1

OntoBlog: Informal Knowledge Management by
Semantic Blogging
Aman Shakya, Vilas Wuwongse, Hideaki Takeda and Ikki Ohmukai

Abstract—Blogs collect abundant information by providing
easy and dynamic publishing interface. Semantic blogging
attempts to provide structure to blog entries by enriching themwith metadata. However, currently semantic blogging does notlink up related items. So it is still difficult to navigate, retrieve andorganize related blog entries. OntoBlog is a prototype semantic
blogging system which attempts to facilitate informal knowledge
management in blogs by interlinking related blog entries. It
combines blogging and knowledge base technologies. This is doneby semi-automatic semantic annotation of blog entries withontology instances. Blog entries are automatically linked torelated instances using language processing. The rich structure of
ontology can enable useful semantic capabilities for knowledge
management. Semantic navigation allows users to navigate
through each blog entry to semantically related blog entries.
Semantic search can be employed in blogs. Semantic aggregationcollects blog entries relevant to the topic of interest and organizes
them meaningfully. A prototype for computer department
domain ontology has been implemented.
Index Terms— Blogging, informal knowledge management,
ontology, semantic annotationB

meaningfully. Semantic blogging should not only be aboutpublishing structured data but also linking related contents
together based on semantics. To manage the abundant
knowledge collected through blogging it is important tointerlink related pieces of information and organize them.
In this paper we propose OntoBlog 1 , a new semantic
blogging prototype for informal knowledge management of
blog contents. It links blog entries to an existing ontology andinstances using semi-automatic semantic annotation. Semantic
annotation helps in retrieving relevant resources; categorizing
and organizing contents and navigating meaningfully [11], [13],[20]. OntoBlog attempts to provide an integrated platform tofacilitate publication, semantic annotation and information
utilization in blogs.
In Section II we discuss about knowledge management inblogs. A motivating example scenario is described in Section
III. In Section IV we discuss about the implementation of thesystem using semantic annotation in blogs. We propose
OntoBlog as an integrated environment. Some semantic
services offered by the system are discussed in Section V.
Section VI describes some related works. Finally, we conclude
and point out some future works in Section VII.

I. INTRODUCTION

logs have made publishing information on the Webvery easy. Blogging facilitates rapid publishing andresults in abundant collection of information. Blogs harness thecollective knowledge of large number of users. They serve as
dynamic media showing the latest posted information at the top.
However, apart from the reverse chronological order blog
entries do not have much structure and organization. Hence,
filtering, organizing and navigating blog entries are difficult in
traditional blogging.
Semantic blogging [4], [5] provides well defined structure
to blog entries in the form of metadata. It combines thedesirable features of both blogging and the Semantic Web.
Currently semantic blogging provides semantic structure toindividual entries. However, these pieces of information are notinterlinked with semantic relations. Thus, it is still difficult tonavigate through related entries and organize contents
Manuscript received November 15, 2007.
A. Shakya, H. Takeda and I. Ohmukai are with the National Institute ofInformatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan (e-mail:shakya_aman@nii.ac.jp, takeda@nii.ac.jp, i2k@nii.ac.jp).
V. Wuwongse is with the Asian Institute of Technology, Klong Luang,
Pathumthani 12120, Thailand (e-mail: vw@cs.ait.ac.th).

II. KNOWLEDGE MANAGEMENT IN BLOGS
The Wikipedia definition says that “Knowledge
Management (KM) comprises a range of practices used byorganizations to identify, create, represent, and distributeknowledge for reuse, awareness and learning”. In fact, KM is avast area including many aspects. We only consider creating
and sharing pieces of information informally and annotatingthem with semantic representations so that they can beorganized and retrieved effectively. Though we only consider
limited aspects of KM, the idea would be applicable and useful
to many domains and organizations in general.
Blogging provides an easy platform for creating and sharing
information snippets. Blogs can effectively capture informal
knowledge from several users and cater to the entire
community. Conventional database driven information systems
are rigid and do not cover all types of information that people
may want to share within an organization or community.
Informal snippets in blogs can cover a wide variety of
information. Cayzer [4], [5] discusses elaborately why blogs
1

An online demo can be found at
http://dutar.ex.nii.ac.jp/ontoblog/blog/default/

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are suitable for managing information snippets.
However, blog entries are not semantically interlinked toeach other making it difficult to navigate, organize and retrieve
contents. We can try to address this issue by complementing theunstructured blog contents with well structured ontology andinstances, i.e. a knowledge base. The semantic relations in the
ontology can be used to interlink related blog entries.
Knowledge bases can be maintained well by organizations
using available tools and methodologies. However, direct useof such knowledge bases is difficult for non-technical users. So
a lot of high quality information remains locked in suchknowledge bases. Coupling knowledge base with informal
techniques like blogging can expose such valuable data for
useful applications. Thus, these technologies can complement
each other and transform blogs into informal knowledge
management platforms. This can be done by semantic
annotation of blog entries as detailed in Section IV.

relevant blog entries and organize them presenting a graphicaltable of contents.

