=Paper=
{{Paper
|id=Vol-312/paper-4
|storemode=property
|title=Bridging Different Generation of Web via Exploiting Semantic Social Web Blog Portal
|pdfUrl=https://ceur-ws.org/Vol-312/bof07-hu-yu.pdf
|volume=Vol-312
}}
==Bridging Different Generation of Web via Exploiting Semantic Social Web Blog Portal==
https://ceur-ws.org/Vol-312/bof07-hu-yu.pdf
Bridging Different Generation of Web
via Exploiting Semantic Social Web Blog
Portal
Yuh-Jong Hu and Cheng-Yuan Yu
Emerging Network Technology (ENT) Lab. Dept. of Computer Science
National Chengchi University, Taipei, Taiwan, 11605, (hu, g9302)@cs.nccu.edu.tw
Abstract. The goal of this research is to analyze one of the Web 2.0 platforms, e.g.,
weblog (or blog) and to justify whether it is possible to bridge Web 2.0 ↔ Web 3.0
(or the semantic web) via exploiting semantic social web blog portal. Compared with
semantic annotation system using web mining techniques to extract keywords from
the WWW, our semantic social web annotation system is based on ontologies derived
from folksonomy tagging system to truly reflect the intentions of people on the
classification of resources. The blogsphere will be our first experimental example to
validate the ontology+folksonomy mashup model. We hope this idea can be applied
to the other Web 2.0 platforms, such as wiki, web services. We have built a semantic
social web blog portal from the Taiwan’s biggest blog service provider (BSP). From
this semantic social web blog portal, users are allowed to execute a variety of online
semantic social web queries that can not be achieved from other Web 2.0 blog search
engines, such as Blogpulse or Technorati. The incentives of having semantic social
web annotation for blogsphere were justified and this might shed some light on
bridging Web 2.0 ↔ Web 3.0.
1 Introduction
The principles on how to identify one application as “Web 1.0” and another
as “Web 2.0” were previously clarified by Tim O’Reilly [21]. The Web x.0 in-
dicates how the x.0 Web generation platform copes with their contents writer
and reader’s experiences. The bridging of Web generation is defined as the
contents created in previous generation of Web can be extracted or accessed
in next generation (or vice versa). The bridging of Web 1.0 ↔ Web 2.0 is an
ongoing process while the bridging of Web 2.0 ↔ Web 3.0 is not well under-
stood yet. To bridge different Web generation does not necessarily mean the
old generation Web will be completely phased out. On the contrary, different
Web generation still might be happily live together. Ever since the New York
Times reporter John Markoff coined the semantic web as Web 3.0 [16], we
were curious whether there existed a feasible bridging mechanism for Web 2.0
↔ Web 3.0 via integrating respective folksonomy and ontology technologies.
The folksonomy of tagging system for blogs is an example to enable so-
cial web services in the Web 2.0. On the other hand, ontologies with their
machine understandable metadata aim at achieving Web 3.0 vision. If we can
(semi-)automatically mash up the ontology data and query model with the
folksonomy tagging system services, then we are in a very good shape toward
this paradigm shift. Eventually, this might realize Tim Berners-Lee’s seman-
tic web vision for an extension of the current web (Web 1.0 or 2.0) in which
information is given well-defined meaning and better enabling computers and
people to work in cooperation [2].
In Web 2.0, people interact with each other and address their opinions vol-
untarily. The challenge of this social web services depends on whether we can
collect these huge amount of unstructured public opinions and discover the
patterns among them. For the past few years, research issues for the develop-
ment of annotation system on bridging Web 1.0 ↔ Web 3.0 were intensively
investigated. People were trying to figure it out whether it is possibly to bridge
existing Web 1.0 with the future semantic web (or Web 3.0) [7][8][14]. Unfor-
tunately, the progress of this study seems to be very slow because it is a
grand challenge to have (semi)-automatic semantic annotation system to cre-
ate ontology-based semantic annotations from huge amount of unstructured
WWW contents.
