exploiting semantic web techniques for representing and utilising

Exploiting Semantic Web Techniques for Representing and Utilising FolksonomiesOwen Sacco

Presentation Map

Presentation Map

Introduction• Aim & Goals

The Semantic Web• Meta Formats, Vocabularies & Query Language

Web 2.0• Web 2.0 Technologies & Applications

Folksonomies• Tags, Tagging, Representing Tags Semantically &

Integrating Folksonomies with the Semantic Web

Presentation Map

Graph Mining Techniques• Fast Unfolding of Communities in Large Networks

State of the Art Tool• Examining the Edge List• The Community Structure Ontology• Jena & Corese• Creating & Querying RDF Statements• Analysis & Results

Conclusion• Enhancements & Future Work

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Introduction

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Introduction

The research is about:• Understanding various Semantic Web technologies

for representing data semantically• Understanding Folksonomies and how to

semantically represent them• To semantically represent tags retrieved from

Bibsonomy (http://www.bibsonomy.org/)

- The tags have been hierarchically structured using the algorithm “fast unfolding of communities in large networks”

• Use Semantic Web technologies to create and exploit such representation of tags

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http://www.bibsonomy.org/

The Semantic Web

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The Semantic Web

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What is the Semantic Web?• Not a separate Web • An extension of the current Web• Semantic = Meaning• Semantic Web = Meaningful Data• Meaning is data about data, i.e. Metadata

Advantages of Semantic Web:• Information is given well-defined meaning • Better enabling computers• People to work in cooperation Source: W3C

Semantic Web

The Semantic Web

Resource Description Framework (RDF)• A framework that describes resources on the WWW• Suitable for merging data on the Web• Resources are uniquely identified by URLs• The RDF Model is made up of triple statements• Triple Statements: Subject, Predicate & Object

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SUBJECT OBJECTPREDICATE

The Semantic Web

• An RDF Model can be serialised in RDF/XML• An example of RDF document

<?xml version="1.0"?> <rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"

xmlns:contact="http://www.w3.org/2000/10/swap/pim/contact#"> <contact:Person rdf:about="http://www.w3.org/People/EM/contact#me">

<contact:fullName>Eric Miller</contact:fullName> <contact:mailbox rdf:resource="mailto:[email protected]"/> <contact:personalTitle>Dr.</contact:personalTitle> </contact:Person>

</rdf:RDF>

Source: W3C RDF Primer

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The Semantic Web

Ontology• “A formal explicit specification of a shared

conceptualisation”• In other words: parties having a common concept of

data agree and specify clearly as possible such concepts

• It is an enabling technology for information sharing and manipulation

• A vocabulary for RDF documents• Ontologies are based on RDF models and are

expressed by using the Web Ontology Language

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The Semantic Web

SPARQL – An RDF Query Language• Query in the Semantic Web context means:

“Technologies and protocols that programmatically retrieve information from the Web of Data”.

• Based on triple patterns similar to RDF triples• A query returns resources for all RDF triples that

match the query’s pattern• Is used to return complex data for mash-ups or

search engines containing semantic data• Syntax is similar to SQL

Source: W3C

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Web 2.0

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Web 2.0

A “Read/Write” Web Web 2.0 has:• Facilitated web design• Provided attractive, rich, easy-to-use interfaces• Assisted in reuse of data by merging information

from various sources• Created social networks of people

According to Internet World Stats, between 2000 and 2003 users doubled thanks to Friendster (one of the first social network websites)

Source: Internet World Stats - Internet Growth Statistics

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http://www.internetworldstats.com/emarketing.htm

Web 2.0

Web 2.0 is considered a Social Web• People are more involved by collaborating & sharing

data One of the major Web 2.0 technologies for web

development is AJAX• A combination of several technologies:

- HTML or XHTML

- Cascading Style Sheets (CSS)

- Java Script

- XML

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Web 2.0

Web 2.0 created new application concepts:• Blogs (Blogger, WordPress)• Wikis (Wikipedia)• Really Simple Syndication, RSS• Mashups (MusicMesh, BBC Music)• Social Networks (Facebook, LinkedIn, MySpace)• Social Bookmarking (delicious, Bibsonomy)• Photo Sharing (Flickr)• Video Sharing (YouTube, Vimeo)

In most of these concepts you find Tagging!

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Folksonomies

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Folksonomies

Tag• “A non-hierarchical keyword or term”

Tagging• “Assign a tag to a piece of information or resources”

Tagger• “The person that assigns the tag”

Folksonomy• “The result of personal free tagging of information

and objects for one’s own retrieval. The tagging is done in a social environment.” Thomas Vander Wal (2004)

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Folksonomies

Tag Cloud• a visualisation of popular tags • popular tags stem out from others by being in larger

font or emphasised

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Folksonomies

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Where can we tag?• Social Bookmarking websites

Folksonomies

• Picture sharing websites

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Folksonomies

• Video sharing websites

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Folksonomies

Why tagging?• It’s Popular

- Nowadays, practically anyone who uses a computer or the Internet is exposed to tagging in some way.

• It’s Social

- Through the most popular tags, we can see a kind of rough consensus on the subject of the resource.

