel “holiday plans” ; nao:created “2010-09-16T21:08:54.29Z”ˆˆxsd:dateTime ; nao:lastModified “2010-09-17T10:59:01.58Z”ˆˆxsd:dateTime ; nao:numericRating “9”ˆˆxsd:int ; nao:description “<html >... </ html>”ˆˆxsd:string ; nao:hasTag <nepomuk:/res/travel> ; 10
  • Data representationDigital Enterprise Research Institute www.deri.ie <nepomuk:/a_note> a pimo:Note ; nao:prefLabel “holiday plans” ; nao:created “2010-09-16T21:08:54.29Z”ˆˆxsd:dateTime ; nao:lastModified “2010-09-17T10:59:01.58Z”ˆˆxsd:dateTime ; nao:numericRating “9”ˆˆxsd:int ; nao:description “<html >... </ html>”ˆˆxsd:string ; nao:hasTag <nepomuk:/res/travel> ; pimo:isRelated <nepomuk:/res/Rome>, <nepomuk:/res/Jane> . 10
  • InterlinkingDigital Enterprise Research Institute www.deri.ie Annotation suggestions:  Based on the content of the note.  Certain types preferred.  Preference based on past use and matched length. “ ... brian ... “  Brian Davis  Brian Wall “ ... brian davis ... “  Brian Davis 11
  • Interlinking algorithmDigital Enterprise Research Institute www.deri.ie  Algorithm  scan text; identify possible entities  for each possible entity find a list of desktop resource candidates – compute score for each possible candidate – filter list by score – sort by score  present the candidates to the user  create the relation only if the user chooses a resource
  • Visualisation - HCIDigital Enterprise Research Institute www.deri.ie 12
  • Visualisation - versionsDigital Enterprise Research Institute www.deri.ie 13
  • Visualisation - versionsDigital Enterprise Research Institute www.deri.ie 13
  • Visualisation - HCIDigital Enterprise Research Institute www.deri.ie 13
  • EvaluationDigital Enterprise Research Institute www.deri.ie The effort of interlinking lower than the effort spent when searching.  Task-based experiment  Comparation of SemNotes to Evernote 14
  • EvaluationDigital Enterprise Research Institute www.deri.ie Experimental setup  20 participants – 14 use note-taking regularly – 5 use Evernote in their daily activity  Familiar data – 130 contacts – 20 scientific papers – 50 notes  8 tasks – 2 tasks - familiarise the participants with the dataset – 6 tasks focused on note-taking, varying the complexity  Measurements – Time spent – Mouse clicks – Keystrokes 15
  • EvaluationDigital Enterprise Research Institute www.deri.ie Tasks T1. Find notes tagged with “todo” T2. Find to-dos that are related to DERI T3. Find a to-do related to a presentation given by John T4. Take a note about planning a social event for your group T5. Find a note containing minutes from the last meeting about the NICE project. Change the date of the next meeting planned T6. Take a note for the action item assigned to you at the last meeting
  • EvaluationDigital Enterprise Research Institute www.deri.ie Quantitative results  Time spent note-taking – no significant differences  Time spent searching – SemNotes significantly faster for complex queries – no significant difference for simple queries 16
  • EvaluationDigital Enterprise Research Institute www.deri.ie Quantitative results  Time spent note-taking – no significant differences  Time spent searching – SemNotes significantly faster for complex queries – no significant difference for simple queries Questionnaire results Faster Better 16
  • EvaluationDigital Enterprise Research Institute www.deri.ie Quantitative results Time Clicks Task Avg Med t Avg Med t T1 0.5 0 0.152 0.167 0 0.692 T2 -8 -8 -2.94 -0.333 -1 -0.48 T3 -0.125 1 -0.046 0.857 1 1.426 T4 0.063 0.016 0.486 6.067 8 2.026 T5 14.357 13 1.713 4.812 2 1.527 T6 0.249 0.243 1.004 20.8 12 3.08
  • But ...Digital Enterprise Research Institute www.deri.ie The desktop is not any more the sole repository of personal information  Social networks  Mobile devices  Cloud services 17
  • To the Web of DataDigital Enterprise Research Institute www.deri.ie Challenges described by Q2 (Q2.1.)  find Web aliases of Semantic Desktop resources 18
  • Finding Web AliasesDigital Enterprise Research Institute www.deri.ie Web alias = Web resource representing the same real-world entity as the desktop resource 19
  • Finding Web AliasesDigital Enterprise Research Institute www.deri.ie Different identifiers 19
  • Finding Web AliasesDigital Enterprise Research Institute www.deri.ie Different identifiers nepomuk:/res/Angela http://angelaonthe.net/foaf/me 19
  • Finding Web AliasesDigital Enterprise Research Institute www.deri.ie Different vocabularies 19
  • Finding Web AliasesDigital Enterprise Research Institute www.deri.ie The sheer size of the Web of Data 19
  • Finding Web AliasesDigital Enterprise Research Institute www.deri.ie The sheer size of the Web of Data 19
  • 2 Step approachDigital Enterprise Research Institute www.deri.ie 1. Candidate Selection 20
  • 2 Step approachDigital Enterprise Research Institute www.deri.ie 1. Candidate Selection  Query various Web of Data sources  Identify candidate URIs  Retrieve data for each of the candidate 20
  • 2 Step approachDigital Enterprise Research Institute www.deri.ie 1. Candidate Selection  Query various Web of Data sources  Identify candidate URIs  Retrieve data for each of the candidate 2. Candidate Filtering 20
  • 2 Step approachDigital Enterprise Research Institute www.deri.ie 1. Candidate Selection  Query various Web of Data sources  Identify candidate URIs  Retrieve data for each of the candidate 2. Candidate Filtering  Compute similarity score.  Filter the candidates. 20
  • Candidate SelectionDigital Enterprise Research Institute www.deri.ie Determined set of sources  Specific requirements  Restricted domain Semantic search engine  Generic domain  Unknown data sources 21
