Social media, Ontologies and

Web 2.0 eLearning

Paola Monachesi (Utrecht University)

work carried out in collaboration with Kiril Simov, Petya Osenova, Eelco Mossel,

Vlad Posea, Thomas Markus

Overview

• Ontologies for eLearning– Lexicalized ontologies for cross-lingual retrieval of

learning material– Ontologies vs. Tagging/Folksonomies– Integrating ontologies and tagging for knowledge

discovery– Integrating ontologies and social networks for

knowledge discovery

• Evaluations and challenges• Conclusions

Ontologies

• Ontologies are a crucial element of the Semantic Web vision

• Ontologies allow for a formalization of knowledge that:– facilitates automatic processing of the

information;– enables inference to be performed.

Ontologies and eLearningExamples of two possible uses:

• enhance the management, distribution and retrieval of the learning material – LT4eL project (www.lt4el.eu)

• ontologies enriched with social tags can mediate between formal and informal learning– LTfLL project (www.ltfll-project.org)

Ontologies and eLearning

• Users:– Tutors/content providers who want to compile

a course– Learners that want to find material in several

languages – Learners that are looking for content in a

knowledge discovery process and for peers

Demo

Enhancing the retrieval of multilingual learning material:

http://www.lt4el.eu/index.php?content=videos

Components

• A corpus of learning objects in 8 languages

• A domain ontology

• Lexicons for 8 languages

• (Linguistically, semantically) annotated learning objects

LT4eL Domain Ontology: general issues

• The domain: Computing• Coverage: operating systems; programs; document

preparation – creation, formatting, saving, printing; Web, Internet, computer networks; HTML, websites, HTML documents; email

• The role of the ontology: for indexing of the LOs

Connection with other Ontologies

DOLCE (Guarino&a

l.)

OntoWordNet

LT4EL

Current state of the ontology

• about 1002 domain concepts,

• about 105 concepts from DOLCE

• about 169 intermediate concepts from OntoWordNet

• http://www.lt4el.eu/index.php?content=tools#ontology

Ontology-Based Lexicon Model

• The lexicons represent the main interface between the user's query and the ontology

• Lexicons for all languages (8) of the project have been created

Mapping Lexical Varieties

Ontology

LexicalizedTerms

Free Phrases

<entry id="id60"> <owl:Class rdf:about="http://www.lt4el.eu/CSnCS#BarWithButtons"> <rdfs:subClassOf> <owl:Class rdf:about="http://www.lt4el.eu/CSnCS#Window"/> </rdfs:subClassOf> </owl:Class> <def>A horizontal or vertical bar as a part of a window, that contains buttons, icons.</def> <termg lang="nl"> <term shead="1">werkbalk</term> <term>balk</term> <term type="nonlex">balk met knoppen</term> <term>menubalk</term> </termg></entry>

Lexicon Entry

Ontology and Multilingual Data

EN

DE

DT

Lexicons Documen

ts

Ontology

DT

DE

EN

Annotation of LOs

• Annotation of the text with concepts– Identification of the text chunk that will be

annotated

– Assigning of all possible concepts for the chunk

– Concept disambiguation

1. Better retrieval of LOs– Find LOs that would not be found by simple text search (where

exact search word must occur in text)

2. Multilinguality– One implementation applies to all languages in the project

3. Crosslinguality– Possible to find LOs in languages different from search/interface

language• No need to translate search query

• Search possible with passive foreign language knowledge

Added Value

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Full text search

Keyword search

Semantic search

Concept browser

Definition finder

Success in answering quiz questions by functionality

Functionality

Success

Student: Searching Test 2a Results

(target groups only)

Student: Searching Opinions 2

• What did they dislike about Semantic Search?

– It didn't return relevant results.– because it doesn't find what I am searching for– its too vague– i didn't use it a lot as the results were chaotic– i find it not much to the point for the types of research i

usually do– It is a bit too much to offer this much search methods– the name semantic is confusing– i liked this type best.. it was the easier to find the relevant

information

Student: Searching Comments: Semantic

• What did they dislike about the Concept Browser?

– It didn't return relevant results– It was too slow for my part and did not give any additional

value– it's a roundabout way of searching– was not eay to use it. maybe this was because i did not

fully understand how the concept browser worked– I am not sure what the concept browser is– don't know what it is.– for content questions this might be a relevant search

method. However, less relevant when studying a language– I like that method the most but it wouldn't be useful in my

studies - English philology– it helps a lot to understand given topic/term

Student: Searching Comments: Concept Br

0% 10% 20% 30% 40% 50%

full text search

keyword search

semantic search

concept browser

definition finder

Most useful functionality - students' opinions

Functionality

Percentage finding this the most useful

Student: Searching Opinions 1

Ontologies and social media

• Ontologies– can support the learner in the learning path; – provide the formalization of domain knowledge

approved by expert (Monachesi et al. 2008)

• Challenges– Too static;– Incomplete;– Knowledge acquisition bottleneck– Mismatch with the view of a domain by a learner – Tagging might provide better representation

Ontologies and social media

• Aim:– Create a link between the formal

representation of a given domain in the form of ontologies

and– The informal description produced by social

tagging and folksonomies

Tagging

• Main issues:– Add the informal dimension to learning by including

learning material from social media and tags• Videos (Youtube)• Images (Flickr) URLs to relevant websites (Delicious)• Q&A (Yahoo answers) Forums Blogs

– Employ NLP techniques to extract domain knowledge and relations from learning material and tags

– Create a link between existing domain ontologies, social tagging and learning material

Ontology enrichment with social tagging

• Exploit tagging to access and extract knowledge from social media applications

• Establish a link between tags, concepts and resources.

