Semantic Wiki: Social Semantic Web in UseJesse Wang
This is my invited talk on Semantic Wiki to the Key Lab of Intelligent Information Processing at Fudan University in Shanghai during ASWC 2009 when I gave a similar tutorial on semantic mediawiki and applications.
This presentation provides a top down introduction to semantics and Web 3.0
It is intended for the busy executive or developer who want to understand quickly why this new technological wave is relevant
For a “one slide presentation” see the first slide only
For a general introduction, see only the slides of the first section
Following slides about semantic technologies, architectures and applications
Semantic Wiki: Social Semantic Web in UseJesse Wang
This is my invited talk on Semantic Wiki to the Key Lab of Intelligent Information Processing at Fudan University in Shanghai during ASWC 2009 when I gave a similar tutorial on semantic mediawiki and applications.
This presentation provides a top down introduction to semantics and Web 3.0
It is intended for the busy executive or developer who want to understand quickly why this new technological wave is relevant
For a “one slide presentation” see the first slide only
For a general introduction, see only the slides of the first section
Following slides about semantic technologies, architectures and applications
Presentation about - Semantic Web - Overview -Semantic Web
Web of Data, Giant Global Graph, Data Web, Web 3.0, Linked Data Web, Semantic Data Web, Enterprise Information Web, HTML, CSS,
Applications of xml, semantic web or linked data in Library/Information Servi...Nurhazman Abdul Aziz
Applications of XML, Semantic Web & Linked Data in Library/Information Services & Skills needed by System Librarians.
H6716 (Internet & Web Technologies) & K6224 (Internet Technologies & Applications)
Semester 2 – 2011/2012
Hazman Aziz, Librarian (Library Technology & Systems)
Amirrudin Dahlan, Senior IT Specialist (Center for IT & Services)
Nanyang Technological University
UNIT II MODELING AND VISUALIZATION
Visualizing Online Social Networks - A Taxonomy of Visualizations - Graph Representation -
Centrality- Clustering - Node-Edge Diagrams - Visualizing Social Networks with Matrix-Based
Representations- Node-Link Diagrams - Hybrid Representations - Modelling and aggregating
social network data – Random Walks and their Applications –Use of Hadoop and Map Reduce -
Ontological representation of social individuals and relationships.
In social networks, where users send messages to each other, the issue of what triggers communication between unrelated users arises: does communication between previously unrelated users depend on friend-of-a-friend type of relationships, common interests, or other factors? In this work, we study the problem of predicting directed communication
intention between two users. Link prediction is similar to communication intention in that it uses network structure for prediction. However, these two problems exhibit fundamental
differences that originate from their focus. Link prediction uses evidence to predict network structure evolution, whereas our focal point is directed communication initiation between
users who are previously not structurally connected. To address this problem, we employ topological evidence in conjunction to transactional information in order to predict communication intention. It is not intuitive whether methods that work well for
link prediction would work well in this case. In fact, we show in this work that network or content evidence, when considered separately, are not sufficiently accurate predictors. Our novel approach, which jointly considers local structural properties of users in a social network, in conjunction with their generated content, captures numerous interactions, direct and indirect, social and contextual, which have up to date been considered independently. We performed an empirical study to evaluate our method using an extracted network of directed @-messages sent between users of a corporate microblogging service, which resembles Twitter. We find that our method outperforms state of the art techniques for link prediction. Our findings have implications for a wide range of social web applications, such as contextual expert recommendation for Q&A, new friendship relationships creation, and targeted content delivery.
PragmaticWeb 4.0 - Towards an active and interactive Semantic Media WebAdrian Paschke
Keynote at W3C Regional Event - Aspects of Semantic Technologies; Fachtagung Semantische Technologien26.-27. September 2013 | HU Berlin
http://semantic-media-web.de/referenten/?detail=33
Semantic Web & Information Brokering: Opportunities, Commercialization and Ch...Amit Sheth
Amit Sheth, "Semantic Web & Info. Brokering Opportunities, Commercialization and Challenges," Keynote talk at the workshop on Semantic Web: Models, Architecture and Management, September 21, 2000, Lisbon, Portugal.
