research article semantic annotation for biological...

Research ArticleSemantic Annotation for Biological InformationRetrieval System

Mohamed Marouf Z. Oshaiba,1 Enas M. F. El Houby,2 and Akram Salah1

1Computer Science Department, Faculty of Computers and Information, Cairo University, Dr. Ahmed Zewail Street,Orman, Giza 12613, Egypt2Engineering Division, Systems & Information Department, National Research Centre, El Buhouth Street, Dokki, Cairo 12311, Egypt

Correspondence should be addressed to Mohamed Marouf Z. Oshaiba; [email protected]

Received 30 June 2014; Revised 17 December 2014; Accepted 19 December 2014

Academic Editor: Tatsuya Akutsu

Copyright © 2015 Mohamed Marouf Z. Oshaiba et al. 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.

Online literatures are increasing in a tremendous rate. Biological domain is one of the fast growing domains. Biological researchersface a problem finding what they are searching for effectively and efficiently. The aim of this research is to find documents thatcontain any combination of biological process and/or molecular function and/or cellular component. This research proposes aframework that helps researchers to retrieve meaningful documents related to their asserted terms based on gene ontology (GO).The system utilizes GO by semantically decomposing it into three subontologies (cellular component, biological process, andmolecular function). Researcher has the flexibility to choose searching terms from any combination of the three subontologies.Document annotation is taking a place in this research to create an index of biological terms in documents to speed the searchingprocess. Query expansion is used to infer semantically related terms to asserted terms. It increases the search meaningful resultsusing the term synonyms and term relationships. The system uses a ranking method to order the retrieved documents based onthe ranking weights. The proposed system achieves researchers’ needs to find documents that fit the asserted terms semantically.

1. Introduction

The emergence of information and communication tech-nologies has drastically changed biological scientific researchprocesses. As a consequence, the variety of biological dataavailable in the public domain is now very diverse and rangesfrom genomic-level high-throughput data to molecular-imaging studies to published research articles. The paradoxof such an expansion is that biological researchers now facethe problem of extracting the specific data they need [1].Furthermore, biological publications hardly follow namingconventions for entities and remain attached to their author’sfavorite names [2].

The Semantic Web technology changes the way searchengines work. The traditional way used by search engines tofind documents is by using the syntax or the key phrase ofthe word. So when the search engine adds some intelligenceby using the semantic search it will improve the searchresults. Semantic technology uses the word in addition to

the meaning of the word, relations with other words, wordroots, and other linguistic synonyms to be understood by thecomputers or people. Metadata is a powerful base used inthe Semantic Web. Each document can contain some relatedwords that help to find it easily in the search process [3].

The semantic technology relies heavily on the formalontologies that structure underlying data for the purposeof comprehensive and transportable machine understanding.Therefore, the success of the semantic technology dependsstrongly on the proliferation of ontologies, which requiresfast and easy engineering of ontologies and avoidance of aknowledge acquisition bottleneck. Conceptual structures thatdefine an underlying ontology are germane to the idea ofmachine processable data on the Semantic Web [4].

Ontology is specification of conceptualization for specificdomain used in knowledge sharing purposes. They are(meta)data schemas, providing a controlled vocabulary ofconcepts, each with explicitly defined and machine process-able semantics. By defining shared and common domain

Hindawi Publishing CorporationAdvances in BioinformaticsVolume 2015, Article ID 597170, 11 pageshttp://dx.doi.org/10.1155/2015/597170

2 Advances in Bioinformatics

theories, ontologies help both people and machines to com-municate concisely, supporting the exchange of semanticsand not only syntax [4].

Biological researchers have spent a lot of time in buildingontologies because of their importance in the field of infor-mation retrieval. They used ontologies and terminologies todescribe their data and turn it into structured and formalizedknowledge. Ontology is used as unified protocol to sharethe knowledge between different resources. One of the mostfamiliar biological ontologies is the gene ontology (GO). Itprovides ontology of defined terms representing gene productproperties. It covers three domains: cellular component (CC),the parts of a cell or its extracellular environment; molecularfunction (MF), the elemental activities of a gene productat the molecular level, such as binding or catalysis; andbiological process (BP), operations or sets of molecularevents with a defined beginning and end, pertinent to thefunctioning of integrated living units: cells, tissues, organs,and organisms as provided in the gene ontology [5].