The social web annotation of bridging Web 2.0 ↔ Web 3.0 seems to provide
another window to deal with this problem. In social web annotation system,
people use free tags (or vocabularies) to address their opinions or prefer-
ences on the Internet resources, such as bookmarks, videos, blogs, and web
pages, without relying on controlled vocabularies. This resolves a hard design
problem for the construction of agreeable monolithic heavyweight ontologies.
Because it is more explicit and direct on the categorization of resources via
free tags from folksonomy than keywords mining from the Web’s contents [4].
Tagging systems are still not well studied and have the research potential for
further improvement [17]. Are there any other incentives to use free tags social
web annotation rather than to use conventional keywords-based annotation?
This will be the issue we are interested to investigate further.
Ontologies are top-down approach with hierarchical classification of infor-
mation sharing and manipulation mechanism while folksonomy is a bottom-up
approach using flat indexing to organize and search information through user
feedback. When we regard users’ free tags as social web annotations, we still
might need ontologies to classify these free tags into different taxonomy. Fur-
thermore, ontologies can provide well-defined structure schema to bind entity
semantic association together and that was impossible to be realized by the
tagging system alone. These entity relationship semantics might exist among
tagger, tags, and resources declared implicitly by entity themselves. We pro-
pose a blog ontology and a topic ontology to harbor all of these free tags
to describe the semantics of entity relationships in the blogspace. We allow
users to explicitly enable semantic social web query for tags with their entity
semantic relationships to get what they are really interested.
In order to exploit the incentives of bridging different Web generation,
we have built a semantic social web blog portal from the biggest blog service
provider (BSP) WRETCH in Taiwan 1 . We have implemented blog crawlers to
collect all of necessary context and content information from this BSP. Three
kinds of information sources were collected for this study: semi-structured
HTML blog pages, structured XML-based RSS, and users’ annotation free
tags. The content and context information from these sources were extracted,
analyzed, and stored to satisfy user’s later semantic query services. Further-
more, we also analyzed the blog information diffusion flow using social network
analysis (SNA) to examine the possible patterns in the WRTECH BSP [24].
Therefore users are allowed to enable semantic social web query services using
a variety of SNA measures in our semantic social web blog portal.
2 Related Work
Several important elements are required to exploit the bridging problem of
Web 2.0 ↔ Web 3.0 to have semantic social web search services. They are
annotation, ontology, blog, folksonomy, and SNA. Unfortunately, most of the
related studies shown as the followings did not have these comprehensive
considerations so they can not have the service capabilities as ours:
• Semantic annotation for ontology+web: The semantic annotation (or
bridging) of Web 1.0 ↔ Web 3.0 were extensively investigated before to
support the indexing and retrieval of well-defined semantic information for
agents [14][19][22]. The goals of these studies were too ambitious to have
any significant progress.
• Semantic tag for ontology+folksonomy: Gruber proposed the mashup of
ontology and folksonomy to enable social web ecology on the Internet [10].
The tagOntology was a very primitive study for identifying and formalizing
a conceptualization of the activity of tagging.
• Semantic blog for ontology+blog: Semantic blog systems were built to
leverage the power of ontology data model so that people can extract
all of the implicit semantics from blogs [3][5][13]. But they did not really
work for lacking enough amount of real dataset to experiment the system’s
feasibility.
• Tagging blog for tags+blog: Brooks et al. analyzed the top 350 tags from
the Technorati blog search engine and they demonstrated that tags are
useful for grouping articles into broad categories but less effective in indi-
cating the particular content of an article [4]. This study did not aim at
solving the bridging problem of Web 2.0 ↔ Web 3.0 either.
• Semantic Web (or Web) as social network: In a semantic social network,
a number of electronic information sources including web pages, emails,
1
http://www.wretch.cc/
FOAF profiles, are extracted and analyzed to acquire their semantic rela-
tionships [9][19]. The purposes of these studies were to apply SNA tech-
niques to analyze the ontology-based context information for the semantic
web research community.