This was the keynote given at probably the first international event with "Semantic Web" in title (and before the well known SciAm article). As in TBL's use of Semantic Web in his 1999 book, (semantic) metadata plays central role. The use of Worldmodel/Ontology is consistent with our use of ontology for (Web) information integration in 1994 CIKM paper. Summary of the talk by event organizers and other details are at: http://knoesis.org/library/resource.php?id=735
Prof. Sheth started a Semantic Web company Taalee, Inc. in 1999 (product was called MediaAnywhere A/V search engine- discussed in this paper in the context of one of its use by a customer Redband Broadcasting). The product included Semantic Web/populated Ontology based semantic (faceted) search, semantic browsing, semantic personalization, semantic targeting (advertisement), etc as is described in U.S. Patent #6311194, 30 Oct. 2001 (filed 2000). MediaAnywhere has about 25 ontologies in News/Business, Sports, Entertainment, etc.
Taalee merged to become Voquette in 2001 (product was called SCORE), Semagix in 2004 (product was called Semagix Freedom), and then Fortent in 2006 (products included Know Your Customers).
Presentation about - Semantic Web - Overview -Semantic Web
Web of Data, Giant Global Graph, Data Web, Web 3.0, Linked Data Web, Semantic Data Web, Enterprise Information Web, HTML, CSS,
Applications of xml, semantic web or linked data in Library/Information Servi...Nurhazman Abdul Aziz
Applications of XML, Semantic Web & Linked Data in Library/Information Services & Skills needed by System Librarians.
H6716 (Internet & Web Technologies) & K6224 (Internet Technologies & Applications)
Semester 2 – 2011/2012
Hazman Aziz, Librarian (Library Technology & Systems)
Amirrudin Dahlan, Senior IT Specialist (Center for IT & Services)
Nanyang Technological University
UNIT II MODELING AND VISUALIZATION
Visualizing Online Social Networks - A Taxonomy of Visualizations - Graph Representation -
Centrality- Clustering - Node-Edge Diagrams - Visualizing Social Networks with Matrix-Based
Representations- Node-Link Diagrams - Hybrid Representations - Modelling and aggregating
social network data – Random Walks and their Applications –Use of Hadoop and Map Reduce -
Ontological representation of social individuals and relationships.
In social networks, where users send messages to each other, the issue of what triggers communication between unrelated users arises: does communication between previously unrelated users depend on friend-of-a-friend type of relationships, common interests, or other factors? In this work, we study the problem of predicting directed communication
intention between two users. Link prediction is similar to communication intention in that it uses network structure for prediction. However, these two problems exhibit fundamental
differences that originate from their focus. Link prediction uses evidence to predict network structure evolution, whereas our focal point is directed communication initiation between
users who are previously not structurally connected. To address this problem, we employ topological evidence in conjunction to transactional information in order to predict communication intention. It is not intuitive whether methods that work well for
link prediction would work well in this case. In fact, we show in this work that network or content evidence, when considered separately, are not sufficiently accurate predictors. Our novel approach, which jointly considers local structural properties of users in a social network, in conjunction with their generated content, captures numerous interactions, direct and indirect, social and contextual, which have up to date been considered independently. We performed an empirical study to evaluate our method using an extracted network of directed @-messages sent between users of a corporate microblogging service, which resembles Twitter. We find that our method outperforms state of the art techniques for link prediction. Our findings have implications for a wide range of social web applications, such as contextual expert recommendation for Q&A, new friendship relationships creation, and targeted content delivery.
