research article hybrid ontology for semantic information...

Research ArticleHybrid Ontology for Semantic Information Retrieval ModelUsing Keyword Matching Indexing System

K R Uthayan1 and G S Anandha Mala2

1Department of Information Technology SSN College of Engineering Chennai 603110 India2Department of Computer Science Engineering Easwari Engineering College Chennai 600089 India

Correspondence should be addressed to K R Uthayan uthayankryahoocom

Received 5 September 2014 Accepted 20 October 2014

Academic Editor Zheng Xu

Copyright copy 2015 K R Uthayan and G S Anandha Mala This is an open access article distributed under the Creative CommonsAttribution License which permits unrestricted use distribution and reproduction in any medium provided the original work isproperly cited

Ontology is the process of growth and elucidation of concepts of an information domain being common for a group of usersEstablishing ontology into information retrieval is a normal method to develop searching effects of relevant information usersrequire Keywords matching process with historical or information domain is significant in recent calculations for assisting the bestmatch for specific input queriesThis research presents a better querying mechanism for information retrieval which integrates theontology queries with keyword search The ontology-based query is changed into a primary order to predicate logic uncertaintywhich is used for routing the query to the appropriate servers Matching algorithms characterize warm area of researches incomputer science and artificial intelligence In text matching it is more dependable to study semantics model and query forconditions of semantic matching This research develops the semantic matching results between input queries and informationin ontology field The contributed algorithm is a hybrid method that is based on matching extracted instances from the queriesand information field The queries and information domain is focused on semantic matching to discover the best match and toprogress the executive process In conclusion the hybrid ontology in semantic web is sufficient to retrieve the documents whencompared to standard ontology

1 Introduction

The difficulty of information storage space and retrievalhas concerned escalating special treatment since 1940 Thedifficulty affirms that huge quantities of information to bestored and the relevant information should be precise Anenormous contract of research work has been completedto offer speedy and intellectual retrieval methods To theresearch concern of digital libraries several indeed containinformation storage and retrieval troubles such as loggingand textual penetrating Conversely the difficulty of suc-cessful repossession continues mostly vague Civilizing theusefulness is a significant ambition for the research of infor-mation retrieval system Identifying the concepts or effortof the user is the major complicated obsession for relevantdocuments searching from a huge amount of informationFor the user using common terms of queries for searchingan information retrieval system will not provide functional

and detailed answersThe domain information of documentsand cognition of the user are thus major for the retrieval ofrelevant documents information

The research on combining the methods of ontologyand information retrieval for semantic web is emerging inrecent times To explore the relevant information for theusers need a conventionalmethod is introduced by entrench-ing ontology into information retrieval If the investigatedinformation is enclosed beneath the knowledge domain ofuserrsquos concepts the motivation increases the probability ofrelevance Therefore the efficiency possibly enhanced Thechallenges of implanting domain knowledge into informationretrieval system are as follows

(1) What is the apposite information retrieval model

(2) How to execute and build ontology

(3) How to discover the relevant documents by ontology

Hindawi Publishing Corporatione Scientific World JournalVolume 2015 Article ID 414910 9 pageshttpdxdoiorg1011552015414910

2 The Scientific World Journal

The semantic web is build for current web extensionwhere the information has well defined meaning and ena-bling cooperation between people and computers Becauseof this well-defined structure humans and even machineswill work in coperation The standard fuzzy ontology is atechnique which is used in information retrieval where thecalculation of relationship among the concepts are done usingmembership values From domainrsquos uncertainty data gen-eration of fuzzy ontology automatically is highly desirableThis research explores hybrid fuzzy ontology-based informa-tion retrieval models in semantic web and gossip about theachievement and authority of applying proposed ontologycontaining common field knowledge and fuzzy concepts fab-ricated from the stored documents automatically For map-ping the generated fuzzy ontology to semantic representationWeb Ontology Language (OWL) is used

This research work is organized as follows The relatedwork is reviewed in Section 2 The proposed ontology-basedinformation retrieval model is depicted in Section 3 Theexperiments and discussion on the results are described inSection 4 Finally conclusion is given in Section 5

2 Related Materials

Tho et al [1] proposed the FOGA (Fuzzy Ontolology Gener-ation frAmework) in which fuzzy ontology is generated onvague information automatically A fuzzy-based method isdescribed for integrating database attributes to the ontologyThey converse about approximating reasoning for additionalenhancement of the ontology de Maio et al [2] describedan approach by analyzing the web resource collection forautomatic fuzzy ontology elicitation This approach applica-bility is validated by web domain case study Abulaish et al[3] recommended a fuzzy ontology generation framework inwhich instead of concept descriptor the possession quantityis encoded using fuzzy membership function The FuzzyFormal Concept Analysis (FFCA) which is generalization ofFormal Concept Analysis (FCA) used for sculpting vaguenessinformation Formica [4] showed the FFCA amalgamationwith rough set theory to complete semantic web explorationand detection of information in the web Chahal et al[5] presented a similarity comparison scheme of semanticweb document which relies instances between keywords indocuments and also the relationship in the web pages whichexists between concepts amalgamation

Formica [6] proposed a similarity measure for FFCAThis FFCA is usually intended for restricted audience andaddressed at technical level although it becomes very inter-esting for semantic web development by supporting differentactivities The development of ontologies manually is a timeconsuming and cumbersome task Zhang et al [7] plannedan approach and an automated tool from Fuzzy ObjectOriented Database (FOOD) models for constructing thefuzzy ontologies This ontology plays an important role forthe development of new strategies of knowledge based sys-tems and in supporting the automated process for accessing

information So de Maio et al [8] presented an ontology-based retrieval approach which supports data organizationand visualization and provides a friendly navigation model

