In this paper, we propose an approach to make knowledge-based blogging more interesting, reliable and sophisticated by using semantic web with ontology in the blogging environment. The aim of our research is to realise the knowledge  management processes of identification, organisation and reuse (adaptation and application) via the blogging environment for them  to be available to users in a seamless application. To achieve knowledge identification, we employ natural language processing  techniques coupled with ontologies to identify possible categories of new blog posts. For knowledge organisation, we finalise the categorisation of blog posts and store them  into a repository. Finally, in knowledge reuse, we create semantic  links between blog posts and other related documents. These processes are integrated in a blogging framework which we call SEMblog

Cheah, Yu-N

Mohd. Kassim, Azleena

English

Repository@USM

Using Semantic Webo Ontologies and Blogs for Knowledge ldentification,Organisation and ReuseAzleena Mohd Kassimr., yu-N Cheahl' School of Computer Sciences, Universiti Sains Malaysia, 11800 USM Penang, MalaysiaIn this paper, we propose an approach to make knowledge-based blogging more interesting, reliable and sophisticated by usingsemantic web with ontology in the blogging environment. The aim of our research is to realise the knowledge rnanagementprocesses of identification, organisation and reuse (adaptation and application) via the blogging environment for thern to beavailable to users in a seamless application. To achieve knowledge identification, we employ natural language processingtechniques coupled with ontologies to identifu possible categories ofnew blog posts. For knowledge organisation, we flrnalise thecategorisation of blog posts and store thetn into a repository. Finally, in knowledge reuse, we create sernantic links betrveen blogposts and other related documents. These processes are integrated in a blogging framework which we call SEMblog.1. IntroductionKnowledge management has always been a key issue inmany organisations. As more and more organisations rely onhuman and intellectual capital to outperform their competitors,the challenge to manage knowledge effectively bicomesgreater, particularly in the efforts to identify usefulknowledge, organise them, and to reuse relevant knowledge._ Current knowledge management methods do not specifyhow the knowledge is to be handled after it is stored in thedatabase or repository. Knowledge is often dumped in aknowledge base or a repository, and later retrieved uponrequest. We do not have a set of procedures on how theknowledge is handled within the repository. There are alsoissues on how to represent knowledge in amachine-understandable form so that it will be easier to bereused over the web or within a centralised system. Semanticweb may be an answer to this issue but most semantic webdocuments that are available are not readable to human usersas they axe very rigorously formatted, usually in XML.Another problem that arises is that typical knowledge searchdoes not necessarity link to other pages that are relevant.To address some of these issues, in this paper, we presenta blogging framework called SEMblog to identify, organiseand reuse knowledge based using semantic web andontologies. The SEMblog framework brings togethertechnologies such as natural language processing (NLp) andthe intemet to produce a more intuitive way to manage$owlgdge, especially in the areas of knowtedgeidentification, organisation and reuse.2. Related WorksWarren (l) worked on issues pertaining to the use of thesemantic web for knowledge management and also therelevance of using ontologies in the semantic web. Warrendiscussed the building of a knowledge managementenvironment, which starts from a scenario, moving towardsutilising technologies for the pulpose. Semantic web is seenas a knowledge management scenario that works well as asearch mechanism, provided suitable technology is applied toit. He also stressed on the irnportance of making the semanticweb explicitly available and has a business process value.Stojanovic and Handschuh (2) built a framework usingthe existing tools in semantic web, which is expected to bringsemantic web to a level that can be realised by realJifeapplications. Both these works (l[2) have given usmotivation on finding a more practical semantic webapplication whilst still prioritising aspects of knowledgemanagement. We note the suitability and potential of theblogging environment for this pupose.Avesani et al. (3) worked on a topic-centric view of blogswith the Tagsoratic project, which makes use of the tags andcategory labels in the project to enable users to find