PragmaticWeb 4.0 - Towards an active and interactive Semantic Media WebAdrian Paschke
Keynote at W3C Regional Event - Aspects of Semantic Technologies; Fachtagung Semantische Technologien26.-27. September 2013 | HU Berlin
http://semantic-media-web.de/referenten/?detail=33
Semantic Web & Information Brokering: Opportunities, Commercialization and Ch...Amit Sheth
Amit Sheth, "Semantic Web & Info. Brokering Opportunities, Commercialization and Challenges," Keynote talk at the workshop on Semantic Web: Models, Architecture and Management, September 21, 2000, Lisbon, Portugal.
This was the keynote given at probably the first international event with "Semantic Web" in title (and before the well known SciAm article). As in TBL's use of Semantic Web in his 1999 book, (semantic) metadata plays central role. The use of Worldmodel/Ontology is consistent with our use of ontology for (Web) information integration in 1994 CIKM paper. Summary of the talk by event organizers and other details are at: http://knoesis.org/library/resource.php?id=735
Prof. Sheth started a Semantic Web company Taalee, Inc. in 1999 (product was called MediaAnywhere A/V search engine- discussed in this paper in the context of one of its use by a customer Redband Broadcasting). The product included Semantic Web/populated Ontology based semantic (faceted) search, semantic browsing, semantic personalization, semantic targeting (advertisement), etc as is described in U.S. Patent #6311194, 30 Oct. 2001 (filed 2000). MediaAnywhere has about 25 ontologies in News/Business, Sports, Entertainment, etc.
Taalee merged to become Voquette in 2001 (product was called SCORE), Semagix in 2004 (product was called Semagix Freedom), and then Fortent in 2006 (products included Know Your Customers).
Semantic domain ontologies are increasingly seen as the key for enabling
interoperability across heterogeneous systems and sensor-based applications.
The ontologies deployed in these systems and applications are developed by
restricted groups of domain experts and not by semantic web experts. Lately,
folksonomies are increasingly exploited in developing ontologies. The
“collective intelligence”, which emerge from collaborative tagging can be
seen as an alternative for the current effort at semantic web ontologies.
However, the uncontrolled nature of social tagging systems leads to many
kinds of noisy annotations, such as misspellings, imprecision and ambiguity.
Thus, the construction of formal ontologies from social tagging data remains
a real challenge. Most of researches have focused on how to discover
relatedness between tags rather than producing ontologies, much less domain
ontologies. This paper proposed an algorithm that utilises tags in social
tagging systems to automatically generate up-to-date specific-domain
ontologies. The evaluation of the algorithm, using a dataset extracted from
BibSonomy, demonstrated that the algorithm could effectively learn a
domain terminology, and identify more meaningful semantic information for
the domain terminology. Furthermore, the proposed algorithm introduced a
simple and effective method for disambiguating tags.
Maximum Spanning Tree Model on Personalized Web Based Collaborative Learning ...ijcseit
Web 3.0 is an evolving extension of the current web environme bnt. Information in web 3.0 can be
collaborated and communicated when queried. Web 3.0 architecture provides an excellent learning
experience to the students. Web 3.0 is 3D, media centric and semantic. Web based learning has been on
high in recent days. Web 3.0 has intelligent agents as tutors to collect and disseminate the answers to the
queries by the students. Completely Interactive learner’s query determine the customization of the
intelligent tutor. This paper analyses the Web 3.0 learning environment attributes. A Maximum spanning
tree model for the personalized web based collaborative learning is designed.
Maximum Spanning Tree Model on Personalized Web Based Collaborative Learning ...ijcseit
Web 3.0 is an evolving extension of the current web environme bnt. Information in web 3.0 can be collaborated and communicated when queried. Web 3.0 architecture provides an excellent learning experience to the students. Web 3.0 is 3D, media centric and semantic. Web based learning has been on
high in recent days. Web 3.0 has intelligent agents as tutors to collect and disseminate the answers to the queries by the students. Completely Interactive learner’s query determine the customization of the intelligent tutor. This paper analyses the Web 3.0 learning environment attributes. A Maximum spanning
tree model for the personalized web based collaborative learning is designed.