• Blog as social network: Gruhl et al. studied the dynamics of informa-
tion propagation through blogspace [11]. Furthermore, the blogspace can
be shown as community using SNA model to express its entity social rela-
tionships through links, comments, and trackbacks, etc [1][6][15]. But they
only addressed pure blog ecosystems.
3 Research Goal
The goal of this research is to construct a semantic social web portal and
to exploit the incentives of bridging Web 2.0 ↔ Web 3.0. The incentives
will be justified when we can search information through this semantic social
web portal compared with other systems that only provide simple tags (or
keywords) search on Web 2.0 or ontology query on Web 3.0. Unless we can
extend tags to have corresponding semantic context, the expressive power
of tags is limited. In this study, we found that coherent taxonomies of blog
articles can emerge from users tagging so relevant customized ontologies can
be constructed.
3.1 Social Network Analysis
Social network analysis (SNA) is the quantity study of the relationships be-
tween individuals or organization. By quantifying social network structures,
we can determine where are the most important nodes in the network [24].
The implications of SNA usage are quite different when we apply SNA to
different generation of Web.
• SNA for Web 1.0: The information on Web 1.0 is rather static so people
only apply SNA on paper citation network or person relationship network
to discover their stable relationships [18].
• SNA for Web 2.0: The nature of information flow on Web 2.0 is dynamic
and user oriented. All of the tags, resources, and tagger’s profiles on Web
2.0 are dynamically created so the challenge to apply SNA for this platform
is how can we timely extract the relationships between taggers with an-
notated tags and their respective resources to enable effective information
search [12].
• SNA for Web 3.0: We are aiming at bridging of Web 2.0 ↔ Web 3.0. The
issues we consider including Web 2.0, Web 3.0, and SNA, are different from
pure semantic social network approach shown in [19].
3.2 Blogs as Social Network
Applying SNA model to the blogsphere has revealed interesting findings about
how individuals share information and interact socially online. Social relation-
ships can be expressed online as different forms of blogs ties: blogroll links,
citation links, and comment links [1]. We observed the WRETCH blog com-
munities in terms of important SNA measures, such as indegree/outdegree,
closeness/betweenness, and k-cores, to interpret their social implications. The
basic idea is that blog article written by important blogger also becomes im-
portant itself so we can reinforce the semantic search service capabilities for
users to satisfy their interested from this perspective idea.
• Indegree and Outdegree: The higher indegree measure indicates the higher
spread of blogger (or article) influence in the blogsphere. The indegree
measures of the top 300 bloggers in the WRETCH BSP were shown as
power law distribution. Contrarily, outdegree measure did not indicate
any importance of a blogger in the community and its pattern did not
appear as power law distribution either.
• Closeness and Betweenness: The higher closeness (or betweenness) of a
blogger means it is in the social network center (or pivoting bridge) so
the spread of influences of this blogger is significant in the community. We
found that closeness (or betweenness) is similar to indegree but it incurs
high computation overhead so we avoid computing this measure in our
online information access.
• K-Cores: A k-core is a maximal subgroup in which each blogger (or ar-
ticle) has at least degree k within the subgroup. Thus k-cores measure
is effectively to demonstrate a particular subgroup cohesive relationship.
The common interests of a community derived from k-cores are important
for topic-specific semantic social web query services to discover similar
resources from this high cohesion level subgroup.
3.3 Semantic Social Web Query Services
We provide different level of semantic query services in our semantic social
web portal: basic semantic query services, advanced semantic query services,
and semantic social web query services:
• Basic semantic query services: The initial contribution of this article is to
combine the tagging system’s folksonomy with ontology to achieve basic
semantic query services. This service provides people or agents to effec-
tively access clustering of blog information through tags and related tags.