IV. IMPLEMENTATION
To implement the scenario described above we propose
linking blog entries to ontology and instances, as illustrated inFig. 2. Blog entries are unstructured and scattered without
explicit links among them. On the other hand, an ontology isvery well structured with semantic links. Blog entries can belinked to ontology using Semantic Annotation, i.e., annotationthat references an ontology. Semantic annotation can enhance
information retrieval and improve interoperability [22]. The
deep structure of the ontology can be leveraged to link up
related blog entries and organize them for informal knowledge
management.
A. Semantic Annotation in Blogs

III. EXAMPLE SCENARIO

Fig. 2. Linking blogs and ontology by semantic annotation.

Fig. 1. Example scenario.

As an example domain, we consider the case of a computer
department of a university. The department maintains an
ontology with concepts like course, topic, teacher, research, etc.
The ontology is maintained by the ontology engineer oradministrator using available ontology management software.
The knowledge base is populated with instances. The
department also maintains a community blog as illustrated inFig. 1. Registered users can easily publish entries on the blog.
When publishing or updating a blog entry, the system
automatically suggests instances related to the blog entry. The
user may accept the suggestions or modify some choices asappropriate. If a related instance or concept is not shown by thesystem, the user may type in appropriate suggestion for a new
instance and/or concept. The list of suggestions posted byseveral users can be viewed by the administrator and he/she canmake appropriate additions or improvements to the ontology.
The users can access the blog entries effectively with the helpof semantic capabilities provided. When a blog entry is viewed,
semantic navigation links are shown as related links. Search
results are augmented by semantic search. Further, if the user isinterested in some topic, semantic aggregation can gather

Blogs are somewhat different from other web information
sources. Blog entries are self-contained snippets [4] of
information or small contents [19]. We may consider a blog
entry as a single discrete unit of information. Thus, annotations
can be applied to the blog entry as a whole. Blogs facilitate easy
and dynamic publishing. Therefore, there are someconsiderations for the application of semantic annotation toblogs.
Integrated authoring environment. Annotation would also
become easy if we can integrate it with the dynamic publishingplatform of blogs. Such an integrated environment has been
pointed out as a requirement for semantic annotation systems
by Uren et al. [22].
Automation. With manual annotation, the user has theburden of creating annotations. Providing useful annotation
may also depend on the willingness of stakeholders [7].
Automatic or semi-automatic annotation with pre-existing
information can help in solving these issues and makes theprocess of annotation fast and easy for the blogger.
Integrated services. As blog entries are scattered in the formof small discrete entries, it becomes essential to provide some
services to relate these pieces of information together andpresent them to the user as an organized collection.

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B. The OntoBlog Integrated Platform
We propose OntoBlog as an integrated authoring platform
introducing the following features into blogging.
Semantic annotation. We use existing ontologies andinstances to semantically annotate blog entries.
Integrated authoring. Semantic annotation is integrated
with the authoring environment of the blog which helps theauthor to annotate entries easily at the time of blogging.
Semi-automatic annotation. The system automaticallydiscovers related instances when blog entries are added andprovides suggestions to the author.
Feedback for ontology maintenance. The users may
suggest new concepts and instances if the system does notcontain appropriate ones related to the blog entry.
Integrated services. The system demonstrates how semantic
capabilities may be incorporated to utilize blog contents, bysome example services.
A prototype implementation, similar to the example scenario,
has been done considering the domain of the Computer Scienceand Information Management department of the Asian Instituteof Technology, Thailand.

C. Publishing Metadata
Besides normal text contents, blog entries may also containmetadata as shown in Fig. 4. In our test installation we usedsome publication types from the SWRC ontology(http://ontoware.org/projects/swrc/). Metadata is stored in the
RDF metadata store as described in another work by us [21].
This provides a scalable storage unlike using a single RDF filefor all metadata (as in [5], [4]). Jena has been used to manage
operations on the RDF metadata store. Blog entries andmetadata can be entered by several users thus harnessing thecollective. Further, publishing metadata in RSS feeds makes
way for aggregation of information from multiple blogs.

Fig. 4. OntoBlog interface.

D. Ontology and Inference

Fig. 3. System architecture.

The system architecture is shown in Fig. 3. The system isbuilt upon a blogging infrastructure backed up by an RDF
metadata store. The Blojsom blogging platform(http://blojsom.sourceforge.net/) has been used. Users post
blog entries, optionally containing some metadata. The system
has a text search engine to index blog entries. Blog entries arelinked to related ontology instances by the blog-ontology
linking component. The component automatically suggests
related instances for blog entries and saves the selected
annotations in the metadata store. The inference engine candeduce implicit relations between instances. Search results areaugmented with related blog entries by finding linked related
instances from the ontology. Related blog entries are finallyorganized into a navigable collection by the organizer based on
the structure of the ontology. The system also exports metadata
in extended RSS format.

