Integrating digital traces into a semantic enriched data
1. INTEGRATING DIGITAL
TRACES INTO A SEMANTIC-
ENRICHED DATA CLOUD
FOR INFORMAL LEARNING
Vania Dimitrova, Dhaval Thakker, Lydia Lau
2. Outline
Motivation and bigger picture
Aggregating Digital traces into Semantic-
enriched data cloud
Semantic Data Browser
Exploratory Evaluation
Conclusions
3. Motivation
Modern learning models require linking
experience in training environments with
experience in the real-world.
Real-world experiences are hard to collect
Social media brings new opportunities to
tackle this challenge, supplying digital traces
Exploiting social content as a source for
experiential learning is being investigated in
Immersive Reflective Experience based
Adaptive Learning (ImREAL)
http://imreal-project.eu/
4. Digital Traces
broad, authentic, gradually increasing and
up-to-date digital examples
5. Ill-Defined domains
Hard to specify and often require multiple
interpretations and viewpoints.
Soft skills –
communicating, planning, managing, advising,
negotiating.
Highly demanded
Modern informal learning environments for
soft skills can exploit digital traces to provide
learning situations linked to real world
experience by peers (other learners) or tutors.
6. Role of Semantic Web
Technologies
To realise this vision, novel architectures are
needed which use: robust and cost-effective
ways to
retrieve, create, aggregate, organise, and
exploit Digital Traces in learning situations; in
other words, to tame Digital Traces for
informal learning.
By combining major advancements in
semantic web : semantic
augmentation, semantic
query, relatedness, similarity, summarisatio
8. Processing Pipeline – DTs
collection
•Availability of Social Web APIs
•Noise filtration mechanisms*
•Role of tutors/trainers in setting gold standard**
Digital
Traces
Collection Semantic
Browsing &
Augmentation
& Query Interaction
Bespoke
Ontologies Ontology
& Linked Underpinning
Data Cloud
* Ammari, A., Lau, L. Dimitrova, V. Deriving Group Profiles from social media, LAK 2012
** Redecker, C. et al. Learning 2.0- the impact of social media on learning in Europe, Policy Brief, European
Commission, JRC, 2010
10. Processing Pipeline - Semantics
Stage 1: Activity Modelling on Interpersonal Communication
Activity Analysis
Use Case
Social Web Theory Activity other relevant
on a Use Model ontologies
Case WN-
Body Affect
Stage 2: Activity Modelling Enrichment using Semantics Language
Social Multi-layered
Web Activity Modelling Ontology
(AMOn) for
Interpersonal Communications Logical Encoding
Stage 3: Providing Access to Real World Experiences
Semantic
Services:
Augmentation,Query
Story Boarding
11. Semantic Augmentation Service
Purpose :
Generic service designed to link is almost always best. An authority handshake should be
reserved for when you wish to show you are in charge.
content with the concepts from the
ontological knowledge bases in order
to fully benefit from the reasoning
capabilities of semantic technologies.
Components: Simulators
•Information Extraction: Finding
mentions of entities in text
•Semantic Linking: between entity
mentions and ontologies, linked data
•Semantic Repository: forward
chaining repository for semantic Information Semantic
expansion Extraction Linking
•Ontologies: AMOn & External
ontologies
Implementation: Semantic Ontology
•RESTful interface for easy Repository
integration AMOn
•Contribution to the semantic
augmentation in the IPC domain
12. Semantic Query Service
Purpose :
Generic service for querying and
browsing using semantically augmented
content. In I-CAW, it allows searching of
socially and locally authored data for real-
world activities from the domain of Related Content
interest Matching Content
Components: Term(s), Browsing
Simulators
Concept(s) Tag Cloud
•Concept Filtering: Identify matching
concepts and relevant information
•Content Filtering: Identify matching
contents and relevant information
•Concept Frequency: CF/IDF analysis
Concept Content
•Semantic Relatedness: Content &
concept relatedness Filtering Filtering
Implementation:
•RESTful interface for easy Concept Semantic
integration, integrated with Frequency Relatedness
Storyboard
•Contribution to the semantic browsing
16. Exploratory Study
Domain : Job Interview
Digital Traces: User comments from YouTube -
cleaned from filtration, stories from blog-like
environment by ImREAL volunteers
Participants
Group 2: Applicants
Group 1: Interviewers
Participant ID P2 P3 P4 P5 P10 P1 P6 P7 P8 P9
No. of
Interviews
as an
interviewer 10-15 10-15 10-15 >15 >15 0 0 0 0 1-5
No. of
interviews
as an applicant 10-15 10-15 10-15 >15 5-10 1-5 1-5 1-5 5-10 5-10
17. Exploratory Study: Good things about
DTs
Participants particularly liked the authenticity
of the content:
“Examples are the beauty of system – I will learn from examples [p10]”
“Anything that facilitates the preparation of training material and provides real
world examples to backup training is very helpful [p5]”
Which probed them to:
Further reflect on their experiences, and in some
cases help articulate what they had been doing
intuitively
Provide their viewpoints (due to culture,
environment, tacit knowledge) – acted as stimuli
Sense the diversity or consensus on the selected
topic
18. Exploratory Study: Issues with
DTs
Issues requiring attention:
Two most experienced interviewers(p5 and p10)
commented that some content could be mistaken as
the norm.
For instance, a comment associated with a video
stated “The interviewer has his hands in front of
him, which indicates that he is concentrating and not
fidgeting...”. P5 and P10 stressed that inexperienced
users may see a comment in isolation and believe it
would be valid in all situations
It was suggested that short comments could be
augmented with contextual information to assist
the assessment of the credibility of the different
19. Conclusions
Social spaces bring new opportunities , i.e. as
a source of diverse range of real-world
experiences.
signs are encouraging – digital traces as a
Initial
source of authentic examples and stimuli
Further work is needed to capitalise on new
opportunities brought by social content
Semantics technologies provide apparatus for
taming digital traces
Further work is needed to turn semantic browsing
into informal learning.