To design information retrieval system the major chal-lenges for researchers and developers is themethod of sharingand searching the information with emergence of webKohli and Gupta [9] surveyed the challenges in informationretrieval and solve those challenges with the help of fuzzyconcept Aloui et al [10] have presented a semiautomaticmethod for fuzzy ontology extraction and design (FOD)Themethod is based on conceptual clustering fuzzy logic andformal concept analysis (FCA) The core of ontology is rep-resented as a set of fuzzy rules To validate the proposed ap-proach they used Protege 43 that supports the fuzzy conceptand automatically generate the script in OWL-2 language

Sometimes irrelevant information is retrieved on thesemantic web but it is meaningful and with ontology map-ping the relevance can be improved Kandpal et al [11]described a new technique for ontology mapping Two var-ious ontologies of a domain are considered and the conceptswhich are similar to each other are retrieved that is ontologyalignment The similarity is calculated if the concepts are notmatched even when term is expanded One of the challengesin information retrieval is providing accurate answers to auserrsquos question often expressed as uncertainty words Rani etal [12] presented a hybrid approach for a semantic questionanswering retrieval system using ontology similarity andfuzzy logic to retrieve collection of documents Fuzzy scaleuses fuzzy type 1 for documents and fuzzy type 2 for words toprioritize answers

Recently the data originated frommultiple types of sour-ces includes the mobile devices individual archives sensorssocial networks enterprises and cameras Internet of thingssoftware logs and health data have led to one of the mostchallenging research concerns of the big data era So Xuet al [13] suggested the basic blocks of the Knowles systemresources representation semantic relations mining andsemantic linking news events and it does not need datacontributors to pursue semantic standards such as RDF orOWL which is a semantics loaded self-categorized network

Liu et al [14] proposed a technique in effective mannerfor organizing the associated multimedia resources and forsemantic link network model which is used for organizingmultimedia network The community cloud computing is apromising and emerging model for a particular communitywith general concerns such as compliance security andjurisdiction Selecting the best group of community cloudsthat are the most economy and communication effective andtrusted to complete a difficult task is extremely challeng-ing To deal with this problem Hao et al [15] formulatecomputationalmodelmulti-community-cloud collaborationnamely MG3 The proposed model is then optimized fromfour aspects minimize the sum of monetary and access costmake the most of security level agreement and trust amongthe community clouds

So the study of related works motivates the semanticmatching technique by combining the fuzzy ontology withkeyword matching to retrieve the relevant information

The Scientific World Journal 3

QueryDocuments

Query preprocessing

Keyword matching method

Ontology selection and mapping

Similarity measure

Relevant documents

Ontology construction model

Term similarity processing

Document analysis

Hybrid ontology construction

WordNet

Domain knowledge

02

Imported ontology using NLP technique

Domain knowledge

01 Document annotation

List of annotation documents

Query processingDocument processing

Figure 1 Hybrid ontology for information retrieval

3 Research Methodology

The hybrid ontology approach to query interpretation is onthe aspiration of generating more than one specific plannedquery from a given keyword This research refers to everyproduced query as an elucidation The proposed model usesa hybrid fuzzy ontology for semantic relevant documentretrieval It semantically repossesses a position of relateddocuments along with users query esteeming the emphasizedsector or domain It can be used to retrieve every cate-gory of documents in a particular domain written in alllanguages The proposed information retrieval models andtheir major components are a set of annotated documentsuserrsquos queries retrieval engine and ranking module Therelationships between concepts are built using ontologyterms and NLP techniques The relationships and natural-language synonyms represents the entities which completesthe ontology by considering the key technique of NLP

As demonstrated in Figure 1 the proposed hybrid ontol-ogy-based information retrievalmodel encloses the followingmodules

(i) Query Preprocessing Query preprocessing is a necessarystep for extracting terms and aspectsThe important functionof this section is to eliminate the insignificant words and filterthe major keywords

(ii) Ontology Construction Methods This module tries tobuild fuzzy taxonomy on behalf of ontology from documentswithout human intervention In order to produce ontologyprofessionally the development process is separated intothree steps term similarity processing document analysisand clustering algorithm

(iii) Matching Method It is the major retrieval mechanismThe related documents usually recovered and ranked usingsimilarity matching

(iv) Ontology Base Various forms of ontology are adoptedin the anticipated model such as WordNet usersrsquo fieldinformation constructed manually and automatically createsthe fuzzy taxonomy

(v) Ranking the Resulted Documents The escalating weightis intended for every permutation of words derived fromenhanced matching algorithmThemost excellent documentobtains the least score The documents are assembled inmounting order according to their collective score Theranked listing of appropriate documents is then demon-strated to the user in the matching order

(vi) Document Annotation for Retrieving Information Fromthe domain knowledge the documents are annotated withconcept by creating annotation class By using domain expertthe annotations are be created automatically Each case isdifferentiated using the manual subclass or with automaticannotations A valid outcome of document for an exactitudeoriented keyword query is observed using two events (i) thedesigned search assignment of the user presenting the queryand (ii) the semantic documents are satisfied To reach therelevant document semantics this research proposes an alter-native to information extraction techniques for recognizingstates of entities and relationships in a text document Everydeclaration is known as annotation and a formatted dataaccumulate including the intact of the extorted annotationsis called an annotation store

Usually for information retrieval system the documentsare processed in two phases document processing and


Algorithm Fuzzy GenerationInput Starting concept 119862

119878of concept lattice 119865(119870) and a similarity threshold 119879

119878

Output A set of generated conceptual clusters 119878119862

Process(1) 119878

119862rarr

(2) 1198651015840(119870) larr An empty concept lattice

(3) Add 119862119878to 1198651015840(119870)

(4) for each subconcept 1198621015840 of 119862119878in 119865(119870) do

(5) 1198651015840(1198621015840) larr Conceptual Cluster Generation(1198621015840 119865(119870) 119879

119878)

(6) if 119864(119862119878 1198621015840) =

|119862119878cap 1198621015840|

|119862119878cup 1198621015840|

lt 119879119878then

(7) 119878119862larr 119878119862cup 1198651015840(1198621015840)

(8) else(9) Insert 1198651015840(1198621015840) to 1198651015840(119870) with sup (1198651015840(119870)) as a subconcept of 119862