Document annotation aims at discovering documents inreferences automatically. It is quite useful for many tasksincluding information extraction, classification, text summa-rization, question answering, and literature-based knowledgediscovery. On the other hand, the web as a global informationspace is developing from a web of documents to a web ofdata. Currently, there are billions of publicly available webdata sources of different domains. Biological domain is oneof the rich data domains. Most available biological data arein unstructured format and described without any ontologyconcepts. These data sources are becoming more tightlyinterrelated as the number of links in the form of mappingsgrows [6].

The main objective of this research is to use the semantictechnology in biological field to improve search results. Theproposed system’s purpose is to find documents that containmolecular function or biological process that can be appliedon specific cellular component. The proposed system helpsbiological researchers to find relevant documents to theirasserted terms in high performance. To fulfill this objectiveGO has been decomposed into three subontologies (CC,MF, and BP), which enable the researchers to select thesearched terms from three different subontologies and givethem more flexibility to select any combination of the threesubontologies terms.

An annotation index has been created to speed thesearching process, which annotates each document based ongene ontology biological concepts. In this research annota-tion refers to describe each document with gene ontologyconcepts and synonyms.

The query expansion has been used to increase the pro-posed systems’ intelligence.The inferred terms fromontologyare used to expand query to give an advantage in reachingmore accurate results by retrieving documents that containinferred terms related to asserted terms.

This research consists of the following. (I) Divide the geneontology semantically to its three subontologies (molecularfunctions, cellular component, and biological processes). (II)Annotate the biological documents using the subontologiesconcepts and build an annotated index to refer to documents’

terms and accelerate the time of searching. (III) Searchfor the needed documents in the annotated index usingany combination of the selected subontology terms or anyindividual subontology term.

The remainder of this paper is organized as follows:Section 2 overviews some of related work. Section 3 discussesthe proposed system description and framework levels.Section 4 is an illustrative example of the framework showinginputs and outputs of the proposed system. Section 5 con-ducts experimental results on the proposed system. Section 6is the conclusion and future work.

2. Related Work

A lot of scientific researches have tackled the semantictechnology and its relation with the information retrievalfield. There are several tools that support the retrievalof information based on biological content. In [2] theyIntroduced GeneView, which built upon a comprehensivelyannotated version of PubMed abstracts and openly availablePubMedCentral full texts.The semistructured representationof biological texts enables a number of features extendingclassical search engines. For instance, users may searchfor entities using unique database identifiers. Annotationis performed by a multitude of state-of-the-art text-miningtools for recognizing mentions from 10 entity classes and foridentifying protein-protein interactions. GeneView currentlycontains annotations for >194 million entities from 10 classesfor ∼21 million citations with 271000 full text bodies.

Another tool used to annotate and index biological docu-ments is described in [1]. The key functionality of this systemis to provide a service that enables users to locate biologicaldata resources related to particular ontology concepts. Thesystem’s indexing workflow processes the text metadata ofdiverse resource elements such as gene expression data sets,descriptions of radiology images, clinical-trial reports, andPubMed article abstracts to annotate and index them withconcepts from appropriate ontologies. The system enablesresearchers to search biological data sources using ontologyconcepts.

Using the web crawler to annotate data is also an impor-tant method in annotation and extraction. This techniqueis shown in SOBA (SmartWeb Ontology-Based Annotation)[7] which is a component for ontology-based informationextraction from soccer web pages for automatic population ofa knowledge base that can be used for domain-specific ques-tion answering. SOBA realizes a tight connection betweenthe ontology, knowledge base, and the information extractioncomponent. The originality of SOBA is in the fact that itextracts information from heterogeneous sources such astabular structures, text, and image captions in a semanticallyintegrated way. In particular, it stores extracted informationin a knowledge base and in turn uses the knowledge base tointerpret and link newly extracted information with respectto already existing entities.

In [8], they focus on the query expansion instead ofdocument ranking. The model is parsing each topic intothe event part and the geographic part and use differentontologies to expand both parts, respectively.

Advances in Bioinformatics 3

In [9], the researchers presented a framework for asemantic biological retrieval system using inverted list thateffectively searches and retrieves meaningful results basedon gene ontology. The framework takes two biological termsas input and retrieves the relevant documents as output. Itimproves the search result by expanding the biological termsusing inferred terms (synonyms, parent, and grandparent) toretrieve more meaningful results. The framework uses a spe-cial ranking methodology to give weight to each documentbased on the rank value to help in ordering the result.