Fig. 5. A part of a computer department ontology.

The ontology may contain various concepts from the domain
and a wide variety of relations, not just a topic hierarchy. A
simple example ontology of a computer science department
was constructed for testing the system. It was created using the
SHOEComputer
Department
Ontology(http://www.cs.umd.edu/projects/plus/SHOE/onts/cs1.1.html).
However, only few parts of the ontology have been used
including concepts and relations depicted in Fig. 5.
Instances of the ontology are populated in the knowledge
base. We can expect the availability of suitable knowledge
bases [13], [20]. A knowledge base can easily be shared bymultiple distributed blogs. Protégé has been used for managing
the knowledge base.

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The OWL Micro reasoner from Jena has been used for
inference. We may have some axioms for inference like “for_course and has_topic are inverse relations”,“is_broader_than and is_narrower_than are inverse relations”,“teachesandtaught_byareinverserelations”,“has_prerequisite and is_broader_than are transitive”, etc.
E. Blog-Ontology Linking
The system links blogging to an existing ontology system bysemi-automatic semantic annotation. Annotation can be
automated by language processing of the blog entries.
Language processing may not be as sophisticated as
information extraction and wrapper mechanisms. However,
simple lexical analysis can be very fast and quite effective [7].
The Porter stemming algorithm has been used (as in [5], [4])which is a widely used technique in information retrieval. The
system provides automatic suggestions to the user forannotation. The user can easily modify the options if somesuggestions are not appropriate. Automatic annotation cannot
be perfect even with other available sophisticated techniques.
Moreover, relevance is a subjective matter and not possible for
perfect automatic judgment. Providing suggestions to the user
keeps the system flexible instead of making it totally automaticand rigid.

Fig. 6. Example Blog-ontology linking.

An additional “keywords” element has been added to eachconcept in the ontology. For each instance, the “keywords”
element contains a collection of words related to that instance.
Whenever an entry is added or updated, the “keywords” foreach instance are stemmed and matched against the stemmed
blog entry. If any of the “keywords” is found in the stemmed
text, the blog entry is considered as related to that instance. The
discovered relations are encoded in the related-to property, as
shown in Fig. 6. In the example, the blog entry with permalink“RMI.html” has been linked to “Java_Technologies” and“Remote_Method_Invocation”.
F. Feedback for Ontology MaintenanceIn case some related instances or concepts are not defined in
the ontology, users may suggest a suitable instance or concept.
The system provides a web-based form for new suggestions

along with the automatic suggestions described in the previous
section. The user may enter the name for the new instance,
select the appropriate concept (or suggest a new concept) andpost some remarks. The list of suggestions posted by various
users can be accessed by the administrator on the blogging
system itself. The feedback thus collected is useful for theadministrator to maintain the ontology up-to-date by adding
missing concepts and instances.
V. SEMANTIC SERVICES FOR KNOWLEDGE MANAGEMENT

Fig. 7. Example Blog-ontology linking.

Semantic annotation of blog entries allows us to relatedifferent blog entries using the structure of the ontology as
illustrated in Fig. 7. In the figure, instances (I1-I7) in the
ontology are represented by different shapes, each shape
representing a concept. Instances are connected to each otherby different relations (indicated by the solid arrows). Linking
blog entries to ontology serves to link related blog entries
implicitly. Blog entries (A to F) are annotated by the ontology
by linking them to the instances, as shown by the dash-dottedlines. Blog entries ‘A’ and ‘B’ are related to each other because
they are both mapped to the same instance ‘I1’. Instance ‘I1’ isrelated to ‘I2’. Hence, blog entry ‘A’ is indirectly related to ‘C’,
which has been mapped to ‘I2’. Instances may also be linked by
implicit relations (shown by dashed arrow) that can be
discovered by inference. Instance ‘I4’ is related to ‘I6’ by aninferred link. Thus, blog entry ‘D’ (mapped to ‘I4’) is related to‘E’ (mapped to ‘I6’).
A. Semantic navigation
Semantic navigation helps the user to browse through related
blog entries. For example, suppose we view a blog entry B
about “Database Programming”. The blog entry may beconnected to {“computer programming”, “databases”,“software development”, “Prof. Takeda”....}. “Computer
programming” may be involved in the relations {“is taught by”,
“has prerequisite”,..... }. Thus, there may be links like[computer programming]– is taught by – [Prof. Takeda]– has prerequisite – [databases],etc.
Clicking on [databases] will lead to the blog entries related todatabases. When a blog entry is opened, the semantic

5navigation links are shown in a collapsible “Related to” block(shown in Fig. 8).