posts thatare semantically connected, referring to the same topic beingsearched. We observed that the use oftags can help to achieveconnectivity through all the contents on the semanticweb-enabled structure.Karger and Quan (4) built a tool called Haystack that isreported to be successful and they also created anRDF-encoded blog to check on how RDF representationbenefits blogging activities. Haystack provides a robustsemantic blogging environment and manages to inter-linkblogs. From our perspective, we aim to go beyond creating asemantic blogging environment (where blogs that are relevantto each other are connected), but we are also going intoknowledge sharing via semantic blogs.Moller et al. (5) presented a protot)?e called semiBlogthat focuses on making blog authoring easier to computerusers. They used the data that are available on desktops(electronic address books, mp3 metadata, etc.) as semanticmetadata that would be imported into the blog. In ourSEMblog framwork, we aim to work with simple XML orRSS, but we find that we could add in more meaning to theannotation by having a tag for categories in the RSS module.3. Our SEMblog FrameworkThe development of our SEMblog framework consist ofthree main steps (see Fig. l): l. knowledge identification, 2.knowledge organisation, and 3. knowledge reuse.165t(nort*&slenirf€alh,rl('lo4ir*!Be!ilisalhn&rox*dgBRerseFig. l. Our SEMblog framework3.1 Step 1: Knowledge ldentificationIn this step, knowledge is acquired from blog posts. Thesemay be in the form of discussions, narrations, thoughts andideas. The acquired knowledge is then analysed using NLPand intelligent agent techniques, assisted by a domaiaontology. Through this analysis, candidate categories for theacquired knowledge are identified (see Fig. 2).Apolictin|.!lt{l TldlnicalladFig. 2. Knowledge ldentification and OrganisationThis step is realised in two distinct phases: 1.processing, and 2. ontology mapping.3.1.1 Text processingText processing is carried out after the SEMblog interfacereceives a (new) blog post from a user. For this purpose, asoftware agent is created. The objective ofthe text processingagent is two-fold.Firstly, it is to obtain a list of weights (based on wordstatistical distribution) for nouns and verbs in the blog post.This is done after runoving all stop words. The weightedscores are basically the percentage of the word's frequencyover the total number of words in the blog post.Secondly, it processes an OWL ontology (we haveadopted the OntoSem (6) ontology) to obtain only the classesand sub-classes. This in effect strips away all RDF tags andleaving behind a list of only the concepts and relatedconcepts.3.1.2 Ontology mappingBy mapping the weighted words to a particular ontologyconcept, we hope to identi$ a knowledge sategory for theblog post. This in turn achieves a form of knowledgeidentification.Ontology mapping is carried out by matching the list ofwords with thek weights with the list of ontology concepts(hierarchy of classes). When a match is found between a wordand an ontology class, we calculate the probability (Pw) ofthat class being a category (see Eq. l).Pwg:wxL (1)where C is the matched ontology class, w is the weight of thewor{ and I is the level of exploration. The value of I isinitialised to 2 for each matching cycle.The cycle continues by looking for the superclass (also aclass) of the eadier matched ontology class. If a superclass isfound, Pw6 is calculated for the matched superclass with thesame w of the word but with the value of I decreased by 0.5.The cycle oflooking for superclasses stops ifa superclass ofan earlier class cannot be found or when I :0, i.e. four levelsof ontology classes that is related to the word have been found.The cycle is repeated for other words (and their respectiveweights).After all &e weighted words have been matched with thelist of on0ology classes, it is possible for some ontologyclasses to have more than one Pw values. The final Pw valuesof these classes are simply the summed value of theirrespective Pw values.For our purpose, the higher the Pll value is for a particularontology class, the higb the chance for that class to h chosenas a category for a particular blog post. For this, tie ontologyclasses are listed in descending order ofPw values. These canbe considered as candidate categories. We then proceed withknowledge organisation to finalise the categorisation process.3.2 Step 2: Knowledge OrganisationAfter the candidate categories for the acquired knowledgehave been identifie4 knowledge organisation involvesidentifying relevant meAdata (most suitable categories)which semantically emiches the knowledge repository (seeFigure 2). These lead to the phase of storing