Talk at Semantic Technology Conference, 2010, 23 June, 2010, San Francisco.
The LOD cloud has a potential for applicability in many AI-related tasks, such as open domain question answering, knowledge discovery, and the Semantic Web. An important prerequisite before the LOD cloud can enable these goals is allowing its users (and applications) to effectively pose queries to and retrieve answers from it. However, this prerequisite is still an open problem for the LOD cloud and has restricted it to “merely more data.” To transform the LOD cloud from "merely more data" to "semantically linked data” there are plenty of open issues which should be addressed. We believe this transformation of the LOD cloud can be performed by addressing the shortcomings identified by us: lack of conceptual description of datasets, lack of expressivity, and difficulties with respect to querying.
The Semantic Web is a vision of information that is understandable by computers. Although there is great exploitable potential, we are still in "Generation Zero'' of the Semantic Web, since there are few real-world compelling applications. The heterogeneity, the volume of data and the lack of standards are problems that could be addressed through some nature inspired methods. The paper presents the most important aspects of the Semantic Web, as well as its biggest issues; it then describes some methods inspired from nature - genetic algorithms, artificial neural networks, swarm intelligence, and the way these techniques can be used to deal with Semantic Web problems.
Slides from a webinar on webware presented by Mike Qaissaunee and Gordon F. Snyder, Jr. (both of nctt.org). The webinar was hosted by MATEC NetWorks (http://www.matecnetworks.org/) and delivered via Elluminate. Visit MATEC NetWorks to watch the webinar.
Linked Data Generation for the University Data From Legacy Database dannyijwest
Web was developed to share information among the users through internet as some hyperlinked documents.
If someone wants to collect some data from the web he has to search and crawl through the documents to
fulfil his needs. Concept of Linked Data creates a breakthrough at this stage by enabling the links within
data. So, besides the web of connected documents a new web developed both for humans and machines, i.e.,
the web of connected data, simply known as Linked Data Web. Since it is a very new domain, still a very
few works has been done, specially the publication of legacy data within a University domain as Linked
Data.
Similar to Exploiting Semantic Web Techniques For Representing And Utilising (20)
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
The Metaverse and AI: how can decision-makers harness the Metaverse for their...Jen Stirrup
The Metaverse is popularized in science fiction, and now it is becoming closer to being a part of our daily lives through the use of social media and shopping companies. How can businesses survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and how does the Metaverse fit into business strategy when futurist ideas are developing into reality at accelerated rates? How do we do this when our data isn't up to scratch? How can we move towards success with our data so we are set up for the Metaverse when it arrives?
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https://arxiv.org/abs/2306.08302
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3. page 2 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
4. 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 page 3
6. 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 page 5
8. The Semantic Web page 7 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
9. 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 page 8 PREDICATE SUBJECT OBJECT
10. The Semantic Web An RDF Model can be serialised in RDF/XML An example of RDF document <?xml version="1.0"?> <rdf:RDFxmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:contact="http://www.w3.org/2000/10/swap/pim/contact#"> <contact:Personrdf:about="http://www.w3.org/People/EM/contact#me"> <contact:fullName>Eric Miller</contact:fullName> <contact:mailboxrdf:resource="mailto:em@w3.org"/> <contact:personalTitle>Dr.</contact:personalTitle> </contact:Person> </rdf:RDF> Source: W3C RDF Primer page 9
11. 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 page 10
12. 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 page 11
14. 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 page 13
15. 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 page 14
16. 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! page 15
18. 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) page 17
19. Folksonomies Tag Cloud a visualisation of popular tags popular tags stem out from others by being in larger font or emphasised page 18
23. 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. page 22
24. 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. page 23 Source: Smith G. 2008. Tagging People-Powered Metadata for the Social Web
25. 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 page 24
26. Folksonomies Richard Newman’s Tag Ontology page 25 Source: Haklae Kim et al., Review and Alignment of Tag Ontologies for Semantically-Linked Data in Collaborative Tagging Spaces
27. Folksonomies Tom Gruber’s Conceptual Model Tagging(object, tag, tagger, source, + or -) page 26 Source: Gruber T., Ontology for Folksonomy: A Mash-Up of Apples and Oranges.