119878

(10) endif(11) endfor(12) 119878

119862larr 119878119862cup 1198651015840(119870)

Algorithm 1 Fuzzy generation model

Uncertainty information

Fuzzy concept lattice Conceptual clustersConcept hierarchy

Fuzzy formal concept analysis

Fuzzy conceptualclustering

Hierarchical relation generation

Figure 2 The traditional FOGA framework

query processing In document processing stage by usingtextual preprocessing the documents are processed to gainimperative stipulations and features for representing the doc-uments The conditions then are applied to construct fuzzytaxonomies from side to side of the ontology building tech-niquesThe concepts contain definitions and instances whichis given by the textual description of WordNet WordNet canbe satisfied as a moderately structured synonym store

There are three databases in WordNet noun is the initialone verbs is second database adjectives and adverbs are thefinal one ldquoSynsetsrdquo is a set of synonyms which designate aconcept or a sagacity of a set of terms Synsets available makediverse semantic relations for instance synonymy (similar)and antonymy (opposite) hypernymy (super concept)hy-ponymy (subconcept) (also known as a hierarchytaxonomy)meronymy (part-of) and holonymy (has-a) Depending onthe grammatical category the semantic relatives with thesynsets will vary The following sections discuss about doc-ument processing and information retrieval using standardfuzzy ontology framework

31 Fuzzy Ontology Framework for Information Retrieval InFOGA [1] construction a fuzzy logic offers a hypotheticalframework for the demonstration and management of theinformation with their deficiencies It does not undertake to

remove them on the contrary it aims to protect them Itstarget is consequently to construct settings of demonstrationand behavior of knowledge efficiently and it is stimulatedfrom the human intellectual process It slopes on the math-ematical fuzzy sets theory This presumption is a growthof the common set theory for investment groups describedin a vague approach The traditional FOGA consists of thefollowing components (see Figure 2)

(a) Fuzzy Formal Concept Analysis From a database restrain-ing unsecured data it assembles fuzzy context Additionallyit will also execute fuzzy formal concepts from the fuzzy formalcontext and categorizes the created concepts as a fuzzy conceptlattice

(b) Fuzzy Conceptual Clustering It groups concepts on thefuzzy concept lattice and executes conceptual clusters Theclustering method is evaluated from fuzzy information andintegrated into the web using fuzzy logic

(c) Hierarchical Relation Generation It produces hierarchicalrelationship between conceptual clusters to build a concepthierarchy

In Algorithm 1 based on the hypothesis the conceptualclusters are derived that if a formal concept 119861 is similar to 119860


Algorithm Keywords MatchingInput Keyword space 119870 attribute sets 119860 and document collection119863Output The presentation for document collections119863 shows in keyword space 119870

Step 1 For all 119889119894120598119863 calculate the weight 119908

119894119898119899in term 119896

119898119899where 119860

119898120598119860 and 119896

119898119899is the 119899th term of 119860

119898

Step 2 For all 119889119894120598119863 we normalized the weights119882

119894119898= 1199081198941198981

1199081198941198982

119908119894119898119901119898

As1198821015840119894119898

=

119901119898

sum

119899=1

119908119894119898119899

max (119908119894119898)

Step 3 Output1198821015840119894119898

Algorithm 2 Keyword matching

then conceptual cluster 119877 will be based on formal concept 119860and its sub concept 119861 The similarity between two concepts isdetermined by similarity confidence threshold 119879119904

To characterize vague information the restriction offuzzy logic will be integrated into ontology Characteristi-cally fuzzy ontology is constructed from a predeterminedconcept hierarchy On the other hand a complicated andtedious process is assembling the concept hierarchy for aparticular domain To overcome this difficulty the FOGA isimplemented for generating fuzzy ontology automatically oninformation uncertainty

32 Keyword Matching Ontologies Ducatel et al [16] broadlydescribed ontology-based queries for query generation andmatching of service representatives However services mayalso desire to illustrate themselves with free text such as withkeywords that are not already specified in the ontology Inorder to be capable of handling it requires a selected way forthe ontology to handle keywords and concepts Collaborativeinformation is a method of exploiting this data for the benefitof other users where frequent queries (from different users)are associated with valuable outcomes

The equal relationships with keywords is segregated forillustrating the semantic conceptions among documents interm comparison processing The quantity of identical asso-ciation can be calculated by semantic comparison calculatingprocess In a set of procedures based on WordNet the wordcomparison between keywords is intended through similaritymeasure

Initially in document investigation the important key-words are selected from the documents as the specifickeyword space 119870 Then the chosen keywords are allocatedinto many attributes of the keyword space119870 Let119860 be the setof attributes in119870 119860119898 = 119896

1198981 1198961198982

119896119898 119901119898 where 119860

119898isin

119860 and 119896119898119899 isin 119870 Algorithm 2 presents the demonstration inkeyword space 119870 which is measured through a subsequentalgorithm for collections of document119863

As the keywords in keyword space are measured thehybrid fuzzymatching technique is well designed to assemblefuzzy classification for every set of attributes The createdfuzzy arrangement matching to the domain of particularattributes is then accepted as the ontology applied to retrieverelevant information The matching strings are shown inFigure 3

According to the professional field (computer science)the ontology model is constructed which is depicted inAlgorithm 3

Figure 3 The similarity strings

33 Combining Hybrid Fuzzy Ontology Generation Frame-work and Keyword Matching Ontologies After splitting thequery into meaningful words each word should be checkedagainst the ontology The entire amalgamation of words is inuse for processing Scrupulous domain ontology is receivedto verify whether the declaration is to provide ontology Ifpersuaded then the association of the words is obtainedinto the deliberation The points are described for matchingontologies and the rules used to group related conceptstogether are listed below (parents-superset and child-subset)

(i) The parent conception demonstrates the perspectiveof the concept from this parent each matching con-cept are collected

(ii) Matching concepts with similar parent are controlledby individual score ought to be located jointly underindividual score