• Advanced semantic query services via ontology+tags: In this service, user
enables conceptual semantic query services with relevant tags. The con-
ceptual semantics can be defined as a channel declared from ontology with
relevant tags in the tags cloud. In other words, the search space for this
service is classified and focused so the search time is reduced and accuracy
is also improved.
• Semantic social web query services via SNA+ontology+tags: In a blog
ontology, we define properties to describe the relationships between blog-
gers, tags, and articles. Additionally, the important SNA measurement
attributes are also declared in a blog ontology. Therefore, we can leverage
the power of SNA measures from dynamically generated relations through
blogger’s daily activity events to enhance this service. We propose two
possible scenarios for this service that could justify our hypothesis 2 :
1. Scenario One: I would like to search authors and their blog articles
with “cuisine” tag paired with “restaurant” keyword in the associated
title or content of the article collected from the entire blog community.
Furthermore, please present these authors’ names and their associated
titles of article in a decreasing order of authors’ indegree measures:
prefix blog:
prefix rdf:
SELECT DISTINCT ?Author ?Article
WHERE
{?Article rdf:type blog:Article
?Article blog:has_articleTag blog:cuisine
?Article blog:has_author ?Person
?Person blog:person_ID ?Author
?Person blog:person_indegree ?Popularity
FILTER {regex(?TitleOfArticle, "restaurant") ||
regex(?ContentOfArticle, "restaurant"))}
}
ORDER BY DESC (?Popularity)
2. Scenario Two: I would like to search blogger names and their articles
from the cuisine channel for those of whom are known by authors pre-
sented in scenario one. Furthermore, please present these blogger names
and their associated titles of article in a decreasing order of authors’
indegree measures:
prefix blog:
prefix rdf:
SELECT DISTINCT ?Author ?Friend ?TitleOfFriendArticle
WHERE
{........
Codes Same As Scenario One
........
?Person blog:has_knows ?friend
?friend blog:person_ID ?Friend
2
The embedded codes are shown as SPARQL query language but users do not
require to have knowledge of SPARQL syntax in order to execute semantic social
web query services.
?FriendArticle blog:has_author ?friend
?FriendArticle blog:has_channel blog:CuisineChannel
?FriendArticle blog:article_title ?TitleOfFriendArticle
?FriendArticle blog:article_description ?ContentOfFriendArticle
FILTER {regex(?TitleOfFriendArticle, "restaurant") ||
regex(?ContentOfFriendArticle, "restaurant"))}
}
ORDER BY DESC (?Popularity)
Compared with Technorati 3 , it only provides limited independent search
services for user from his input blog posts, tags or directory where user can
not have semantic (social web) query services for any possible relevant outputs
using his previous search results. So user can not search the most influential
blogger friend’s articles or he can not search high similarity articles from those
bloggers with certain higher level of SNA indegree measures.
4 Semantic Social Web Blog Portal
In this research, a semantic social web blog portal was constructed to exploit
the incentives of bridging Web 2.0 ↔ Web 3.0 where users could enjoy semantic
social web query services on this portal. This portal structure is a layer schema
shown as Fig. 1. In the bottom layer, crawler collects semi-structured HTML
blog pages, structured RSS or FOAF context information, and free tags. Both
RSS 1.0 and FOAF ontology schema are based on RDF(S) so their semantics
are explicitly specified. Then, we extract and store the crawler’s collected
information in our local repository. In the ontology and tags annotation layer,
we mash up the blog ontology and the topic ontology with collected free tags
from social web annotation by folksonomy. The blog information diffusion
patterns will be analyzed by using SNA software Pajek to derive important
SNA measures, such as indegree, outdegree, closeness, betweenness, and k-
cores, etc[20]. Finally, we provide semantic social web query services for users
to satisfy his best interested.
4.1 Data Collection
WRETCH is the biggest BSP platform in Taiwan with more than 2 million
registered bloggers so huge amount of living and recreation information were
available for our experiment on the research issues of bridging of Web 2.0
↔ Web 3.0. After filtering out insignificant noise data, the number of useful
bloggers information samples in our analysis is around 108,518 bloggers. The
period of time for our data collection was one month spanned from Sep. 09
2006 to Oct. 09 2006.
