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

Digital Enterprise Research Institute deri.ie

Interlinking Personal Semantic Data
on the Desktop and the Web

Laura Drǎgan

Outline
Digital 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

Background

Personal Information Management

2

Background


1962
1968

1945

1965

2

Background

Web

2

Background

Semantic Web

2

Background

Semantic Web

Semantic Desktop

2

Motivation

Use the framework provided by
the Semantic Desktop to build useful
applications and services

3

Research questions

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 questions

Q1. How to buildsemantic applications and tools for the
Semantic Desktop?

4

Research questions

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 questions

Semantic Desktop?

into the Web of Data?

4

Q1 sub-questions

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-questions

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-questions

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-questions

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

Directions

7

Directions

1.

7

Directions

2.

7

Directions

2.
1.

7

Within the Semantic Desktop

8

SemNotes

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 representation

<nepomuk:/a_note>

10

Data representation

<nepomuk:/a_note>
a pimo:Note ;

10

Data representation

<nepomuk:/a_note>
a pimo:Note ;
nao:prefLabel "holiday plans" ;

10

Data representation

<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 representation

<nepomuk:/a_note>
a pimo:Note ;
;
nao:description “<html >... </ html>”ˆˆxsd:string ;

10

Data representation

<nepomuk:/a_note>
a pimo:Note ;
;
nao:hasTag <nepomuk:/res/travel> ;

10

Data representation

<nepomuk:/a_note>
a pimo:Note ;
;
nao:hasTag <nepomuk:/res/travel> ;
pimo:isRelated <nepomuk:/res/Rome>,
<nepomuk:/res/Jane> . 10

Interlinking

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 algorithm

 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 - HCI

12

Visualisation - versions

13

Visualisation - HCI

13

Evaluation

The effort of interlinking lower than the effort spent when searching.

 Task-based experiment
 Comparation of SemNotes to Evernote

14

Evaluation

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

Evaluation

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

Evaluation

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

Evaluation

 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

Evaluation


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 ...

The desktop is not any more the sole repository of
personal information
 Social networks
 Mobile devices
 Cloud services

17

To the Web of Data

Challenges described by Q2 (Q2.1.)
 find Web aliases of Semantic Desktop resources

18

Finding Web Aliases

Web alias
= Web resource representing the same
real-world entity as the desktop resource

19

Finding Web Aliases

Different identifiers

19

Finding Web Aliases

Different identifiers

nepomuk:/res/Angela

http://angelaonthe.net/foaf/me

19

Finding Web Aliases

Different vocabularies

19

Finding Web Aliases

The sheer size of the Web of Data

19

2 Step approach

1. Candidate Selection

20

2 Step approach

 Query various Web of Data sources
 Identify candidate URIs
 Retrieve data for each of the candidate

20

2 Step approach


2. Candidate Filtering

20

2 Step approach


2. Candidate Filtering
 Compute similarity score.
 Filter the candidates.

20

Candidate Selection

Determined set of sources
 Specific requirements
 Restricted domain

Semantic search engine
 Generic domain
 Unknown data sources

21

Candidate Filtering

(local, web)

1. Filter by type
2. Compute similarity score
3. Filter by score

return
score

22

Matching Module

(local, web) Type No
return 0
matching

Yes

Compute score

score ≥
threshold No

Yes return
score

Matching Parameters

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 Calculation

Driven by the local data

•weighted sum of matching props
•score =
•total sum of all weighted props

Evaluation

Manually constructed gold standard
 Data collection
 Relevance judgements

IR measures
 Effect of parameter settings
 Adjust thresholds

23

Data collection

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 Judgements

25

Relevance Judgements

3000 pairs x 3 experts

Fleiss' K = 0.638 ± 0.214
Average pairwise agreement 92.252%

25

IR Measures

 MAP
 NDCG
 P@k (k=1,2,3,4,5)

Baseline:
 exact match
 all properties count equally
 single value considered for each property

Evaluation Results

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 directions

2.

1.

27

A use case

Note Blog post
[Semantic] note-taking [Semantic] blogging

[Preserve context]
[Preserve privacy]

28

Steps

Transformation
 On the local side
 Extension to SemNotes

Publication
 On the server side
 According to Linked Data principles

29

Steps

(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 layers

30

Ontology level

 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 level

 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 - local

 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 - remote

 Web server with MySQL, PHP, ARC2
 Create new URLs for resources
 Receive and process the note
 Publish the data online

Published data

31

Research answers

Semantic Desktop?


32

Research answers

Semantic Desktop?
 SemNotes
– Create new data
– Reuse existing data
– HCI
– Evaluation


32

Research answers

Semantic Desktop?
 SemNotes
– Create new data
– Reuse existing data
– HCI
– Evaluation

 Web aliases
 Semantic blogging use case

32

Future work

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

Summary

Web aliases
2.

1.

+ semantic publishing use case

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

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

En vedette

En vedette (7)

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

Similaire à Interlinking Personal Semantic Data on the Semantic Desktop and the Web of Data (20)

Dernier

Dernier (20)

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

Notes de l'éditeur