(iii) Each series of parent-child associated matching con-cepts that demonstrates the context of the series mustend in a non-matching concept

(iv) Unconnected groups are attached together as afforestprepared by the highest score of the group

(v) If the parents have the children with similarity thenthey will acquire the privileged of two portions andare connected together

In consequence the amalgamation of mutual hybridFOGA and keyword matching with an elucidation of akeyword query is set together by individually matching thequery terms in the keyword query against the elements of


Step 1 Determine the scope of the ontologyStep 2 Consider reusing (parts of) existing ontologiesStep 3 Enumerate all the concepts you want to includeStep 4 Define the arrangement of these conceptsStep 5 Define properties of the conceptsStep 6 Define facets of the concepts such as cardinality required values and so forthStep 7 Define instancesStep 8 Check the consistency of the ontology

Algorithm 3 Steps for construction model

Algorithm for relevance path-matchInput match point = 119905119902

119894 119905119903119895 119889(119902119894 119903119895) match path maxQDist

Output bool = PASS FAILΔ119902 larr

1003816100381610038161003816119905119902119894 minusmatch path119905119902end1003816100381610038161003816

Δ119903 larr10038161003816100381610038161003816119905119903119895minusmatch path119905119903end

10038161003816100381610038161003816if Δ119902 ltmaxQDist amp Δ119903 ltmaxQDist thenreturn(PASS)else if Δ119902 gtmaxQDist thenprocess amp extract(match path)end ifreturn(FAIL)

Algorithm 4 Relevance path match

annotation store An annotation store 119878 = (119879 119874119863) consistsof a position of types 119879 (signify doc docx pdf etc) a set ofobjects119874 and exceptional distinguished sort119863 isin 119879 such thatfor every 119909 isin 119874 type(119909) isin 119879 Further for every object 119909 isin 119874also type(119909) = 119863 otherwise there survives an element docwith type(119909doc) =119863 Given an annotation store 119878 = (119879 119874119863)

and a query term119870where 119878 is the type of document and eachadded type is an annotation type in 119878 In the above object 119909 isrepresented by type(119909) A document attribute is enclosed foreach attribute which look up the document from where theobjects are extractedThis annotation store of the path can beof any expression of 119879119886

1sdot sdot sdot 119886119898 where legitimate attribute of

type 119879 is represented as 1198861 type attribute (119879119886

1) is 1198862and so

onThis research work envisages the following three forms of

matches

(i) Type Match If the particular or selection name of itssignificance is matched by 119896 then 119896 matches a type 119879 isin 119879For example the keywords ldquophonerdquo ldquocontactrdquo and ldquonumberrdquomay all match the type Phone Number if all three keywordshave been defined as synonyms of this concept In commonthis research assumes that the input to the precision orientedretrieval system is the set of synonyms which is associatedwith each type

(ii) PathMatchMatches not in favor of paths are calculated inan analogous approach using the matching set of synonymsThe pathmatch containsmaxQDist-vector and scalar param-eter Δ119902-query and Δ119903-search collection

Algorithm 4 uses this constraint to avoid big nonmatch-ing gaps between consecutive matching points This algo-rithm considers the maxQDist as the maximum elapsedtime in either time series Moreover given that the query isprocessed sequentially in time (ie 119905119902119894 lt 119905119902119894+1 forall119894) pathsthat do not comply with this constraint are removed fromΔ119879 (function ldquoprocessampextract()rdquo) as it is ensured that theywill no longer comply with the constraintThe removed pathsare then evaluated in terms of minimum length numberof matching points and score to determine if they can beconsidered a good match between both time series (119905119902119894 119905119903119895)

For instance as the synonym ldquofonerdquo is connected withthe concept PhoneNumber then TypePath index mapsldquofonerdquo to the type PhoneNumber to the path Author-Phonephone and so on As such the synonyms ldquocallinrdquoldquodial-inrdquo ldquoconcallrdquo and ldquoconferencecallrdquo are mapped tothe type ConferenceCall The keyword ldquotomrdquo has a valuematch with Authorname AuthorPhoneauthorname indi-cating that ldquotomrdquo has appeared as the name of the author ofan email as the name of a person who was declared in thesignature block of an email and so forth

(iii) ValueMatchTo concludematches not in favor ofminutevalues are calculated with contrasting 119896 next to the rest ofminute values connected with every path in the annotationstore The value matching makes use of domain checks tocalculate the relationship computed among phrases At anytime constraint value-sets are present we can enhance ourknowledge of the domain as such constraints turn to beprecious when evaluating two terms that do not preciselymatch through their labels


Input 997888119902 = (1198961 1198962 1198963 119896

119898) and a selected ontology119867

Output An ontology of query119867119902

Step 1 Set 119894 = 1Step 2 Set 119897 = 1 MR = 0 where MR is the maximum relationshipsStep 3 Compare the term 119896

119894with all 119862119897

119895isin 119867119897 find the best node with highest relationship 119877(119896

119894 119862119897

119895)

If MR lt 119877(119896119894 119862119897

119895) then MR lt 119877(119896

119894 119862119897

119895) and MC = 119862

119897

119895

Step 4 119897 = 119897 + 1 and if119867119897isin 119867 then go to Step 3

Step 5 Add the sub-ontology start fromMC into the119867119902 Set 119894 = 119894 + 1 and if 119896

119894in 997888

119902 then got to Step 3Step 6 Output an ontology119867

119902of query

Algorithm 5 Hybrid ontology mapping

The next step of comparison measure retrieves and ranksthe relevant documents from the document database In thebeginning the ontology of query preferred form the initialstep (in Algorithm 5) is used to regulate the weights ofdocuments The method of computing adjusted weights for119867119902is demonstrated as follows