• It’s Flexible

- Ad-hoc, free-form and does not adhere to any strict classification scheme or vocabulary.

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Folksonomies

Basic Model• Taggers create the tags,

and sometimes they add resources.

• If we can identify something, then it can be tagged.

• Tagging is open-ended, tags can be any kind of term.

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Source: Smith G. 2008. Tagging People-Powered Metadata for the Social Web

Folksonomies

How about:• Collaborative sharing tags across multiple

applications• Collaborative filtering based on tagging• Connecting people based on tagging

All these can be achieved through Tag Ontologies• Ontology is not a taxonomy• Ontology makes semantic agreement• Semantic agreement enables useful composition

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Folksonomies

Richard Newman’s Tag Ontology

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Source: Haklae Kim et al., Review and Alignment of Tag Ontologies for Semantically-Linked Data in Collaborative Tagging Spaces

Folksonomies

Tom Gruber’s Conceptual Model

Tagging(object, tag, tagger, source, + or -)

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Source: Gruber T., Ontology for Folksonomy: A Mash-Up of Apples and Oranges.

Folksonomies

Limitations of tagging:• Ambiguity of tags (example: apple is it a fruit or the

computer company?)• Lack of synonymy (example: lorry or truck)• Discrepancies in granularity (example: java vs

programming language)• Flat Organisation of Folksonomy

How do we overcome these?• Use: CommonTag, MOAT, SCOT

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Folksonomies

CommonTag• To add concepts to tags from databases such as

Freebase and DPPedia

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Source: CommonTag

http://www.commontag.org/Home

Folksonomies

Meaning Of A Tag (MOAT)• An ontology to represent how different meanings

(URIs of semantic Web resources) can be related to a tag

• Extends the Tag class from Richard Newman’s tag ontology

• Tagging (User, Resource, Tag, Meaning)• Architecture of MOAT Framework:

- MOAT server stores different meanings that can be queried

- MOAT client interacts with the server to let users easily annotate their content

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Folksonomies

Social Semantic Cloud of Tags (SCOT)• An ontology aimed to represent set of tags• Built on top of Richard Newman’s Tag Ontology

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Source: SCOT: Let's Share Tags!

http://scot-project.org/

Folksonomies

Limitations of the previous ontologies:• An extra step is being added to the tagging activity• Isn’t it daunting for the user when presented with a

list of meanings to choose from? • Which meaning shall the user choose?• Will tagging remain popular with this additional step?• If an automatic process is used to select a meaning

of a tag, how accurate can this process be? • Can this process really understand the user at that

instance?

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Folksonomies

• With this additional meaning, isn’t tagging becoming another “strict” classification scheme?

• Can relationships of tags really be built on meanings?

• How about using some form of algorithm that can unfold new relationships of tags?

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Fast Unfolding of Communities in Large Networks

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A recursive method to extract the community structure of large networks

This method is based on modularity optimisation The modularity is a scalar value that measures the

density of links inside communities as compared to links between communities

It unfolds a complete hierarchical community structure for large networks in a short time

Results have shown that on a network of 118 million nodes, the algorithm took 152 minutes

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Source: Blondel V.B. et al. 2008. Fast unfolding of communities in large networks


The algorithm consists of two phases which are iterated until a maximum modularity is attained.

First, all nodes are assigned to different communities.

Then each node is compared with its neighbours. The node is placed in the community which yields a maximum gain in modularity.

This process is repeated for all nodes until no further movement can be attained.

The second phase consists of building a network whose nodes are now the communities found during the first phase.

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After the second phase, the process starts again with the first phase

A “pass” denotes a combination of both passes The “passes” are iterated until there are no more

changes and the maximum modularity is reached for the whole network

The height of the network denotes in the number of passes

At the end, a hierarchical structure is attained that consists of communities of communities.

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State of the Art Tool

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The Data• It is provided beforehand• Consists of a hierarchical structure made up of

communities of communities of related tags • This hierarchical structure is constructed using the

“Fast Unfolding of Communities in Large Networks” algorithm

• The tags are from the Social Bookmarking Website Bibsonomy (http://www.bibsonomy.org/)

• The aim for using the community structure algorithm is to unfold new relationships amongst tags

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http://www.bibsonomy.org/


• A visualisation of tagging graph that depicts the relationships amongst tags

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The Input to the system will consist of Edge Lists Each Edge List file consists of a pass 4 Edge List files were used for this system: • The first list is a plain list of related tags queried from

Bibsonomy• The other three lists denote communities or

communities of communities computed from the community structure algorithm

Each relation (line) in each of the Edge List file consists as follows:• The first edge list: <tagi, tagj, weight>

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• The other three edge lists:

- <communityi, tagj, weight> or

- <communityi, communityj, weight> The Edge List files contain:• The first (lower level): 13126 nodes with 264718

edges• The second (first pass): 529 nodes with 6337 edges• The third (second pass): 65 nodes with 374 edges• The fourth (third pass): 50 nodes with 207 edges