  • Candidate SelectionDigital Enterprise Research Institute www.deri.ie Determined set of sources  Specific requirements  Restricted domain Semantic search engine  Generic domain  Unknown data sources 21
  • Candidate FilteringDigital Enterprise Research Institute www.deri.ie (local, web) 1. Filter by type 2. Compute similarity score 3. Filter by score return score 22
  • Matching ModuleDigital Enterprise Research Institute www.deri.ie (local, web) Type No return 0 matching Yes Compute score score ≥ threshold No Yes return score
  • Matching ParametersDigital Enterprise Research Institute www.deri.ie String matching (SM)  Exact matching versus approximate string matching  Koeln vs. Köln Weighted properties (WP)  Weighted participation of properties in the final score  Email address more exact than name Multi-valued properties (MVP)  All matching values for a property contribute to the score  e.g. Authors names for a paper
  • Score CalculationDigital Enterprise Research Institute www.deri.ie Driven by the local data •weighted sum of matching props •score = •total sum of all weighted props
  • EvaluationDigital Enterprise Research Institute www.deri.ie Manually constructed gold standard  Data collection  Relevance judgements IR measures  Effect of parameter settings  Adjust thresholds 23
  • Data collectionDigital Enterprise Research Institute www.deri.ie Desktop data  50 people – nco:PersonContact  50 music albums – nmo:MusicAlbum  50 publications – nfo:PaginatedTextDocument  11.917 triples Web data  20 candidates for each desktop resource -> 3000 URIs  1.530.686 triples 24
  • Relevance JudgementsDigital Enterprise Research Institute www.deri.ie 25
  • Relevance JudgementsDigital Enterprise Research Institute www.deri.ie 3000 pairs x 3 experts Fleiss K = 0.638  ± 0.214 Average pairwise agreement 92.252% 25
  • IR MeasuresDigital Enterprise Research Institute www.deri.ie  MAP  NDCG  P@k (k=1,2,3,4,5) Baseline:  exact match  all properties count equally  single value considered for each property
  • Evaluation ResultsDigital Enterprise Research Institute www.deri.ie Approximate string matching  improves results for albums and people  does not help for publications Weights and multiple values  when combined improve results for publications, but not for the other types 26
  • Merging the two directionsDigital Enterprise Research Institute www.deri.ie 2. 1. 27
  • A use caseDigital Enterprise Research Institute www.deri.ie Note Blog post [Semantic] note-taking [Semantic] blogging [Preserve context] [Preserve privacy] 28
  • StepsDigital Enterprise Research Institute www.deri.ie Transformation  On the local side  Extension to SemNotes Publication  On the server side  According to Linked Data principles 29
  • StepsDigital Enterprise Research Institute www.deri.ie (Note-taking & annotation) (Entity matching) Transformation  On the local side  Extension to SemNotes Publication  On the server side  According to Linked Data principles 29
  • Levels and layersDigital Enterprise Research Institute www.deri.ie 30
  • Ontology levelDigital Enterprise Research Institute www.deri.ie  Local - Nepomuk ontologies  Remote – SIOC, FOAF, DC, ... pimo:Note sioc:Post nao:prefLabel rdfs:label nao:Tag sioct:Tag nao:created dcterms:created pimo:Person foaf:Person nao:lastModified dcterms:modified pimo:Project doap:Project nao:hasTag sioc:topic pimo:Event ical:Vevent pimo:isRelated sioc:related_to
  • Data levelDigital Enterprise Research Institute www.deri.ie  Local – notes, desktop resources (tags included)  Remote – blog posts, Web resources, tags http://semnotes.deri.ie/notes/note/id http://semnotes.deri.ie/notes/resource/id http://semnotes.deri.ie/notes/tag/label
  • Application level - localDigital Enterprise Research Institute www.deri.ie  Plugin for SemNotes  Ask server for server URLs for the new note and resources  Replace desktop URIs with the server URLs in the note  Add RDFa to the note  Push the transformed note to the server
  • Application level - remoteDigital Enterprise Research Institute www.deri.ie  Web server with MySQL, PHP, ARC2  Create new URLs for resources  Receive and process the note  Publish the data online
  • Published dataDigital Enterprise Research Institute www.deri.ie 31
  • Research answersDigital Enterprise Research Institute www.deri.ie Q1. How to buildsemantic applications and tools for the Semantic Desktop? Q2. How to expand the scope of the Semantic Desktop into the Web of Data? 32
  • Research answersDigital Enterprise Research Institute www.deri.ie Q1. How to buildsemantic applications and tools for the Semantic Desktop?  SemNotes – Create new data – Reuse existing data – HCI – Evaluation Q2. How to expand the scope of the Semantic Desktop into the Web of Data? 32
  • Research answersDigital Enterprise Research Institute www.deri.ie Q1. How to buildsemantic applications and tools for the Semantic Desktop?  SemNotes – Create new data – Reuse existing data – HCI – Evaluation Q2. How to expand the scope of the Semantic Desktop into the Web of Data?  Web aliases  Semantic blogging use case 32
  • Future workDigital Enterprise Research Institute www.deri.ie Information Extraction algorithms and methods  create multiple types of relations based on the text  extract new entities from text  extract links between entities mentioned in the notes Explore visualisations  personal data browser Large scale user study of semantic personal information usage and behaviours 33
  • SummaryDigital Enterprise Research Institute www.deri.ie Web aliases 2. 1. + semantic publishing use case