997888119889

1015840

119894= sum

119896119889isin119889119894

max( sum

119896119902isin119867119902

119877 (119896119902 119896119889)) times 119908

119894119889 (1)

where119908119894119889is the weight of document and 119889

119894presented in term

119896119889 119896119902is the terms of119867

119902 Finally the comparison measure is

computed with the following function

Sim(997888119889

1015840

119894997888119902) = max(sum

119904119903isin119878

cos(997888119889

1015840

119894997888119902) times 119904119903) (2)

where 119904119903is the weights of nominated ontology cos(

997888119889

1015840

119894997888119902 ) is

the cosine comparison For instance a query ldquoFishing ferry inSouth Africardquo can be symbolized as ldquofishrdquo ldquoferryrdquo ldquoinrdquo ldquoSouthAfricardquo The term ldquosouth africardquo is mapped into the conceptldquos africardquo of the ontology 119867 ldquoLocationrdquo and 119904 ldquoLocationrdquo = 1The ontology of query119867

119902is mapped

4 Experimental Results

This section described the experimental setup for hybridFOGA using keyword matching to retrieve the relevantinformation and ranking the documents automatically Thedataset is constructed using list of abstracts selected from1000 documents which are all collected from theweb Initiallythe documents are updated to the FOGA framework withpreprocessed information The elimination of stop wordsand operations of stemming are performed The weightestimation process is done with term analysis and semanticanalysis tasks The related journals are collected for the fuzzyontology from the web Using HTML the abstract pagesare intended for manuscripts The text document conversionis done by removing the HTML tag elements from theweb documents and document information is maintained inseparate files The two most common and important metrics

0102030405060708090

100

Standard FOGA Keyword matching Hybrid FOGA

Prec

ision

and

reca

ll (

)

PrecisionRecall

Figure 4 Showing the precision and recall for proposed hybridFOGA

0010203040506070809

1


F-measure

Figure 5 The 119865-measure for proposed hybrid FOGA

for information retrieval efficiencies are precision and recallIn consequence this research work used these measures forthe ontology presentation for evaluation Precision and recallare described in terms of a set of retrieved documents (egthe list of documents listed through a web search enginefor an uncertainty) and a group of relevant documents (eg


(a) (b)

Figure 6 The hybrid fuzzy ontology

the list of every document on the net that is applicable for aconvinced area)

Precision = (relevant items retrieved)

(retrieved items)

= 119875 (relevant | retrieved)

Recall = (relevant items retrieved) (relevant items)

= 119875 (retrieved | relevant)

119865-Measure = 2 sdotprecision sdot recallprecision + recall

(3)

The standard precision combines each query at recalllevel diagonally and calculates whole system performanceapproximately on a documentquery capability

For the sake of precision and recall some researchersimprove the architecture of inverted files The authors movequery keywords to semantic terms But index tables still usedkeyword-based ones To make the match easier a new indextable with semantic terms is proposed in this work

The combination of standard ontology with FOGA tech-niques in this research prescribes the solution for informa-tion retrieval using keyword matching indexing techniquesThe 119865-measure indicates that the overall average perfor-mances of all relationships are similar with a slight trend ofhigher 119865-measure for hybrid FOGA implementations

Both Figures 4 and 5 represent the precision recall and119865-measure for information retrieval by comparing threeschemes for fuzzy ontology frameworkThe hybrid techniquehas shown the best precision recall and 119865-measure values inthe FOGA framework Our approach improve the classicalmethodology approach and the best documents are in the topof retrieved document list

To evaluate the proposed hybrid FOGA framework thisresearch collected a set of 1000 scientific documents in theresearch area ldquoinformation retrievalrdquo There are two shortestgoals general to all IR methods (a) effectiveness IR mustbe accurate (achieves what the user expects to observe inthe answer) (b) efficiency IR should be speedy (quickerthan chronological scanning) The main goal of informationretrieval is to possess relevant documents in response to userneeds The performance of ontology is evaluated with theresearch area hierarchy created using hybrid FOGA Initiallyprecision recall and 119865-measure are calculated for informa-tion retrieval If these parameters acquires the goodnessthen the conceptual information are generated accuratelyThus the performance of hybrid fuzzy ontologies is shownin Figure 6

5 Conclusion

In this research a latest approach for retrieving informationsuccessfully through implementation of hybrid ontology isdiscussedThis research presents a development in the hybridontology semantic information retrieval through (a) gettingback a group of relevant documents semantic method usingthe proposed hybrid ontology (b) dealing with the varietyof field topics problem using hybrid concept view fuzzyontology and (c) ranking the end result set of documentsaccording to 119865-measures which are relevance quantity withrespect to uses query confidence and updating degree Sothis research proposed a hybrid ontology which integratesand takes advantages of SW and IR technologies to providebetter search capabilities achieving a qualitative improvementby using keyword-based information retrieval The futurework in this part is possible to construct a documentannotation algorithm using the proposed hybrid ontology


Furthermore the hope of this research work motivatesimplementing fuzzy theory and neural network methods tobuild fuzzy ontology from unstructured data automatically

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] Q T Tho S C Hui A C M Fong and T H Cao ldquoAutomaticFuzzy ontology generation for semantic Webrdquo IEEE Transac-tions on Knowledge andData Engineering vol 18 no 6 pp 842ndash856 2006

[2] C de Maio G Fenza V Loia and S Senatore ldquoTowards anautomatic fuzzy ontology generationrdquo in Proceedings of theIEEE International Conference on Fuzzy Systems pp 1044ndash1049August 2009

[3] M Abulaish and L Dey ldquoA fuzzy ontology generation frame-work for handling uncertainties and nonuniformity in domainknowledge descriptionrdquo in Proceedings of the InternationalConference onComputingTheory andApplications (ICCTA rsquo07)pp 287ndash293 March 2007

[4] A Formica ldquoConcept similarity in fuzzy formal concept anal-ysis for semantic webrdquo International Journal of UncertaintyFuzziness and Knowledge-Based Systems vol 18 no 2 pp 153ndash167 2010

