Rank Based Clustering For Document Retrieval From Biomedical Databases

Transcription

1 Jayanth Mancassamy et al /Internatonal Journal on Computer Scence and Engneerng Vol.1(2), 2009, Rank Based Clusterng For Document Retreval From Bomedcal Databases Jayanth Mancassamy Department Of Computer Scence, Pondcherry Unversty, Kalapet, Inda. P. Dhavachelvan Department Of Computer Scence, Pondcherry Unversty, Kalapet, Inda. Abstract Now a day s, search engnes are been most wdely used for extractng nformaton s from varous resources throughout the world. Where, majorty of searches les n the feld of bomedcal for retrevng related documents from varous bomedcal databases. Currently search engnes lacks n document clusterng and representng relatveness level of documents extracted from the databases. In order to overcome these ptfalls a text based search engne have been developed for retrevng documents from Medlne and PubMed bomedcal databases. The search engne has ncorporated page rankng bases clusterng concept whch automatcally represents relatveness on clusterng bases. Apart from ths graph tree constructon s made for representng the level of relatedness of the documents that are networked together. Ths advance functonalty ncorporaton for bomedcal document based search engne found to provde better results n revewng related documents based on relatveness. Keywords- Bomedcal, Clusterng, Databases, Informaton Retreval, Text Mnng, Web-based. I. INTRODUCTION At present, there found to be a tremendous movement towards development of technologes that are been establshng n each and every area nvolvng bonformatcs. Prevously area of bonformatcs found to be lackng but now, ths area found to have a tremendous development comparatvely to other areas [1-5]. Today, rapdly ncreasng volume of publcatons n the bomedcal, fndng related work s an ever more dffcult challenge. General solutons to the document search problems are dffcult because bomedcal scence s very dverse for the artcles most relevant to the readers the relevancy may vary. Relevance s a well-establshed technque to mprove performance n nformaton retreval. Herarchcal classfcaton has receved growng attenton n the bomedcal feld n recent years whch s used for varous bologcal data s related classfcaton specfcally n the feld of text mnng [10-15]. In the study made t has been found that herarchcal classfcaton found to have better results of utlzaton whch provoked n the development of a text based search engne whch have been narrated n ths artcle. The developed text based search engne s capable of retrevng bomedcal documents from bomedcal databases Medlne and PubMed that are clustered based on the relatveness of the document to the user search. Clusterng based on page rankng whch represents the level of relatveness for the retreved clustered documents. Document retreval s based on the occurrence of bomedcal termnologes and keywords based on the user search text. Consderatons taken for document retreval are manly consdered here are both on postonal and relatonshp apart from the other crtera s has been descrbed n secton II. The developed tool have been evaluated whch have been narrated n secton III n results and dscussons. II. A. Implementaton SYSTEM DESCRIPTION In ths secton system mplementatons along wth archtecture have been dscussed. The system developed s a text based search engne whch s capable of extractng the documents from Medlne and PubMed databases. Fgure 1 shows the system archtecture n whch rectangle represents tasks. Dotted lnes represent sub tasks lnks and streamed lne represents major tasks nvolved for retrevng documents and rounded corner box represents actvtes. The only poston n the system where human nterventon s requred s enterng the text to be searched for documents retrevng n ths system. For the developed system search text s nputted by the user for whch related and relevant documents have to be retreved. The followng are major tasks nvolved n processng the texts and retrevng the documents. 1) Analyzer Ths s the frst major task that s carred out each and every tme when texts are entered for document retrevng. Keywords, bomedcal termnologes are extracted from the texts that are to be searched for documents. From the extracted keywords and termnologes related or equvalent alternatve words are extracted through wordnet and stored n a relaton lst temporarly through relaton setter. 111

2 Jayanth Mancassamy et al /Internatonal Journal on Computer Scence and Engneerng Vol.1(2), 2009, Fgure 1: System Archtecture Search Texts Bomedcal Termnologes Keywords Analyzer Relaton setter Temp Folder Keywords, Termnologes WordNet Relaton Lst Document Extractor Internet Medlne Doc Lster Rank Organzer Organzer PubMed Cluster Tree Constructor Retreved Documents Dsplay 2) Document Extractor Based on the relaton lst related and relevant documents are extracted from Medlne and PubMed bomedcal databases based on keywords and termnologes based on the comparson made wth that of relaton lst. The extracted documents are stored n temp folder. The document wth related keywords based on whch document have been extracted and stored n temp folder s lsted n doc lster and stored n the same temp folder for carryng out the next major task. The task of ths organzer s to rank the documents based on rank score through rank organzer by utlzng doc lster. The next task of ths organzer s to cluster the documents based on rank score wth represents the level of relatveness of the text search made by the system whch s then constructed n graph tree through tree constructor. 3) Organzer 112