Many other researchers used ontologies, annotation, andinformation retrieval system using different techniques [3, 6,10–14].

The previous work is concentrated on the ontologiesthat try to annotate the literature papers, optimize thesearching process, increase the search result using the queryexpansion method, and increase the recall and precision ofthe retrieved document. Our proposed system is a semanticbiological information retrieval system that annotates bio-logical documents using multiontology. It is mainly directedto researchers that need to find documents that containsemantic relationships among the three main concepts inGO (cellular component, biological process, and molecularfunction).The system uses the term’s relations and term’s syn-onyms to give more meaning results. Ranking methodologyis used in this system to help the researchers to find the mostappropriate result first. It is our assumption that the systemwill help the researcher by minimizing the time and effortof searching semantically for any combination of the threesubontologies concepts.

After reviewing several scientific researches that con-centrate on the semantic technology, query expansion, geneontology, and annotation, we conclude that the closerresearches to this research are [1, 9]. Reference [1] is close tothis research from the annotation perspective and semanticperspective. It presents a system for ontology-based anno-tation and indexing of biomedical data. It uses ontology toautomatically expand the initial set of annotations generatedby a concept recognition tool. Reference [9] is close to thisresearch from the semantic perspective. Their framework isa semantic biological retrieval system that depends on geneontology. It uses terms’ synonym, parent, and grandparentto semantically enhance the system results using queryexpansion method.

3. Proposed System

The proposed system is an information retrieval system thatsearches for biological documents relevant to the assertedterms in the annotated index. Since the main purpose ofthis research is to retrieve documents that contain functionand/or process for specific cellular component, it decomposesthe gene ontology to its three subontologies and uses them toannotate the biological documents and builds the annotatedindex. The annotated index can be used as the metadata ofeach document. Semantics has been used in the proposedsystem to enhance the search results by adding terms’ syn-onyms and relations from the gene ontology to the query togain more semantic results. After searching the index and

getting the results, the system ranks the results with specificcriteria. The proposed system contains two levels which arepreprocessing level and search level as shown in Figure 1.

3.1. Preprocessing Level. Preprocessing is the first level inthe proposed framework. It consists of two phases whichare gene ontology phase and annotation phase. In geneontology phase, the gene ontology has been decomposedinto three subontologies, the needed terms’ attributes havebeen extracted from gene ontology and the subontologieshave been normalized into one format. This phase has beenapplied when the system initiated. In annotation phase, theannotated index has been built from corpus documents usingsubontologies concepts and the system has been prepared forthe searching process.

3.1.1. Gene Ontology Phase. The gene ontology providesontology of defined terms representing gene product proper-ties [5]. The proposed system uses the gene ontology version1.2, which is a text file that contains 37,486 gene terms.Figure 2 shows a sample of GO. In this research the GO hasbeen decomposed into three subontologies, normalized andstored in database to fulfill the proposed systems’ objectiveof finding documents that contain function or process thatcan be applied on specific cellular component and make surethat the transaction will be smoother and faster. The GOdecomposition has two main steps.

(i) Extracting the Needed Attributes from Gene Ontology. GOis stored as structured format. Each [Term] in the geneontology contains different attributes that describe the term.Not all attributes in [Term] are needed to the proposedsystem. So the only needed attributes have to be extractedsuch as Term ID, term name, term namespace, term relations,term relation type, term synonyms, and synonyms type. Todecompose GO into three subontologies and extract theneeded terms’ attributes, for each [Term] in the GO textfile, the system checks for its namespace (type) and gets theneeded attributes only then categorize themwith one of threesubontologies (cellular component, molecular function, andbiological process).

(ii) Storing the Needed Attributes in Normalized Database. Inthis step, the data which has been extracted to represent thethree subontologies has been stored in three different tableswith the same format to make the transaction easier andfaster. Each of the three subontology tables are connectedwith other two tables, which are synonyms and relationstables, which will be used later in the query.

3.1.2. Annotation Phase. Corpus, which consists of a lot ofbiological resources, has been annotated with the GO termsand the annotated index has been built which will be usedin searching process. To annotate biological resources, foreach document the system extracts the biological terms basedon the normalized GO database. Special regular expressionshave been used to annotate terms frombiological documents.The used regular expressions are as follows.