[5] P Chahal M Singh and S Kumar ldquoAn ontology basedapproach for finding semantic similarity between web docu-mentsrdquo International Journal of Current Engineering and Tech-nology vol 3 no 5 pp 1925ndash1931 2013

[6] A Formica ldquoSimilarity reasoning for the semantic web basedon fuzzy concept lattices an informal approachrdquo InformationSystems Frontiers vol 15 no 3 pp 511ndash520 2013

[7] F Zhang Z M Ma G Fan and X Wang ldquoAutomatic fuzzysemantic web ontology learning from fuzzy object-orienteddatabase modelrdquo in Database and Expert Systems Applicationsvol 6261 of Lecture Notes in Computer Science pp 16ndash30Springer Berlin Germany 2010

[8] C deMaio G Fenza V Loia and S Senatore ldquoHierarchical webresources retrieval by exploiting fuzzy formal concept analysisrdquoInformation Processing amp Management vol 48 no 3 pp 399ndash418 2012

[9] S Kohli and A Gupta ldquoA survey on web information retrievalinside fuzzy frameworkrdquo in Proceedings of the Third Interna-tional Conference on Soft Computing for Problem Solving vol259 of Advances in Intelligent Systems and Computing pp 433ndash445 Springer New Delhi India 2014

[10] A Aloui A Ayadi and A Grissa-Touzi ldquoA semi-automaticmethod to fuzzy-ontology design by using clustering andformal concept analysisrdquo in Proceedings of the 6th InternationalConference on Advances in Databases Knowledge and DataApplications (DBKDA 14) pp 19ndash25 2014

[11] A Kandpal R H Goudar R Chauhan S Garg and KJoshi ldquoEffective ontology alignment an approach for resolvingthe ontology heterogeneity problem for semantic informationretrievalrdquo in Intelligent Computing Networking and Informaticsvol 243 of Advances in Intelligent Systems and Computing pp1077ndash1087 Springer New Delhi India 2014

[12] M Rani M K Muyeba and O P Vyas ldquoA hybrid approachusing ontology similarity and fuzzy logic for semantic ques-tion answeringrdquo in Advanced Computing Networking andInformaticsmdashVolume 1 Smart Innovation Systems and Tech-nologies pp 601ndash609 Springer Berlin Germany 2014

[13] Z Xu X Wei X Luo et al ldquoKnowle a semantic link networkbased system for organizing large scale online news eventsrdquoFuture Generation Computer Systems vol 43-44 pp 40ndash502015

[14] Y Liu L Chen X Luo L Mei C Hu and Z Xu ldquoSemantic linknetwork basedmodel for organizingmultimedia big datardquo IEEETransactions on Emerging Topics in Computing vol 2 no 3 pp376ndash387 2014

[15] F Hao G Min J Chen et al ldquoAn optimized computationalmodel for multi-community-cloud social collaborationrdquo IEEETransactions on Services Computing vol 7 no 3 pp 346ndash3582014

[16] G Ducatel Z Cui and B Azvine ldquoHybrid ontology andkeyword matching indexing systemrdquo in Proceedings of theIntraWeb Workshop (WWW rsquo06) Edinburgh Scotland 2006

Submit your manuscripts athttpwwwhindawicom

Computer Games Technology

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Distributed Sensor Networks


Advances in

FuzzySystems

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014


ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014


Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Electrical and Computer Engineering

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Journal of

Computer Networks and Communications


Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

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Biomedical Imaging


ArtificialNeural Systems

Advances in


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Computational Intelligence and Neuroscience

Industrial EngineeringJournal of


Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in



The semantic web is build for current web extensionwhere the information has well defined meaning and ena-bling cooperation between people and computers Becauseof this well-defined structure humans and even machineswill work in coperation The standard fuzzy ontology is atechnique which is used in information retrieval where thecalculation of relationship among the concepts are done usingmembership values From domainrsquos uncertainty data gen-eration of fuzzy ontology automatically is highly desirableThis research explores hybrid fuzzy ontology-based informa-tion retrieval models in semantic web and gossip about theachievement and authority of applying proposed ontologycontaining common field knowledge and fuzzy concepts fab-ricated from the stored documents automatically For map-ping the generated fuzzy ontology to semantic representationWeb Ontology Language (OWL) is used

This research work is organized as follows The relatedwork is reviewed in Section 2 The proposed ontology-basedinformation retrieval model is depicted in Section 3 Theexperiments and discussion on the results are described inSection 4 Finally conclusion is given in Section 5

2 Related Materials

Tho et al [1] proposed the FOGA (Fuzzy Ontolology Gener-ation frAmework) in which fuzzy ontology is generated onvague information automatically A fuzzy-based method isdescribed for integrating database attributes to the ontologyThey converse about approximating reasoning for additionalenhancement of the ontology de Maio et al [2] describedan approach by analyzing the web resource collection forautomatic fuzzy ontology elicitation This approach applica-bility is validated by web domain case study Abulaish et al[3] recommended a fuzzy ontology generation framework inwhich instead of concept descriptor the possession quantityis encoded using fuzzy membership function The FuzzyFormal Concept Analysis (FFCA) which is generalization ofFormal Concept Analysis (FCA) used for sculpting vaguenessinformation Formica [4] showed the FFCA amalgamationwith rough set theory to complete semantic web explorationand detection of information in the web Chahal et al[5] presented a similarity comparison scheme of semanticweb document which relies instances between keywords indocuments and also the relationship in the web pages whichexists between concepts amalgamation

Formica [6] proposed a similarity measure for FFCAThis FFCA is usually intended for restricted audience andaddressed at technical level although it becomes very inter-esting for semantic web development by supporting differentactivities The development of ontologies manually is a timeconsuming and cumbersome task Zhang et al [7] plannedan approach and an automated tool from Fuzzy ObjectOriented Database (FOOD) models for constructing thefuzzy ontologies This ontology plays an important role forthe development of new strategies of knowledge based sys-tems and in supporting the automated process for accessing

information So de Maio et al [8] presented an ontology-based retrieval approach which supports data organizationand visualization and provides a friendly navigation model