3 Jayanth Mancassamy et al /Internatonal Journal on Computer Scence and Engneerng Vol.1(2), 2009, Fgure 2: Model Graph Tree Structure Clusterng Prorty Level 0 1 Doc1 2 Doc 3 3 Clusterng Range - Level >45 7 The document extracted are vsualzed n graph tree wth parent nodes representng level of relatveness and sub node of the parent nodes represents document placed n herarchcal level whch could be revew by clckng each nodes. B. Workng Methodology In ths secton processng methodology of the system have been narrated n detal as follows 1) Analyzer Inputted text processng s ntalzed at ths step whch takes n the search text whch s analyzed for keywords, bomedcal termnologes and sets relatonal lst by usng relaton setter. 2) Relaton Setter The actvty of ths relaton setter s to dentfy keywords from the texts that are to be searched for whch bomedcal termnologes are sptted. Both keywords and termnologes are temporally stored. By usng both keywords and termnologes as base usage WordNet all possble alternatve related words are extracted and lsted n relaton lst based on the relatveness. The prorty of postonng the keywords and termnology n the lst s frst exact termnologes and keywords followed by related termnologes and keywords. 3) Document Extractor Documents are extracted from Medlne and PubMed databases based on extractng keywords and termnologes 113

4 Jayanth Mancassamy et al /Internatonal Journal on Computer Scence and Engneerng Vol.1(2), 2009, from the documents and makng a comparson wth that of relaton lst. If match found then the document are been lsted wth matched keyword n doc lst. Both doc lst and the matched documents are stored n a temp folder. Doc lst conssts of relatonshp of the keywords to the document. 4) Organzer are vsualzed n graph tree wth parent nodes representng level of relatveness and sub node of the parent nodes represents document placed n herarchcal level where top poston found to hgh comparatvely to the next levels. Documents could be vewed by clckng on the respectve chld nodes. III. EVALUATION AND DISCUSSION Doc lster s used norder to carry out further actvtes n documents dsplayng for the search made by the organzer. Based on keywords and termnologes rank score s through rank organzer rank score of extracted documents are evaluated based on keywords and termnologes. For rank score evaluaton each drect match keyword (d k ) and drect match termnologes (d t ) value 1. Where as for ndrect drect match keyword (d k ) value 0.5 and ndrect match bomedcal termnologes (d t ) value 0.8. DS d + d + d + d k t k t = + K d + d + d + d = k t k t CL 100 K d + k t d = 100 K k t d = 100 K d d + d Kw Here, DS represent document rank score, K are keywords excludng bomedcal termnologes and T are bomedcal termnologes found n user entered search text. Kw s total keywords and bomedcal termnologes match the text search found from the document. CL represents Clusterng Level of relatveness for the texts search made for document retreval. d denotes level of drect keywords and termnologes matches the present document compared wth that of the search documents. d denotes level of ndrect keywords and termnologes matches the present document compared wth that of the search documents. Clusterng of documents and postonng the documents n the cluster depends on CL, d and d. If d found to hgher compare to d than 0.2 s add to DS. Graph Tree constructon d made based on the clusterng and rankng of the documents. Tree constructon groups documents and represented n a tree structure based on relatveness of the document that have been represented as n fgure 2. In the fgure 1 to 7 represents cluster level of relatveness whch are parent nodes. Whle chld nodes are documents extracted from the databases. The approach s a top down approach where cluster level 1 s hgh where the level ncreases the level of relatveness of the documents to the search text decreases. doc 1 to doc n represents the poston and rank of the document n that cluster. The document extracted (1) (2) (3) (4) In ths secton evaluaton on the developed system has been carred out for whch the performance doesn t le n one workng step of the system. Where as each and every process are responsble for system performance for whch the major performance les n the analyss part. For varous evaluatons carred out on the system the performance found to be good. The only place where human nterventon s requred s enterng the texts for search n retrevng documents form Medlne and PubMed databases. The evaluaton of the system found to have precson of 87% and recall found to be 89% whch s found to be hgh of whch ther found to be less varaton n performance. It s well known that, there exsts lots of nformaton needs related to bomedcal area whch vary n functonalty where most of them fall onlne. The man novelty les n clusterng the documents based on relatveness and representng n a graph tree structure for dsplayng documents. Documents are represented as chld nodes and revewng each document s by clckng the each chld node. The system utlzaton could be very benefcal for the communty n the feld of bomedcal for revewng document based on the relatveness of the search text formulated by users. Ths would be very helpful n terms of tme and effort for revewng only the requred hghly relatve documents. IV. CONCLUSION Currently developed text based search system found to be hghly sgnfcant n bomedcal doman for document retreval. Ths system shows an mprovement over the exstng systems wth better results whch offer new nformaton representaton capabltes wth dfferent technques lke clusterng based on relatveness of the document to the search texts. Apart from ths, makng users to dentfy level of relatveness of the clusters and documents ranks n a graph tree structure. From varous evaluatons carred out the performance of the system found to be good comparatvely to other systems n bomedcal doman. REFERENCES [1] Jayanth Mancassamy and P. Dhavachelvan, Metrcs based performance control over text mnng tools n bonformatcs, ACM Portal, Pages , January [2] Jayanth Mancassamy and P. Dhavachelvan, Based Accuracy Perpetuaton for Bonformatcs Sequence Analyss Tools, Internatonal Journal of Recent Trends n Engneerng (IJRTE) - Fnland, pp , May

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