Inferred terms + asserted terms

Search level

Query Query expansion RankingRanked

Preprocessing level

CC

BP

MF

Index

Annotation

Resources

Gene

Annotation phaseGene ontology phase

Decomposition

Search results

ontology

documents

Asserted terms Expanded query

Figure 1: The framework of the proposed system.

Figure 2: A sample of GO text file.

(“\\s+” + term + “\\s+”) or (“\\/” + term + “\\s+”) or(“\\s+” + term + “\\.”) or (“\\.” + term + “//s+”) or (“\\∧” +term + “//s+”) or (term) where term is a concept that exists inthe GO and is included in an annotated document. Figure 3shows the normalized GO and the created annotation index.Table 1 describes the variables’ meaning of normalized GOand the created annotation index in database design.

3.2. Search Level. This is the second level of the systemworkflow. In the search level the user can select the searchingterms from different subontologies to start the searchingprocess. The system will expand the user terms by inferringother terms from normalized database that help semanticallyimprove the result set. After retrieving the relevant docu-ments the system will arrange and rank them to match the


Indexes

IndexesIndexes

Indexes

Indexes

Indexes

RelationRID INT(11)GID VARCHAR(10) ParentID VARCHAR(10)

Rtype VARCHAR(45)ParentName VARCHAR(500)

Process Function

Synonym

ComponentFID VARCHAR(10)FName VARCHAR(200)

CID VACHAR(10)CName VARCHAR(200)

PID VARCHAR(10)PName VARCHAR(200)

SID INT(11)GID VARCHAR(45)SName VARCHAR(500)Stype VARCHAR(45)

PaperID VARCHAR(20)PaperTitle VARCHAR(1000)PaperAuthor VARCHAR(45)

Paper

TermsPaperID VARCHAR(20)TermName VARCHAR(500)TermType VARCHAR(45)TermFreq INT(11)

Annotation indexGene ontologynormalized DB

Figure 3: The normalized GO and the created annotation index.

Table 1: The normalized GO and the created index description.

Table name Variable name Variable description

Gene ontologynormalized database

Component CID Cellular component Term IDCName Cellular component name

Function FID Molecular function Term IDFName Molecular function name

Process PID Biological process Term IDPName Biological process name

Relation

RID Relation ID (auto number)GID Gene ID (Term ID in GO)

ParentID Parent Term IDParentName Parent term name

RType Relation type (is a, part of, has part, regulates)

Synonym

SID Synonym ID (auto number)GID Gene ID (Term ID in GO)

SName Synonym name

SType Synonym type (EXACT, NARROW, BROAD,RELATD)

Annotated index

PaperPaperID Paper ID (paper name)PaperTitle Paper title

PaperAuthor Paper author

Term

PaperID Paper ID (paper name)TermName Term nameTermType Term typeTermFreq Term frequency


Selectingasserted terms

Expanding userquery

Searching inannotation

index

Documentunion

Documentrearranging

Documentranking

CC MF BP(i) (j) (k)

i: 1⇢c j: 1⇢d k: 1⇢e

· · ·

· · ·

...

...

...

......

...

= Wx

= W1

= W2

= W3

Where x is the union numberof retrieved documents

D2D1D3

D2

D1

D3

Dx

Dx

D2D1 D3 Dx

TccScc 1Scc 2

Rcc 1Rcc 2

Rcc m

Scc n

Smf 2

TmfSmf 1

Rmf 1Rmf 2

Rmf b

Smf a

Rbp y

TbpSbp 1

Sbp 2

Sbp xRbp 1

Rbp 2

Tbp SbpRbpTmf SmfRmfTcc SccRcc




Figure 4: The different phases of search.

user priorities. The different phases of search are shown inFigure 4.

3.2.1. Selecting Asserted Terms. The user can find and selectsearched terms from 3 different dropdown lists for 3 differentsubontologies terms. The user has the ability to choose theasserted terms and search for documents that either havecellular components, molecular functions, biological process,or any combinations of them.