GraphML has been used to represent the semantic
aggregation graphs. Semantic aggregation is depicted in Fig. 9.
The user runs semantic aggregation by searching on a topic ofinterest. The search results are listed on the right-hand side
frame. Related instances from the ontology are aggregated andvisualized on the left-hand side frame. The relation type
between instances is identified by the color of the link andshown in an index. When a node is clicked, blog entries related
to that node are displayed on the right-hand side.
VI. RELATED WORK

Fig. 8. Semantic navigation.

B. Search
The system provides indexed text search and metadata
search. These are further augmented by semantic search. Guhaet al. [9] have presented extensive research on semantic searchalong with sophisticated implementation. We do not intend toreproduce or extend the research on semantic search. Rather
OntoBlog just provides a simple demonstration of its
applicability. Semantic search may be particularly useful whentext search alone does not produce enough results. Semantic
search can be enabled or disabled in configuration settings.
Further, the depth of semantic search can also be controlled.
C. Semantic Aggregation
Semantic aggregation can be introduced in the system tocollect and organize search results relevant to a topic of interest.
A simple algorithm is outlined below.
1. Get all relevant blog entries from search.
2. Find the set of instances S from the Ontology linked toeach blog entry.
3. Find all relations between the instances in S
4. Visualize the related instances as directed graphs

Fig. 9. Semantic aggregation.

A number of works have been done in semantic blogging.
The Semantic Blogging Demonstrator uses a category tree
based on ‘broader than/narrower than’ relations [5], [4] tocategorize blog entries. OntoBlog uses an ontology with wider
variety of concepts and relations, rather than a simple
taxonomy, and enables inferences. It also allows utilizing richknowledge base of instances maintained using existing
ontology management environments. The demonstrator
emphasizes semantic view, navigation and query. However,tree browsing provided for semantic navigation is already afeature of many blogs [3]. Facet browsing is more like metadata
search. OntoBlog offers a more intuitive way of semantic
navigation by providing related links through each blog entry.
Karger and Quan [12] extended Haystack to enable users toview cross-blog reply graphs and track conversation in multiple
blogs. Semblog [19] annotates content using FOAF metadata.
SocioBiblog [21] facilitates sharing of bibliographic
information in a social network based on extended RSS. Moller
et al. [16] identify structural and content-related metadata in
blogging - SIOC ontology [2] for structural metadata and
FOAF, vCard, BibTex/SWRC, iCalendar, etc. for content
metadata. In our case, the commenting mechanism generates
some structural metadata. Though SWRC has been used as an
example, any content metadata can be used. In addition, thesemantic annotation metadata provides categorization metadata.
semiBlog [16], [17], [18] utilizes data on the user’s desktop.
But we still have to search metadata in desktop. More abundant
metadata, of better quality, may be available in existingknowledge bases.
Uren et al. [22] present a detailed survey of semantic
annotation tools and analyze them on the basis of a number ofrequirements. A large body of research on semi-automaticsemantic annotation already exists including works like
S-CREAM [10] and extraction ontologies [6]. Our attempt is todemonstrate the application of semantic annotation in blogs,
not to build a sophisticated annotation system. However,
OntoBlog satisfies some requirements [22] like automation,
integrated environment, document-annotation consistency andseparate annotation storage. Magpie [7], [8] automatically links
instances identified in web documents to relevant ontologicalinstance/class. It uses simple lexicon-based parsing andlinguistic rules to identify instances. Magpie depends on
external agents for semantic services. KIM [13], [20] uses anontology a pre-populated with instances. It introduces

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information retrieval based on IE techniques for the recognitionof named entities.

VII. CONCLUSION AND FUTURE WORKIn this paper attempted to show the possibility of informal
knowledge management with blogs for utilizing the abundant
information that can be gathered from the community. We
proposed OntoBlog which demonstrates a new method forsemantic blogging – integrating blogs with an existingknowledge base through semantic annotation. The semantic
relations in the ontology can be exploited to turn blogs into a
web of interrelated contents. We can effectively retrieve andorganize contents when pieces of information are interlinked toeach other through semantic relations.
We can explore mechanisms for the decentralized creation of
the ontology in future. Collaborative techniques likefolksonomy or semantic wiki could be utilized. The system iscurrently a community blog. Features like semantic navigationand search are yet to be employed across multiple blogs. The
system can be made more powerful by introducing different
types of inferences. Mature semantic search systems can beincorporated. Information retrieval mechanisms can be used to
produce ranked search results. Language processing withstemming is quite basic. It can be made more accurate byhandling lexical, semantical and syntactical variations [1].
Lexical variations can be dealt with technologies like
Word-Net. Sophisticated automatic annotation using IE
techniques can be incorporated to make the automation morerobust.
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