of knowledge.SofhrrcJ{gflrt1663.2.1 Storing of knowledgeIn storing the knowledge (blog post), we have identifiedtwo approaches. The first approach is to use the n ontologyclasses (categories) with the highest pw values. We then storethese categories as annotated metadata to accompany the blogpost in the knowledge repository. The advantage of thisapproach is that we can make semantic linkages to thecategories with the highest pw values (the most viablecategories). The disadvantage is that other relevant categoriesthat are not ranked the highest may be missed. problems mayalso exist if the r-th and n+l-th categories tie with the samePw values.The second approach, which solves some of the issues ofthe first approach, would be to define a threshold value. Th.Tllre Pw values of the categories are first put through asigmoid function to obtain a normalised value (,g) for eachcategory's Pw value (see Eq. 2)..Sc: (2)D'jelItUchRSSugd/,€Aoolicdim Led Tednicd La;lFig. 3. Knowledge Reuse3.3.1 Semantic link creationWhen the metadata is identified for a new blog post, theseare actually the semantic link to other blog posts. So, when aparticular blog post is created (or retrieved, e.g. for viewingpurposes), the XML knowledge repository is parsed to obtainthe URL contained in the <1ink> tags of blog posts that havethe same categories as the earlier created blog post. TheseURLs are also made available with the retrieved blog post.This process is repeated if the user chooses to visit any ofthese related URLs.4. DiscussionWe have previously identified the use of a threshold valueto decide which candidate categories are chosen as metadatafor a particular blog post. The choice of the threshold value isnot arbitrary but is dependent on the length ofthe blog post.The realistic assumption here is that the longer the post, themore relevant words that can be found: and ttre shorter thepost, the less relevant words that can be found. So, for ourpurpose, we propose threshold values as shown in Table l.Table l. Blog post word count and respective thresholdvalues1 * "-(rwlFor our purposes, we have set a reasonably high thresholdvalue ofbetween 0.95 to 0.98 so that categories with Svalues> Th wll.l be chosen as metadata for the blog antry. Theadvantage of this second approach is that categories arechosen based on whether they exceed the threshold value asopposed to just selecting the top n categories. This way,categories do not compete with each other if they have thesame Pw value, rather they aim to exceed the thresholdinstead. The disadvantage is that some irrelevant categoriesmay somehow still be chosen.Before finalising the metadala, they are checked against apredefined list of categories that are deemed .,too general',and are not suitable to be considered as metadata. Forexample, these may be categories which are too high up theontology hierarchy. If these are found to be in the list ofmetadata, they are removed.The result from this phase is an XML entry in theknowledge repository with the relevant <tir1e>, <link>,<description>, and <category> tags. An example blogenbry is as follows:<posE ><post_j.d>2 <,/posC_id><post_date>20O6 -!2 - 07 </post_date><title>Natural disast.er in wikipedia</t,itle>< 1 ink >ht Ep : / / s ei'.]rh]-og / tts er I / post / 2. txt < / 1 ink><description>A naEural disaEter is the consequenceof the conbination of a natural hazard (iphysical. event e.g. volcanic erupt.ion,earthquake, landslide) and human activit.ies.Hunan vulnerability, caused by thelack<,/descriPt ion><cat.egory catL=r'humanil cat2=iprimatertcat3 = " disaster- event " cat4 =-" natur aI -]nazatd,il / ></post>3.3 Step 3: Knowledge ReuseKnowledge reuse is carried out by forming semanticassociations. This ensures that blog posts that are related to aparticular blog post are effectively made available to blogreaders via these semantic links (see Fig. 3).DbpfeyRalu{Ittt:Jls€srtbcc,\r7Tag6edfrufilprgv'$iJs Ec$oal<Caryry>URl,ne*stle<200 0.98>200 and <500 0.96> 500 0.9s167Attributes (|ur SEMlrlos Ilavsrack (4) semlBlos (5) .:fi Technoreff(n lKnowl,edgeBasedThe main objective(multiple flow) Not definedInformation flow fromsource to repository Not definedRclationshipSemantic links viacategorisationRelationship basedexploration - semanticlinks relationshipsDoes not specify anyrelationshipTrack links to make outrelevance ofblogs.OntologrNavigate the ontology(OWL) and perform acalculation on metadatauo the tree structureAllow additionbootstrap files andcontent-loading throughthe RDF storeNot defined Not definedSemanticAlsoiiadolsXML/RSS format ofdata repository toenable semantic linksXML/RDF and RSSformat storing andaccessUses RDF to representsemanticsNot