Jtelss presentation Paola Monachesi
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Jtelss presentation Paola Monachesi Jtelss presentation Paola Monachesi Presentation Transcript

  • 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 eLearning
    • Examples 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&al.) 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 Lexicalized Terms Free Phrases
    • <entry id=&quot;id60&quot;>
    • <owl:Class rdf:about=&quot; http://www.lt4el.eu/CSnCS#BarWithButtons &quot;>
    • <rdfs:subClassOf>
    • <owl:Class rdf:about=&quot; http://www.lt4el.eu/CSnCS#Window &quot;/>
    • </rdfs:subClassOf>
    • </owl:Class>
    • <def> A horizontal or vertical bar as a part of a window,
    • that contains buttons, icons. </def>
    • <termg lang=&quot; nl &quot;>
    • <term shead=&quot; 1 &quot;> werkbalk </term>
    • <term> balk </term>
    • <term type=&quot; nonlex &quot;> balk met knoppen </term>
    • <term> menubalk </term>
    • </termg>
    • </entry>
    Lexicon Entry
  • Ontology and Multilingual Data EN DE DT Lexicons Documents 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
  • 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
  • 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 1 Work 3 Application 3 Image 5 Free 5 Tool 6 Software 6 Utility 7 Freeware 10 Screenshot 16 Windows Number of users that assigned the tag: Tag: number of people who saved this bookmark = 20 bookmarked url = wiki.mindtouch.com/MindTouch_Deki
  • Results: Related tags howto opensource software tutorial ubuntu free freeware mac tools windows Class E: 101-200 users (sample of 15 bookmarks) howto opensource software sysadmin tutorial ubuntu free freeware mac *macosx* *mobile* *osx* Class D: 51-100 users (sample of 15 bookmarks) howto reference software ubuntu freeware tools Class C: 26-50 users (sample of 15 bookmarks) howto reference ubuntu free freeware windows Class B: 14-25 users (sample of 15 bookmarks) ubuntu howto tutorial software sysadmin unix opensource reference security tools programming howto Ubuntu tools windows opensource programming mac web free freeware web2.0 utilities linux windows Class A: 8-13 users (sample of 15 bookmarks) linux: delicious top-11 (gold standard) linux software: delicious top-11 (gold standard) software
  • delicious tags vs. computing 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%)
    • software
    • ubuntu
    • howto
    • opensource
    • reference
    • sysadmin
    • tutorial
    • = in ontology
    • freeware
    • mac
    • tools (in ontology: tool)
    • windows
    • macosx
    • mobile
    • osx
    • free (no CS)
    • = in ontology
    Merged: all related tags for top-5 selected tags: design, blogs, tools, software, linux Related tags for linux Related tags for software
  • 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 relation
    • Found 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 application Data sources Indexing mechanism for the data produced in the user network repository Support through personalized search and recommendation User interface 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