To design information retrieval system the major chal-lenges for researchers and developers is themethod of sharingand searching the information with emergence of webKohli and Gupta [9] surveyed the challenges in informationretrieval and solve those challenges with the help of fuzzyconcept Aloui et al [10] have presented a semiautomaticmethod for fuzzy ontology extraction and design (FOD)Themethod is based on conceptual clustering fuzzy logic andformal concept analysis (FCA) The core of ontology is rep-resented as a set of fuzzy rules To validate the proposed ap-proach they used Protege 43 that supports the fuzzy conceptand automatically generate the script in OWL-2 language

Sometimes irrelevant information is retrieved on thesemantic web but it is meaningful and with ontology map-ping the relevance can be improved Kandpal et al [11]described a new technique for ontology mapping Two var-ious ontologies of a domain are considered and the conceptswhich are similar to each other are retrieved that is ontologyalignment The similarity is calculated if the concepts are notmatched even when term is expanded One of the challengesin information retrieval is providing accurate answers to auserrsquos question often expressed as uncertainty words Rani etal [12] presented a hybrid approach for a semantic questionanswering retrieval system using ontology similarity andfuzzy logic to retrieve collection of documents Fuzzy scaleuses fuzzy type 1 for documents and fuzzy type 2 for words toprioritize answers

Recently the data originated frommultiple types of sour-ces includes the mobile devices individual archives sensorssocial networks enterprises and cameras Internet of thingssoftware logs and health data have led to one of the mostchallenging research concerns of the big data era So Xuet al [13] suggested the basic blocks of the Knowles systemresources representation semantic relations mining andsemantic linking news events and it does not need datacontributors to pursue semantic standards such as RDF orOWL which is a semantics loaded self-categorized network

Liu et al [14] proposed a technique in effective mannerfor organizing the associated multimedia resources and forsemantic link network model which is used for organizingmultimedia network The community cloud computing is apromising and emerging model for a particular communitywith general concerns such as compliance security andjurisdiction Selecting the best group of community cloudsthat are the most economy and communication effective andtrusted to complete a difficult task is extremely challeng-ing To deal with this problem Hao et al [15] formulatecomputationalmodelmulti-community-cloud collaborationnamely MG3 The proposed model is then optimized fromfour aspects minimize the sum of monetary and access costmake the most of security level agreement and trust amongthe community clouds

So the study of related works motivates the semanticmatching technique by combining the fuzzy ontology withkeyword matching to retrieve the relevant information


QueryDocuments

Query preprocessing



Similarity measure

Relevant documents



Document analysis


WordNet

Domain knowledge

02


Domain knowledge


















119878


Process(1) 119878

119862rarr


(3) Add 119862119878to 1198651015840(119870)



119878)

(6) if 119864(119862119878 1198621015840) =

|119862119878cap 1198621015840|

|119862119878cup 1198621015840|

lt 119879119878then

(7) 119878119862larr 119878119862cup 1198651015840(1198621015840)


119878

(10) endif(11) endfor(12) 119878

119862larr 119878119862cup 1198651015840(119870)



















119894119898119899in term 119896

119898119899where 119860

119898120598119860 and 119896

119898119899is the 119899th term of 119860

119898


119894119898= 1199081198941198981

1199081198941198982

119908119894119898119901119898

As1198821015840119894119898

=

119901119898

sum

119899=1

119908119894119898119899

max (119908119894119898)

Step 3 Output1198821015840119894119898







1198981 1198961198982

119896119898 119901119898 where 119860

119898isin


























1) is 1198862and so


matches







Input 997888119902 = (1198961 1198962 1198963 119896




119894with all 119862119897


119894 119862119897

119895)

If MR lt 119877(119896119894 119862119897

119895) then MR lt 119877(119896

119894 119862119897

119895) and MC = 119862

119897

119895



119894in 997888


119902of query



997888119889

1015840

119894= sum

119896119889isin119889119894

max( sum

119896119902isin119867119902

119877 (119896119902 119896119889)) times 119908

119894119889 (1)



119896119889 119896119902is the terms of119867



Sim(997888119889

1015840

119894997888119902) = max(sum

119904119903isin119878

cos(997888119889

1015840

119894997888119902) times 119904119903) (2)


997888119889

1015840

119894997888119902 ) is


119902is mapped



0102030405060708090

100


Prec

ision

and

reca

ll (

)

PrecisionRecall


0010203040506070809

1


F-measure




(a) (b)




(retrieved items)





(3)






5 Conclusion






References
























Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




QueryDocuments

Query preprocessing



Similarity measure

Relevant documents



Document analysis


WordNet

Domain knowledge

02


Domain knowledge


















119878


Process(1) 119878

119862rarr


(3) Add 119862119878to 1198651015840(119870)



119878)

(6) if 119864(119862119878 1198621015840) =

|119862119878cap 1198621015840|

|119862119878cup 1198621015840|

lt 119879119878then

(7) 119878119862larr 119878119862cup 1198651015840(1198621015840)


119878

(10) endif(11) endfor(12) 119878

119862larr 119878119862cup 1198651015840(119870)



















119894119898119899in term 119896

119898119899where 119860

119898120598119860 and 119896

119898119899is the 119899th term of 119860

119898


119894119898= 1199081198941198981

1199081198941198982

119908119894119898119901119898

As1198821015840119894119898

=

119901119898

sum

119899=1

119908119894119898119899

max (119908119894119898)

Step 3 Output1198821015840119894119898







1198981 1198961198982

119896119898 119901119898 where 119860

119898isin


























1) is 1198862and so


matches







Input 997888119902 = (1198961 1198962 1198963 119896




119894with all 119862119897


119894 119862119897

119895)

If MR lt 119877(119896119894 119862119897

119895) then MR lt 119877(119896

119894 119862119897

119895) and MC = 119862

119897

119895



119894in 997888


119902of query



997888119889

1015840

119894= sum

119896119889isin119889119894

max( sum

119896119902isin119867119902

119877 (119896119902 119896119889)) times 119908

119894119889 (1)