3.2.2. Expanding the User Query. The system infers therelevant terms to the asserted terms. For each asserted term,it extracts the term’s synonyms and the term’s relations fromthe normalized GO database. The inferred terms are used toimprove the search results. For example, if the user wantsto search for a biological process called “brain development”so the basic query will be “select PaperID from terms where

TermName = brain development.” But when applying thequery expansion, new queries will be created that con-tain the inferred term. The queries will be “select PaperIDfrom terms where TermName = organ development,” “selectPaperID from terms where TermName = brain development,”and “select PaperID from terms where TermName = centralnervous system development.” The reason is that the “organdevelopment” is a parent of the “brain development,” and“brain development” is part of the process called “centralnervous system development.” So there is “is a” relationshipbetween the “brain development” and “organ development”and a “part of ” relationship between the “brain development”and “central nervous system development.” Each term frominferred terms will be in separated query.

3.2.3. Searching in the Annotated Index. The system startssearching in the annotated index for documents containing


any combination of terms (𝑇), synonyms (𝑆), and relations(𝑅). Suppose that the three terms are cellular component term(𝑇cc), molecular function term (𝑇mf), and biological processterm (𝑇bp). The system will search for all synonyms andrelations that related to each term and create list of synonymswith synonyms’ type and relations with relations’ type foreach term.The synonyms’ lists are cellular component terms’synonyms (𝑆cc), molecular function terms’ synonyms (𝑆mf),and biological process terms’ synonyms (𝑆bp). Also list ofrelations which are cellular component terms’ relations (𝑅cc),molecular function terms’ relations (𝑅mf), and biologicalprocess terms’ relations (𝑅bp) will also be created. Aftersearching, the system returns documents that contain terms,synonyms, and relations with the frequencies in each docu-ment.The result set is a number of documents that have theseterms (i.e., documents per biological terms), but the searchresults are needed in another view; the view of documentsper number of biological content, which are asserted termsand inferred terms or any combinations of them.

3.2.4. Document Union. When the researcher searches forany combination of the three subontologies, there is aprobability to get redundant documents. That is because thesystem retrieves documents based on just one term, synonym,or relation. So the search result may contain one documentthat retrieved twice for different terms. So, in this phase thesystem gets the union of all the documents to eliminate theredundant documents. After finishing this phase, the systemwill get only unique documents with unrepeated results.

3.2.5. Document Rearranging. The system rearranges thedocuments using the following technique; for each doc-ument there is metadata structure that looks like this[𝑇cc, 𝑇mf, 𝑇bp, 𝑅cc, 𝑅mf, 𝑅bp, 𝑆cc, 𝑆mf, 𝑆bp] where each entry inthis list is the frequency of term in represented documentwith considering that each entry for synonym or relation is alist to represent different synonyms or relations found in thedocument.

3.2.6. Document Ranking. The retrieved documents areranked using special criteria; these criteria can be changedby the users of the system. As mentioned before, this systemsearches for documents using asserted terms in additionto inferred terms. These terms which are biological terms,terms’ synonyms, and terms’ relations have been used tocalculate the ranking value of each retrieved document. Thedocuments will be arranged according to the ranking valuesof these documents. Weights have been assigned for each ofthese terms. According to assigned weights each documentcan get ranked value using the following formula:

𝐷Rank = 𝑊𝑇 (𝑇cc + 𝑇mf + 𝑇bp)

+

𝑚

∑

𝑖=1

(𝑆𝑖∗𝑊syn (𝑖)) +

𝑛

∑

𝑗=1

(𝑅𝑗∗𝑊rel (𝑗)) ,

(1)

where 𝐷Rank is the document ranking value and “𝑊𝑇” is the

searched term’s weight; it is the weight of any term type (cc,mf, or bp).

𝑇cc, 𝑇mf, and 𝑇bp are the frequencies of the cellular com-ponents, molecular function, and biological process terms ina document, respectively.𝑊syn(𝑖) are the synonyms weights where 𝑖 can take value

from 1 to 4 to represent different types of synonyms in geneontology which are four types; they will be assigned weightvalues as follows.

𝑊syn(1) is the weight for “NARROW” synonymsdefined in GO as “the term is narrower or moreprecise than the term name.”𝑊syn(2) is the weight for “EXACT” synonyms definedin GO as “an exact equivalent, interchange with theterm name.”𝑊syn(3) is the weight for “RELATED” synonymsdefined in GO as “terms are related in some way notcovered above.”𝑊syn(4) is the weight for “BROAD” synonyms definedin GO as “the synonym is broader than the termname.”

𝑆𝑖is the frequencies of these 4 different synonym types in

the document.𝑊rel(𝑗) are the relations weights where 𝑗 can take value

from 1 to 4 to represent different types of relations in geneontology which are four types; they will be assigned weightvalues as follows.