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A sample from one of the edge lists (the lower level file)

caching,offlinebrowser,2.0

caching,archiving,2.0

institutions,activity,1.0

malian,senegal,2.0

malian,northern,2.0

malian,guinea,2.0

malian,drummers,2.0

cdf,c,1.0

cdf,library,1.0

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First Task: To semantically represent all edge lists that represent the hierarchical structure

Since the lower level edge list is made up of a set of tags, then the tags will be described using the SCOT ontology

But to represent the hierarchical structure of communities, a new ontology must be designed that needs to be built on top of SCOT and also, the new ontology must be linked to SCOT

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The Community Structure Ontology

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CommunityStructure

Community CommunityAggregation

name

scot:Tag

Linkage sioc:Resource

CommunitylinkWeight

modularity pass

UnfoldedCommunity UnfoldingActivity

linkedIn

linkedWith

linkedTag

associatedCommunity

communityOf


Ontology was designed with a tool called Protege – A Java application for designing Ontolotgies

Ontology built on OWL2 Classes: CommunityStructure, Community,

CommunityAggregation, Linkage Object properties: associatedCommunity,

communityOf, linkedIn, linkedTag, linkedWith, unfoldedCommunity, unfoldingActivity

Data properties: communityName, linkWeight, modularity, pass

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Second Task: To create an application that will transform the edge lists to RDF/XML statements and store the documents on physical storage. Also, a query engine will be included into the application to query the RDF/XML statements.

The application is developed using the Java programming language.

For the creation of RDF/XML statements and to write such statements to physical storage, a widely used API is embedded in the system. This API is called the JENA API

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Jena – A Semantic Web Framework• Developed by HP• An RDF API for reading and writing RDF models in

RDF/XML• An OWL API for reading and writing OWL ontologies• In-memory and persistent storage for writing

RDF/XML statements to memory or physical storage such as text files or even relational databases

• SPARQL query engine

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The Tool

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The tool provides the following features:• Properties to setup:

- The Edge List Directory

- The Edge List File Structure

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- Settings to setup the type of storage required

– RDF/XML documents

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– Relational database persistent storage

– A TDB storage, a custom fast persistent storage

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Properties to setup the Ontologies

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The Method to transform the edge list to RDF Statements:• First, the edge lists are merged together and

ordered according to their hierarchical structure• Second, the RDF Model consisting of RDF

statements are created according to the Community Structure and SCOT Ontologies

• Third, RDF statements are written according to the settings setup.

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Writing of RDF Statements• RDF Documents:

- For whole documents: the whole document is written after the whole model is created

- For split documents: documents are written after the model for each community is created.

– Two index lists are created, one A-Z and an other to indicate where each community document is located

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Writing of RDF Statements• RDF Persistent Storage

- RDB Method: MySQL is used as a persistent relational databases and RDF statements are written on-the-fly, i.e. After each statement is created, these are written in the database

- TDB Method: each statement is written on-the-fly as well

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Writing of RDF Statements (Results)

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Storage Method DurationRDF Documents (Whole Documents) 1.9 minutes

RDF Documents (Split Documents) 2.2 minutes

RDF Persistent Storage (RDB Method) 35.2 minutes

RDF Persistent Storage (TDB Method) 3.2 minutes


Querying Statements• For RDF Documents Corese SPARQL Engine was

used

- Corese SPARQL Engine is developed by Edelweiss

- Built on top of Jena with some added enhancements such as Approximated Searches, Select Expressions

- Queries only RDF documents and does not have the capability of querying directly to relational databases

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Querying Statements• For Persistent Storage, the Jena SPARQL Engine is

used since Jena allows for direct querying Querying Methods• RDF Documents (Split Documents):

- First query index lists

- Get community document

- Query community document and get linked communities

- Query index list and query contents for each community

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Querying Methods• RDF Documents (Whole Documents)

- Query whole model and query for community

- Retrieve linked communities

- Query linked communities for their content• Persistent Storage

- Query whole model and query for community

- Retrieve linked communities

- Query linked communities for their content

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Querying Statements (Results)• Results are based on a community called malian• This community has 57 linked communities and 15

linked tags

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Storage Method DurationRDF Documents (Whole Documents) Out of memory

RDF Documents (Split Documents) 43.3 minutes

RDF Persistent Storage (RDB Method) 7 seconds

RDF Persistent Storage (TDB Method) 1 second


Other features• RDF Document Viewer

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Conclusion

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Conclusion

In this research we have seen the importance of Semantic Web and to describe semantically Web data

We have seen the importance of using folksonomies for search and exploration

Additionally, we have also seen various ontologies of how such folksonomies can be semantically represented

From community structure algorithms and graph mining techniques, new relationships amongst other tags can be unfolded

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Conclusion

An ontology was designed and developed for the fast unfolding of communities in large networks

From this ontology, RDF/XML statements can be created and are linked to the SCOT ontology

We have seen that by using Triple Stores, persistent storage for triple statements is much faster for querying

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Future Enhancements

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Future Enhancements

To try this model on larger tag models from different websites

To include the tagger and links to the actual resource

To analyse these links that contribute to the linked data initiative

Optimise writing and querying based on larger models

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exploiting semantic web techniques for representing and utilising

Technology