• Investigate impact of enriched ontology on advanced learners and beginners

Ontology enrichment with tagging

Experiment with delicious.com data

Social media application: Delicious• Assess:

– Whether it is possible to find related tags in case of limited resources and users, as in the case of eLearning application.

• Use of tag co-occurrence• (Use of cosine similarity)

– How the related tags corresponds with concepts present in the ontology

Tri-partite model

Tags Users Resources

Experiment with delicious.com data

• Use a domain ontology on computing• From most popular tags of delicious, select those that are in the

ontology• Top 5 tags found in this way:

– design– blog– tools tool– software– linux

• Experiment for 1 tag at a time• Select bookmarks for the tag• Find related tags by co-occurrence

Criteria for selection of bookmarks

• 5 classes of numbers of users who tagged the resource:– A: 8-13– B: 14-25– C: 26-50– D: 51-100– E: 101-200

• For each class selected 15 most recent bookmarks, for which holds:– Seed tag occurs in top-5 tags for the bookmark– Saved by the desired number of users

Example data for a bookmark

bookmarked url = wiki.mindtouch.com/MindTouch_Deki

number of people who saved this bookmark = 20

Tag: Number of users that assigned the tag:

Windows 16

Screenshot 10

Freeware 7

Utility 6

Software 6

Tool 5

Free 5

Image 3

Application 3

Work 1

Results: Related tagssoftware software:

delicious top-11

(gold standard)

linux linux:

delicious top-11

(gold standard)

Class A: 8-13 users

(sample of 15 bookmarks)

windows tools

windows

opensource

programming

mac

web

free

freeware

web2.0

utilities

linux

howto

Ubuntu

ubuntu

howto

tutorial

software

sysadmin

unix

opensource

reference

security

tools

programming

Class B: 14-25 users

(sample of 15 bookmarks)

free

freeware

windows

howto

reference

ubuntu

Class C: 26-50 users

(sample of 15 bookmarks)

freeware

tools

howto

reference

software

ubuntu

Class D: 51-100 users

(sample of 15 bookmarks)

free

freeware

mac

*macosx*

*mobile*

*osx*

howto

opensource

software

sysadmin

tutorial

ubuntu

Class E: 101-200 users

(sample of 15 bookmarks)

free

freeware

mac

tools

windows

howto

opensource

software

tutorial

ubuntu

delicious tags vs. computing ontology

Related tags for

softwareRelated tags for

linuxMerged: all related tags

for top-5 selected tags:

design, blogs, tools, software, linux

freeware mac tools (in ontology: tool) windows

macosx

mobile

osx

free (no CS)

= in ontology

software ubuntu

howto

opensource

reference

sysadmin

tutorial

= in ontology

• 33 related tags found• 7 of 33 are not in domain• 26 of 33 are in domain (79%)• 23 of 26 are in gold standard (88%)• 13 of 26 are in ontology (50%)

Aspects of ontology enrichment

• Mapping of related tags to existing concepts– Tag as concept– Tag as lexicalization

• Manual process but working towards heuristics for automatic assignment

• Addition of relations

Tag relation to concept relationFound relations between tag software, and other tags that are in the ontology:

Ontology integration for knowledge discovery

User evaluation

• Assumption: Enriched ontology can be a valid support for knowledge discovery given the explicit relations between concepts vs. tag visualization

• Hypothesis: differences in knowledge discovery approach (advanced vs. beginners)– Beginners: prefer tag visualization– Advanced: prefer ontology

Setup

• Learning task: quiz solving on markup languages – 3 questions to be answered with ontology enhanced

with tags– 3 questions to be answered with tags

• 6 beginners (no CS background, no knowledge of the domain)

• 6 advanced (CS background)• Results: questionnaire with 10 questions

Results - beginners

• What was useful in finding the answer by using:– the enriched ontology

• documents (4.4)• social tags (3.6)• conceptual structure (2.8)

– the clouds of tags• documents (3.4)• related tags (2.8)• structure (1.8)

Results -advanced learners

• What was useful in finding the answer by using:– the enriched ontology

• social tags (4.33)• conceptual structure (3.17)• documents (3.17)

– the clouds of tags• documents (3.5)• structure (3.0)• related tags (3.0)

Summary

• Beginners prefer documents rather than structure

• Advanced learners rely on tags and structure more than beginners

Social networks

• Main issues:– Knowledge discovery based on social

networking– Adapt search and recommendation algorithms

for finding relevant peers and resources– Support networking for learning purposes

Research issue

• Communities of users with common interests use multiple social networking applications

• Can we offer support?– Support = personalized search and

recommendations across social networking applications

Architecture of the applicationData sources

Indexing mechanism for the data produced in the user network

repository

Support through personalized search and recommendation

User interface

Design of services• Adapted FolkRank algorithm for search and

recommendation based on tags• Search by disambiguating tags using

knowledge bases (DBpedia and Freebase)• Convert information extracted from the social

network into semantic friendly formats (FOAF, SIOC, SCOT, DC)

Future work • Social network based knowledge discovery

– Social networks and tags

Integrated with• Content based knowledge discovery

– Ontology enhanced with tags

Integrated with • Formal learning

– Semantic annotated documents with discourse

CSF

Common Semantic Framework

• Objectives– support formal and informal learning and the

emergence of new knowledge– communication among users– identification, retrieval and recommendation

of relevant material

Support formal and social learning: Goal

• To support:– Knowledge discovery– recommendation of formal and informal

learning material and users• By means of:

– ontologies – tagging– social networks

Architecture

Conclusions

• Ontologies enriched with tags have a potential to support knowledge discovery

• Challenges:– Visualization– Best way to integrate the two– Role of social networks