𝑊rel(1) is the weight for “is a” relation.𝑊rel(2) is the weight for “has part” relation.𝑊rel(3) is the weight for “part of ” relation.𝑊rel(4) is the weight for “regulates” relation.

𝑅𝑗is the frequencies of different types of relations in the

document.After calculating the documents’ ranking values using the

previous formula, the system rearranges the documents fromhigher to lower according to𝐷rank.

The proposed system gives the users the flexibility inranking documents according to his/her criteria by allowinghim/her to enter customized weights. So the system hastwo options for ranking: either using the system defaultranking values or using customized ranking based on the usercustomized values as shown in Figure 5.

The default values are assigned assuming that 𝑊rel(1) >𝑊rel(2) > 𝑊rel(3) > 𝑊rel(4) for relations’ weights and𝑊syn(1) > 𝑊syn(2) > 𝑊syn(3) > 𝑊syn(4) for synonyms’weights. This research also assumes that 𝑊

𝑇> 𝑊Syn >

𝑊Rel.These assumptions suppose that the biological terms areexactly the researcher’s needs, so they take the higher value.Synonyms get higher rank than relation because synonym isa representation of the term with difference in vocabulary orletters; it is useful in retrieving documents by authors who usedifferent wording in reference to the same term. So it will getthe second priority. Finally the relation will take the lowestweight because it is very generic to the term but it is alsorelated to the term.


Figure 5: Snapshot from program interface for users’ customizedranking criteria window.

Figure 6: Snapshot from program interface for the three dropdowninput lists.

4. Illustrative Example

When the researcher wants to search for the asserted termsin the proposed system, he/she can select three differentterms from three different dropdown lists for cellular compo-nents, molecular function, and biological process as shown inFigure 6. So the researcher has the ability to search for

(i) one term (cc or mf or bp),(ii) two terms (any combination of the three compo-

nents),(iii) three terms.

So when the terms have been chosen, the system startsgathering the inferred synonyms and relations that relatedto the chosen terms from the gene ontology. Then the queryhas been expanded using the inferred terms to add semanticvalue to the searching process.This example has been appliedon “CRAFT” corpus [15] to clarify how the system works.Suppose that the user searches for a function called “binding”in “CRAFT” corpus; the system will create the metadata filefor the searched term like the following:

Term Name: bindingID: GO:0005488Type: FunctionRelations: molecular functionSynonyms: ligand NARROW

Then the system starts searching in the annotated indextomatch themetadata file with the annotated index fields andthe result will be as shown in Table 2.

Table 2: Result of term existence in documents based on cc, mf, andbp.

cc mf bpTerms 0 42 0Relations 0 0 0Synonyms 0 10 0Number of founddocuments (union) 45 documents

Table 3: List of arranged documents based on the ranking criteria.

Document name Document rankingPUBMED (10).txt 340PUBMED (1).txt 320PUBMED (58).txt 270PUBMED (19).txt 170PUBMED (35).txt 140PUBMED (43).txt 110PUBMED (4).txt 90PUBMED (36).txt 80PUBMED (55).txt 80PUBMED (56).txt 80

At the end of the searching the results will be a collec-tion of related documents, and the system will rank thesedocuments based on the frequency of the terms (cc, mf, bp,synonyms, and relations) in each document and the weightof each term. So the more ranked documents are the higherorder in the list. And the final result of the documents nameswith the ranking value is shown in Table 3. Supposing that𝑊𝑇= 10.0,𝑊Syn(1) = 8.0,𝑊Syn(2) = 7.0,𝑊Syn(3) = 6.0, and

𝑊Syn(4) = 5.0,𝑊rel(1) = 4.0,𝑊rel(2) = 3.0,𝑊rel(3) = 2.0, and𝑊rel(4) = 1.0.

5. Experimental Results

Aset of experiments are performed to study the efficiency andeffectiveness of the proposed system. All test cases are testedusing gene ontology version 1.2 and CRAFT corpus version1.0.

The system tries to improve the searching process usingannotation technique. Annotation technique extracts thebiological terms involved in the gene ontology from doc-uments. Table 4 and Figure 7 show the relation betweennumber of documents and annotation time in seconds tobuild annotated index for CRAFT corpus.

