2011 04-04 (educon2011) emadrid etovar upm finding oers with social semantic search

Finding OERs with Social-Semantic Search

{N. Piedra, J. Chicaiza, J. López}1 , E. Tovar2 and O. M Bonastre3

1(Universidad Técnica Particular de Loja; Ecuador)

2(Politechnic University of Madrid, Spain)

3(Miguel Hernández University, Spain)

2nd Annual Global Engineering Educational ConferenceIEEE Educon 2011, Amman, Jordan 4-6 April 2011

Open Educational Resources

Teaching, learning, and research digital resources and tools, available on the public domain or that have been released under an intellectual property license, that is, permit their free use, re-use or re-purposing by others


Web 2.0 technologies augment the Web power.

Semantic technologies enable structure and semantically enriching of the content


Why merge these two Web visions?

• “Social and Semantic Web are two approaches complementary and each field can and must draw from the other strengths”

(Ankolekar et al., 2008)


Synergy between social & semantic web

ecosystem of participation ecosystem of data

Data created collaboratively

and semantically enriched

Aggregation of user

contributions

Integration of structured

data


There is a need for counting with semantic metadata in the description of the OERs, i.e., semantic search engine to find accurate results and annotate

educational resources

Social-Semantic Technologies driven to OER searcher


Some ways to find open educational resources

DiscoverEd

Courseware Watchdog


OER-ONTO SEARCH

A searcher based on metadata of Open Educational Resources and user contributions


Why is neccesary a vertical search engine for OERs?

• General search engines which are based on horizontal search, do not know the context

• Development and usage of vertical search engines could be the option to find more accurate results, thus: specialized search (to find out and retrieve only educational content) filtering of content (to focus the target audience and its preferences), and, the possibility to choose the most key educational resources provider.


Why is neccesary a vertical search engine for OERs?

• By means of metadata enrichment and logic inference, OER consumers will get more precise results from general search engines.

• At the moment of determining the relationship between the open resources, social annotations, RDF graphs and expert's recommendations, the system itself will be in charge of recommending action paths for information seekers


The challenges

• Information about open educational content is maintained in heterogeneoussites/repositories

• Different metadata schemas are used for describe educational material


The challenges

• Apart from its heterogeneous nature, another important feature of the OER domain is that the content offered on the web sites continuously undergoes changes. The RDF store change to reflect additions of new OER


The implementation process

• Metadata & annotations

Adquisition

• RDF triples from metadata & annotations

Encoding and Storage

• Resources properties retrieved from RDF store

Application


Adquisition

http://ocw.mit.edu

http://ocw.utpl.edu.ec


http://ocw.mit.edu/

http://ocw.utpl.edu.ec/

Encoding

• For representing the semantic metadata of OER – We use the OER-CC ontology, which has been

designed to support the OER search based on Semantic-Social Web technologies.

• For Ensuring Metadata Quality of the Semantic Metadata– Automatic methods to extract and clean metadata


StorageMIT-OCW

Knowledge area: Electrical Engineering and Computer ScienceAddress Web Feed: feed://feeds.pheedo.com/ OcwWeb/rss/new/mit-newcourses-6

Obtained Metadata: resource URL, title, description, Publisher, language, rights, date, creator, relation and subjectKind of resources: OCW coursesTriplets number: 1380

UTPL-OCWKnowledge area: Systems and Computer EngineeringAddress Web Feed: feed://ocw.utpl.edu.ec/sistemas-informaticos-y-computacion/rss_recent

Obtained Metadata: resource URL, title, description, Publisher, language, Rights, date, creator and subjectKind of resources: OCW courses and Youtube videosTriplets number: 1690


Implementation

• Implemented features for OER-Onto search:

– It searches for keywords (simple and advanced)– It provides options for results filtering– It also provides descriptions in many languages (if

multilingual descriptions of metadata were specified) – The presentation of results is made in HTML and RDF

formats, what it makes easier the communication with human and machine agents

– Support for search refinement, i.e., it provides tag cloud and RDF Graph of related OER


Tier Layers of the Search Engine Prototype2nd Annual Global Engineering Educational ConferenceIEEE Educon 2011, Amman, Jordan 4-6 April 2011

Search Interface


Initial Evaluation

• To check the supported functionalities, we have made an experimental evaluation with educators, students, and self-learners from Computer Science School of UTPL


Answer level 0 1 3 5 7 9

QuestionsNo

result

insufficient result

result with heavy

analysis

result with

moderate

analysis

good result

excellent

result 1. Java for beginners X

2. Video of descriptive statisticals X

3. OER with CC license Share Alike X

4. Course in Spanish X

5. OER created by UTPL X


Discussion

• Queries about information that exist in metadata. Good results.

• The proposed searcher was able to answer a high number of the questions despite its simplicity.


Future work and Conclusions

• Currently, we are working to implement other supporting components, such as design of an improvement metric system

• Enhanced OER search to continue integrating social and semantic web technologies

• Final Thoughts: When having semantically described resources and offering the results of a search in RDF format, the authors think it will contribute to create a Web of Data –Linked Data- for Open Educational Resources. In this way, different human and software agents will be able to communicate among them


References• Ankolekar, A., Krötzsch, M., Tran, T., and Vrandecic, D. “The two

cultures: Mashing up Web 2.0 and the Semantic Web”. Journal of Web Semantics, Elsiver, 6 (1), 2008, 70-75

• Gruber, T. “Collective Knowledge Systems: Where the Social Web meets the Semantic Web”, Journal of Web Semantics, 6 (1), 2008, pp. 4-13.

• Heath, T. “Information-seeking on the web with trusted social networks: from theory to systems”. PhD thesis, The Open University, 2008

• López, C., García, F. J. “Ontologies applied to Learning Objects Repositories for educational environments in the Semantic Web”. m-ICTE, 2005

• Piedra, N. Chicaiza, J. López, J. Martínez, O. Tovar, M. “An approach for description of Open Educational Resources based on semantic technologies”, Proceedings on EDUCON 2010, IEEE, 2010, 1111 -1119


Acknowledgements

• This work has been supported by e-Madrid Project, S2009/TIC-1650, which promotes research and development of technologies to support learning (Technology-Enhanced Learning)


2011 04-04 (educon2011) emadrid etovar upm finding oers with social semantic search

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