119896119889 119896119902is the terms of119867



Sim(997888119889

1015840

119894997888119902) = max(sum

119904119903isin119878

cos(997888119889

1015840

119894997888119902) times 119904119903) (2)


997888119889

1015840

119894997888119902 ) is


119902is mapped



0102030405060708090

100


Prec

ision

and

reca

ll (

)

PrecisionRecall


0010203040506070809

1


F-measure




(a) (b)




(retrieved items)





(3)






5 Conclusion






References
























Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in






119878


Process(1) 119878

119862rarr


(3) Add 119862119878to 1198651015840(119870)



119878)

(6) if 119864(119862119878 1198621015840) =

|119862119878cap 1198621015840|

|119862119878cup 1198621015840|

lt 119879119878then

(7) 119878119862larr 119878119862cup 1198651015840(1198621015840)


119878

(10) endif(11) endfor(12) 119878

119862larr 119878119862cup 1198651015840(119870)



















119894119898119899in term 119896

119898119899where 119860

119898120598119860 and 119896

119898119899is the 119899th term of 119860

119898


119894119898= 1199081198941198981

1199081198941198982

119908119894119898119901119898

As1198821015840119894119898

=

119901119898

sum

119899=1

119908119894119898119899

max (119908119894119898)

Step 3 Output1198821015840119894119898







1198981 1198961198982

119896119898 119901119898 where 119860

119898isin


























1) is 1198862and so


matches







Input 997888119902 = (1198961 1198962 1198963 119896




119894with all 119862119897


119894 119862119897

119895)

If MR lt 119877(119896119894 119862119897

119895) then MR lt 119877(119896

119894 119862119897

119895) and MC = 119862

119897

119895



119894in 997888


119902of query



997888119889

1015840

119894= sum

119896119889isin119889119894

max( sum

119896119902isin119867119902

119877 (119896119902 119896119889)) times 119908

119894119889 (1)



119896119889 119896119902is the terms of119867



Sim(997888119889

1015840

119894997888119902) = max(sum

119904119903isin119878

cos(997888119889

1015840

119894997888119902) times 119904119903) (2)


997888119889

1015840

119894997888119902 ) is


119902is mapped



0102030405060708090

100


Prec

ision

and

reca

ll (

)

PrecisionRecall


0010203040506070809

1


F-measure




(a) (b)




(retrieved items)





(3)






5 Conclusion






References
























Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in






119894119898119899in term 119896

119898119899where 119860

119898120598119860 and 119896

119898119899is the 119899th term of 119860

119898


119894119898= 1199081198941198981

1199081198941198982

119908119894119898119901119898

As1198821015840119894119898

=

119901119898

sum

119899=1

119908119894119898119899

max (119908119894119898)

Step 3 Output1198821015840119894119898







1198981 1198961198982

119896119898 119901119898 where 119860

119898isin


























1) is 1198862and so


matches







Input 997888119902 = (1198961 1198962 1198963 119896




119894with all 119862119897


119894 119862119897

119895)

If MR lt 119877(119896119894 119862119897

119895) then MR lt 119877(119896

119894 119862119897

119895) and MC = 119862

119897

119895



119894in 997888


119902of query



997888119889

1015840

119894= sum

119896119889isin119889119894

max( sum

119896119902isin119867119902

119877 (119896119902 119896119889)) times 119908

119894119889 (1)



119896119889 119896119902is the terms of119867



Sim(997888119889

1015840

119894997888119902) = max(sum

119904119903isin119878

cos(997888119889

1015840

119894997888119902) times 119904119903) (2)


997888119889

1015840

119894997888119902 ) is


119902is mapped



0102030405060708090

100


Prec

ision

and

reca

ll (

)

PrecisionRecall


0010203040506070809

1


F-measure




(a) (b)




(retrieved items)





(3)






5 Conclusion






References
























Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in

















1) is 1198862and so


matches







Input 997888119902 = (1198961 1198962 1198963 119896




119894with all 119862119897


119894 119862119897

119895)

If MR lt 119877(119896119894 119862119897

119895) then MR lt 119877(119896

119894 119862119897

119895) and MC = 119862

119897

119895



119894in 997888


119902of query



997888119889

1015840

119894= sum

119896119889isin119889119894

max( sum

119896119902isin119867119902

119877 (119896119902 119896119889)) times 119908

119894119889 (1)



119896119889 119896119902is the terms of119867



Sim(997888119889

1015840

119894997888119902) = max(sum

119904119903isin119878

cos(997888119889

1015840

119894997888119902) times 119904119903) (2)


997888119889

1015840

119894997888119902 ) is


119902is mapped



0102030405060708090

100


Prec

ision

and

reca

ll (

)

PrecisionRecall


0010203040506070809

1


F-measure




(a) (b)




(retrieved items)





(3)






5 Conclusion






References
























Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




Input 997888119902 = (1198961 1198962 1198963 119896




119894with all 119862119897


119894 119862119897

119895)

If MR lt 119877(119896119894 119862119897

119895) then MR lt 119877(119896

119894 119862119897

119895) and MC = 119862

119897

119895



119894in 997888


119902of query



997888119889

1015840

119894= sum

119896119889isin119889119894

max( sum

119896119902isin119867119902

119877 (119896119902 119896119889)) times 119908

119894119889 (1)



119896119889 119896119902is the terms of119867



Sim(997888119889

1015840

119894997888119902) = max(sum

119904119903isin119878

cos(997888119889

1015840

119894997888119902) times 119904119903) (2)


997888119889

1015840

119894997888119902 ) is


119902is mapped



0102030405060708090

100


Prec

ision

and

reca

ll (

)

PrecisionRecall


0010203040506070809

1


F-measure




(a) (b)




(retrieved items)





(3)






5 Conclusion






References
























Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in




(a) (b)




(retrieved items)





(3)






5 Conclusion






References
























Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in







References
























Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in










Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in