Using annotation, the search process will be done inannotated index instead of searching in thewhole documents.Since the size of annotated index is smaller than the corpussize, the search process will be faster. Table 5 and Figure 8show a comparison between documents size in bytes beforeannotation and the size of annotated index for the corpus.

One of the most important aspects in evaluating retrievalsystems is searching time. Table 6 and Figure 9 show therelation between the average times of searching in the


Table 4: Annotation time based on the number of documents.

Number of documents Annotation time (seconds)10 33.69820 72.85930 195.38140 143.11150 256.96560 327.65867 351.658

Table 5: Comparison between documents’ sizes and annotatedindex size.

Number ofdocuments

Document size(bytes)

Annotated index size(bytes)

10 412,161 21,51020 869,791 44,39330 1,316,576 69,67640 1,686,597 90,16550 2,145,198 113,12060 2,592,895 140,52667 2,908,446 156,294

Table 6: Searching time via the number of documents.

Number of documents Searching time in second10 0.18220 0.22030 0.22740 0.17150 0.15360 0.19067 0.191

Table 7: Extracted terms.

Cellular component term Chromosome

SynonymsChromatid (related)Interphase chromosome (narrow)Prophase chromosome (narrow)

Relations Intracellular non-membrane-boundedorganelle (is a)

annotated index in seconds with the increasing number ofdocuments used in building the annotated index.

Test Case 1. In this test case the system will take a cellularcomponent term only as an input to verify the one termsearch process as shown in Tables 7 and 8.

Test Case 2. In this test case the system will take a cellularcomponent term and a function term as an input to verifythe two terms search process as shown in Tables 9 and 10.

Test Case 3. In this test case the system will take a cellularcomponent term, a molecular function term, and a biological

Annotation time

Number of documents10 20 30 40 50 60 67

Ann

otat

ion

time (

s)

0

100

200

300

400Time for building annotation index

Figure 7: Annotation time versus number of documents.


3,000,000

2,250,000

1,500,000

750,000

0

Size

(byt

e)

Documents size (byte)Database size (bytes)

Size of documents

Figure 8: Comparison between documents’ sizes and annotatedindex size.

0.26

0.23

0.20

0.17

0.14

Line 1


Sear

chin

g tim

e (s)

Figure 9: Searching time via number of documents.


Table 8: Count of relevant documents.

Cellular componentTerms 31Relations 0Synonyms 2

Number of relevant documents31


(a)

Cellular componentterm Nucleus

Synonyms Cell nucleus (exact)

Relations Intracellular non-membrane-boundedorganelle (is a)

(b)

Functionterm Alkaline phosphatase activity

Synonyms

Alkaline phenyl phosphatase activity (exact)alkaline phosphohydrolase activity (exact)Alkaline phosphomonoesterase activity (exact)glycerophosphatase activity (broad)orthophosphoric-monoester phosphohydrolase(alkaline optimum) (exact)Phosphate-monoester phosphohydrolase (alkalineoptimum) (exact) phosphomonoesterase activity(broad)

Relations Phosphatase activity (is a)


Component FunctionTerms 30 1Relations 0 2Synonyms 5 0


process term as an input to verify the three terms searchprocess as shown in Tables 11 and 12.

6. Conclusion and Future Work

Wehave developed ontology-based information retrieval sys-tem that retrieves relevant documents to the searching inputswith high performance using annotated index. The systemdecomposes the GO semantically into three subontologies.The system retrieves related documents by focusing onlyon extracted biological terms based on GO and buildingannotated index using these terms.The system has improvedthe performance of information retrieving method, since ithas used the biological ontology to get the synonymous andthe relations of the asserted terms in the query to retrieveall relevant documents that contain the terms and their


(a)

Cellular component term HostSynonyms Host organism (exact)Relations Other organisms (is a)

(b)

Function term BindingSynonyms Ligand (narrow)Relations Molecular function (is a)

(c)

Process term Growth

Synonyms Growth pattern (related)Nondevelopmental growth (related)

Relations Biological process (is a)


Component Function ProcessTerms 5 42 36Relations 3 0 0Synonyms 0 10 2


synonyms and relations.The system has used ranking criteriato rank the retrieved documents. Also researchers have theflexibility to include their ranking values to help them inordering retrieved documents according to their criteria.Thissystem has been tested using GO version 1.2 and CRAFTversion 1.0.

Conflict of Interests

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

References

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