28. 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 page 27
29. Folksonomies CommonTag To add concepts to tags from databases such as Freebase and DPPedia page 28 Source: CommonTag
30. 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 page 29
31. Folksonomies Social Semantic Cloud of Tags (SCOT) An ontology aimed to represent set of tags Built on top of Richard Newman’s Tag Ontology page 30 Source: SCOT: Let's Share Tags!
32. 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? page 31
33. 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? page 32
35. Fast Unfolding of Communities in Large Networks 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 page 34 Source: Blondel V.B. et al. 2008. Fast unfolding of communities in large networks
36. 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. page 35
37. Fast Unfolding of Communities in Large Networks 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. page 36
40. State of the Art Tool 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 page 39
41. State of the Art Tool A visualisation of tagging graph that depicts the relationships amongst tags page 40
42. State of the Art Tool 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> page 41
43. State of the Art Tool 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 page 42
44. State of the Art Tool 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 page 43
45. State of the Art Tool 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 page 44
46. State of the Art Tool The Community Structure Ontology page 45 CommunityStructure UnfoldedCommunity UnfoldingActivity Community CommunityAggregation linkedIn associatedCommunity linkedWith Linkage name sioc:Resource modularity pass linkedTag communityOf Community linkWeight scot:Tag
47. State of the Art Tool 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 page 46
48. State of the Art Tool 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 page 47
49. State of the Art Tool 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 page 48
51. State of the Art Tool The tool provides the following features: Properties to setup: The Edge List Directory The Edge List File Structure page 50
52. State of the Art Tool Settings to setup the type of storage required RDF/XML documents page 51
53. State of the Art Tool Relational database persistent storage A TDB storage, a custom fast persistent storage page 52
54. State of the Art Tool Properties to setup the Ontologies page 53
55. State of the Art Tool 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. page 54
56. State of the Art Tool 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 page 55
57. State of the Art Tool 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 page 56
58. State of the Art Tool Writing of RDF Statements (Results) page 57
59. State of the Art Tool 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 page 58
60. State of the Art Tool 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 page 59
61. State of the Art Tool 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 page 60
62. State of the Art Tool Querying Statements (Results) Results are based on a community called malian This community has 57 linked communities and 15 linked tags page 61
63. State of the Art Tool Other features RDF Document Viewer page 62
65. 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 page 64
66. 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 page 65
68. 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 page 67
Web 2.0 is the second generation of the web that evolved from Web 1.0Web 1.0 was a read only web with static content and lacked user involvementWeb 1.0 site under construction and Web 2.0 is beta
Taggers are foaf:Agents Taggings reify the n-ary relationship between a tagger, a tag, a resource, and a date.Tags are members of a Tag classTags have names
Notably, Gruber defines the source as the scope of namespaces or universe of quantification for objects.The object in this model represents the content which is being tagged; the tag is the label or word used to tag with; the tagger represents who tagged the object; the source is the system where the actual tagging model is stored; the polarity represents a + or –, which is “a vote” of the tagging fact, that is to assert that the tagging fact is true or not.
Limitations of tagging due to its independence and free-form structureDiscrepancies: java is to specific for some users but programming language is to generic for others
Freebase provides datasets built by communities that are freely accessible. Freebase offers tools that help developers access and control the content contained within these datasetsDBPedia extracts information from the online encyclopaedia Wikipedia and provides such information in a semantic format that can be processed by machines.
The nodes contain numbers that are connected to tags