3
http://technorati.com/
Fig. 1. A layer conceptual schema for construction a semantic social web blog portal
4.2 Data Analysis
In our mashup model, the free tags collected from users are usually 2-word or
3-word Chinese words (or characters) to annotate their daily real life’s living
activities. The scan and parsing processes of Chinese characters are different
from the English free tags. There are no spaces between Chinese characters so
we use regular expression to extract the meaningful high frequency 2-word or
3-word tags as our folksonomy final consensus social web annotations. With
no surprise, the distribution for the top 300 tags is shown as power law that
is similar to lots of other studies [12].
Initially the tags addressed by blogger in the WRETCH only imply that
the taxonomy of blog articles can be classified as one of 16 broad channel cat-
egories, such as living, cuisine, music, drama, travel, etc. When we carefully
examined the tags, we surprisingly found that those of significant 54,824 blog-
gers (approximate to 50% of 108518 bloggers) with their addressed 1046 tags
were converging to some of high frequent 521 2-word and 197 3-word tags.
And these tags were evenly distributed to our 16 broad channel categories.
This demonstrates that the social consensus opinions are possibly formulated
in terms of folksonomy tagging. We are expecting a more powerful folksonomy
annotation scheme can be realized in a near future as long as we have more
versatile ontology+tag structure.
4.3 Blog Ontology
The blog ontology describes the profile of a blogger with his blog articles
(see Fig. 2). The profile of a blogger is very similar to FOAF that defines a
blogger’s personal ID, friend relationship, and mbox, etc. The attributes of
each blog article include article title, date, feedback comment, and trackback,
etc. In addition, the SNA index measure is defined as one of a blogger’s pro-
file attributes. Therefore, SNA analysis capabilities were embedded into blog
ontology to serve our SNA+ontology+tag semantic social web query services.
Fig. 2. The blog ontology describes the profile of a blogger with his blog articles
The blog ontology is declared as OWL ontology language, where property
can be classified as two types: object property and datatype property. For
example, the domain and range of the has author object property are de-
clared respectively as Archives class and P ersons class, where Archives is
the superclass of both Articles and Categories classes. Based on this object
property, we describe the abstract relationships between a blogger and his blog
articles. The datatype property allows us to define a concrete XML-Schema
attributes, such as SNA index, for P rof iles subclass for further arithmetic
operations.
4.4 Topic Ontology
The blog articles in the WRETCH were classified into one of the 16 broad
topic channels based on their attachment tags. The design processes of broad
classification of blog article channel will be shown as three steps (see Fig. 3):
First, we subjectively declare 16 broad topic channel as instances under their
superclass Channel. The 16 broad topic channels are life, cuisine, music, etc,
where Channel and T ag are subclasses of Category superclass in the topic
ontology. Second, a set of possible tags we consider for each channel are those
with higher frequent 2-word or 3-word tags presented by users. Third, if a new
blog article has attachment tags that match at least one of higher frequent
tags in the set declared for one of a broad topic channels, then this new blog
article will be automatically classified to that channel.
4.5 Social Web Annotation
The goal of Web 1.0 annotation is to create a well-defined and computer un-
derstandable structure knowledge base e.g., ontologies, whose content mirrors
that of the WWW. The biggest challenge for bridging of Web 1.0 ↔ Web 3.0
is the terms mining from the Web can not be automatically and exactly fitted
into the ontology that defines the vocabularies for the target knowledge base
Fig. 3. Topic ontology - Channel and Tag are subclasses of Category so we can
automatically mash up ontology data and search model with the folksonomy tagging
system services
[7]. Therefore, most of the semi-automatic annotation systems usually apply
machine learning techniques to recognize new class instances and relation in-
stances mining from the Web. In the folksonomy annotation for bridging of
Web 2.0 ↔ Web 3.0, the granularity of class instances and relation instances
are restricted to the resource targets that can be clearly tagged by folksonomy.