definedAutomaticcategorisationUser-definedcategorisation - Userscreate topic andcategorise their contentNot definedUser-definedcategorisation usingtagsIntegrate knowledgeidentification,orsanisation and reuseFocuses on theHaystack Client serverapplications.Integration betweendesktop application andthe web.Integrated approach ofblogs and forumFrom a storage point of view, our semantic knowledgerepository uses RSS tags like <title>, <1ink> and<description>. We would like to highlight that byincluding SEMblog-related tags like <post-id>n<post_datet and <category>, we have enriched the basicRSS tags and this facilitates the search for related web pagesbased on the categories. We have also chosen XML for ourknowledge repository and metadata as it is portable andplatform independent.Presentb, we have not performed any quantitativeanalysis of the results obtained due to the fact that kaowledgecaptured via blogs is by itself qualitative. However, we wereable to make some qualitative comparisons with other relatedwork in this area of semantic blogging. These are summarisedin Table 2.5. ConclusionThe key idea of our research is to find a way to manipulateknowledge, particularly on its identification, organisation andreuse, all in a seamless environment. Thus, we have createdan architecture based on blogging to achieve this. We havealso adapted web technologies such as semantic web, as wellas ontology and NLP techniques. By doing so, we havemanaged to incorporate semantic web, ontology and NLP intoa more user-*iendly and practical application.On the whole, we can see that we have multiple flows ofknowledge, triggered by the single process of writing a blogpost. When a user blogs, we identify the knowledge, decideon the categorisation in order to organise the knowledge, andmake it retrievable or reusable. At the same time, previousknowledge (blog posts) that has been stored is made availableto the user by providing links to web pages that aresemantically related to the current blog post.In the future, this systern can be improved by focusing onNLP techniques, especially on language, grammar andTable 2. Qualitative evaluation of related projectsvocabulary issues. Knowledge involves underrtanding and inorder to make the machine understaad the knowledge beforeconveying it to the users, we can use techniques like NLP ormachine translations as an intermediary. Another possibleprospect is adapting this research concept into organisationalknowledge management. With this, important knowledgeflowing in and out of the organisation through fiansactions orbusiness processes can be identified, organised and reusedwithin the organisation.In conclusion, we have achieved our objective to providea framework where users are able to create and accessknowledge whilst actively identifying, organising, andreusing knowledge.6. References(1) P. Warren: Knowledge Management and the Sernantic Wcb: FromScenario to Technology. IEEE Intelligent Systems, Vol.2l, pp.53-59(2006)(2) N. Stojanovic and S. Handschuhs: A Framework for KnowledgeManagement on the Sernantic \,/eb, The Eleventh International WorldWide Web Conference,llonolulu, Hawaii, USA (2002)(3) P. Avesani, M. Cova, C. Hayes and P. Massa: Leaming ContextualisedWeblog Topics. In E. Adar, N. Glance and M. Hunt (eds), Proceedingsof 1fi44f 2005 2nd Annual llorkshop on the Webloging Ecosystem:Aggregation, Analysis and Dyttanics. (2405)(4) D. R. Karger and D. Quan: What would it mean to blog on the senanticweb? In S.A. Mcllraith, D. Plexousakis and F. van Harmelen (eds)"Third International Semantic Web Conference (ISWC'04), Hiroshima,lapm, Yot. 3298 of Lecture Notes in Computer Sctuzce, Springer (20O4)(5) K. M6ller, J. Breslin and Stefan Deckec semiBlog - SenranticPublishing of Desklop Dat4 Proceedings of the l4th Conference onInformation Systems Development (15D2005), Kaibad Sweden (2005){6) Ontosem OWL. Available at: http://morpheus.cs.umbc.edu/aksl/ontosern.owl(7) Technorati, lnc. Available at: http://technorati.com/abouV168

A Framework for Knowledge Management on the Sernantic \,/eb, The Eleventh International World Wide Web Conference,llonolulu,

Deckec semiBlog - Senrantic Publishing of Desklop

Knowledge Management and the Sernantic Wcb: From Scenario to Technology.

Leaming Contextualised Weblog Topics. In

What would it mean to blog on the senantic web? In

Using Semantic Web, Ontologies and Blogs for Knowledge Identification, Organisation and Reuse.

http://eprints.usm.my/9680/1/Using_Semantic_Web_Ontologies_and_Blogs_for_Knowledge_Identification_Organisation_and_Reuse.pdf

Using Semantic Web, Ontologies and Blogs for Knowledge Identification, Organisation and Reuse.

Abstract

Similar works

Full text

Available Versions

Repository@USM