The folksonomy of social web annotations are explicitly collected from tags
or implicitly initiated by users from their activity events. These explicit tags
and implicit events are precise terms that describe the instances and relations
corresponding to our ontology schema.
The objective and granularity of tags for describing instances and rela-
tions that corresponding to the target resources can be further refined if we
have more elaborate social web annotation system in the future. As seman-
tic wikipedia in [23], we might allow users to enable semantic tags similar to
typed links and attributes two kinds of property for describing corresponding
abstract relationship and concrete attributes within/between entity. Then var-
ious levels of reasoning for discovery of semantic relationship among taggers,
tags, and resources can be achieved.
Our semantic social web annotation system takes three inputs either col-
lected by web crawler or computed by local software agent. The first is HTML
blog pages with hyperlinks , comments, and trackbacks context. The second
is RSS context with permalink, publication data, author, and description at-
tributes. The third is tags, channel, and SNA indices computed via agents.
They are all stored in a local database and to be mashed up for afterward
semantic social web query services (see Fig. 4).
4.6 Semantic Social Web Blog Portal Testbed
An online semantic social web blog portal testbed (see Fig. 5) was constructed
based on previous layer conceptual schema (see Fig. 1) to experiment our
Fig. 4. Semantic social web annotation from three inputs of data sources for mashup
purpose to enable semantic social web services
mashup model. The crawler collects all of the necessary context information
from the WRETCH BSP. The context information shown in Figure 4 were
processed to create relevant class and relation instances defined in the blog
ontology and the topic ontology (see section 4.3 and section 4.4). This se-
mantic social web annotations for folksonomy were automatically generated
except in the bootstrapping stage where we have to analyze the blog site de-
pendent context to specify our initial lightweight ontology schema. A variety
of important SNA measures, such as indegree, closeness, betweenness, and
k-core, were computed via Pajek SNA software. 4 to provide semantic social
web query services shown in section 3.3.
Fig. 5. The semantic social web blog portal to experiment our Web 2.0 ↔ Web 3.0
bridging model
4
http://valado.fmf.uni-lj.si/pub/networks/pajek/
5 Conclusions
The goal of this research is to exploit the incentives of bridging Web 2.0 ↔
Web 3.0 via building a semantic social web blog portal. On the Web 2.0, we
usually use tagging system to label all kinds of Internet resources. Web 2.0 is
a folksonomy social web, where we effectively search what we are desirous of
information through tags. The tagging system enables the wisdom of crowds
and surprisingly social consensus can be derived from these voluminous and
unregular tags. Contrarily, Web 3.0 (semantic web) is aiming at using ontol-
ogy for effectively information search under taxonomy classification. We have
justified that the concepts of folksonomy and taxonomy can be mashed up to-
gether to achieve semantic social web query services via bridging of Web 2.0 ↔
Web 3.0. That allows us to leverage search capabilities from both bottom-up
folksonomy indexing and top-down taxonomy ontology two techniques.
Conceptually, tags in the tagging system are equivalent to terms mining
from the WWW in the conventional annotation system. The terms mining
from the Web are usually defined as instances that are related to a particu-
lar class or property in ontology. But tags from the folksonomy are usually
instances related to a particular class. Therefore, all of the relation instances
have to be created dynamically following the ontology schema. The relation
instances that describe the relationships between bloggers, tags, and blogs,
are generated from blogger’s daily activity events based on our blog ontology.
Although users can effectively search information by folksonomy tagging sys-
tem in Web 2.0, we still have the capacity to improve search capability via
social network analysis (SNA). A real SNA-based semantic social web query
services could possibly encourage users to find out what they are really inter-
ested in because well-organized topic-specific ranking contents are ready for
user to enjoy.
Acknowledgements
This research was partially supported by Taiwan National Science Council
(NSC), Under Grant No. NSC 95-2221-E-004-001-MY3.
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