Comparison them on user’s request. It includes the most

Comparison of Information Retrieval Models AbstractInformationretrieval is an emerging field of computer science that is based on the storageof documents and retrieving them on user’s request. It includes the mostessential task of retrieving relevant document according to the requestedquery.

For this task efficient and effective retrieve models have been made andproposed. Our survey paper sheds light on some of these information retrievalmodels. These models have been built for different datasets and purposes. Ahealthy comparison among these models is also shown Keywords: Information retrieval, retrievalmodels. IntroductionHugeamount of information is available in electronic form and its size iscontinuously increasing. Handling information without any information retrievalsystem would be impossible.

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As the size of data increases researchers startpaying attention on how to obtain or extract relevant information from it.Initially much of the information retrieval technology was based onexperimentation and trial error.  Managingthe increasing amount of textual information available in electronic formefficiently and effectively is very critical. Different retrieval models wereformed based on different terminologies to manage and extract information.

Information is mostly stored in form of documents. The main purpose of theseretrieval systems is to find information needed. An information retrievalsystem is a software program that stores and manages information on documents,often textual documents but possibly multimedia. The system assists users infinding the information they required. A perfect retrieval system wouldretrieve only the relevant documents but practically it is not possible asrelevance depends on the subjective opinion of the user. Basic patternsof models:Almostevery retrieval model includes following basic steps: Document Content representation Query representation Query and collection comparison Representation of results  Figure 1 information retrieval process (Hiemstra, November 2009)       Many models represent documents inindexed form as it is efficient approach. Different algorithms are used anddeveloped especially for indexing purpose as better the data is stored moreaccurately and efficiently it is retrieved.  Query formulation is the next important step.

User tries to search data using keywords or phrases. In order to search thesephrases in indexed collection, the query must be present in same form. Indexingcan be done by different ways according to content representation of both thedocuments in the collection and the user query. (Cerulo,2004) (Hiemstra, November 2009)Resultsof any retrieval system depend on its comparison algorithm therefore itdetermines the accuracy of the system. The better the comparison better theresults are obtained. A list of documents is obtained as the outcome thiscomparison that can be relevant or irrelevant.

The main objective of aretrieval model is to measure the degree of relevance of a document withrespect to the given query. (Paik, August 13,2015)The rankof relevant documents is higher as compared to irrelevant documents and theyare shown at the top of the list to minimize user time and efforts spend insearching the documents  The paper is divided in different sectionswith each section explaining different models & their results with theiradvantages and limitations.RetrievalModelsExact match models This modellabels the documents as relevant or irrelevant.

It is also known as Boolean Model, the earliest and theeasiest model to retrieve documents. It uses logical functions in the query toretrieve the required data. George Boole’s mathematical logic operators arecombined with query terms and their respective documents to form new sets ofdocuments. There are three basic operators AND (logical product) OR (logicalsum) and NOT (logical difference)(Ricardo Baeza-Yates, 2009). The resultant of AND operator is a set ofdocuments smaller than or equal to the document sets of any of the terms. ORoperator results in a document set that is bigger than or equal to the documentsets of single terms. Booleanmodel gives users a sense of control over the system.

It distinguishes betweenrelevant and irrelevant documents clearly if the query is accurate. This modeldoes not rank any document as the degree of relevance is totally ignored. Thismodel either retrieves a document or not, that might cause frustration for enduser. Region modelsAnextension of the Boolean model that reason about arbitrary parts of textualdata, called segments, extents or regions. A region might be a word, a phrase,a text element such as a title, or a complete document. Regions are identifiedby a start position and an end position.

Region systems are not restricted toretrieving documents. Theregion models did not have a big impact on the information retrieval researchcommunity, not on the development of new retrieval systems. The reason for thisis quite obvious: region models do not explain in anyway how search resultsshould be ranked.

In fact, most region models are not concerned with ranking atall; one might say they – like the relational model – are actually data modelsinstead of information retrieval models. (Mihajlovi´) Ranking ModelsBooleanmodels may skip important data as they do not support ranking mechanism.Therefore there was a need to introduce ranking algorithms in retrieval system.The results are ranked on the basis of occurrence of terms in the queries. Someranking algorithms depend only on the link structure of the documents whilesome use a combination of both that is they use document content as well as thelink structure to assign a rank value for a given document. (Gupta, 2013) Similaritymeasures/coefficientUsingdocument sets and query, a similarity measure, compare them and the documentswith more similarities are returned to the user. Many methods are user tomeasure the similarity that are cosine similarity, tf-idf etc.

CosinesimilarityThecosine similarity compute the angles between the vectors in n dimensionalspace. The cosine similarity in d documents and d’ is given by 🙁 d * d’ ) / | d | * | d’ | The performance of retrieval vector base model can be improved byutilizing user-supplied information of those documents that are relevant to thequery in question. (Kita, oct 1 , 2000) VaibhavKant Singh, Vinay Kumar Singh (Vaibhav KantSingh, 2015) describes vector space model for information retrieval.

TheVSM provide a guide to the user that are more similar and have moresignificance by calculate the angle between query and the terms or thedocuments. Here documents are represented as term-vectors d = (t1,t2, t3………tn)Where ti=1<=i<=t tiis non-negative value and denotes the term i occurrences on document someimportant measures of vector space model are as follows {0,1}.ProbabilisticmodelTheprobabilistic model is based on probability ranking principle. Some statisticsare involved for event's probability estimation that tells whether the documentretrieve is relevant or non-relevant in accordance with information need.Probabilistic models employ the conditional probability under occurrence of theterms. Probabilistic model state that the retrieval system rank the set ofdocuments according to the probability which is relevant to the query with allthe given evidences. The documents are ranked according to probabilities indecreasing order.

The term-index of term weight words are in binaryrepresentation.Bayesiannetwork ModelBayesiannetwork models (BNM) is acyclic graphical model which means it does not have adirected path but deals with random variables. BNM contains a set ofrandom-variables and the conditional probability dependencies between them. Itis also known as belief networks, casual nets etc. BNM ranks the documents byusage of multiple evidences in order to compute conditional probability.

Probability distribution presentation uses graphical approach to analysescomplex conditional assumptions that are independent.InferenceNetwork ModelIninference retrieval model the random-variables concerned with four layers ofnodes that are a query node, set of document nodes, representation nodes andindex word nodes. The random-variables are represents as edges in inferencenetwork retrieval model.  All the nodesin this model represents random-variables with binary variables {0, 1}.

Figure 1 simplified inference networkmodel (Hiemstra, November 2009)Languagebased models:Languagebased models are the type of retrieval models based on the idea of speechrecognition. Speech recognition depends on two main and unique models that arethe acoustic-model and the language model. It is computed for each collectioncontaining set of documents and based on terms. Ranking of documents are doneby probability generalization of query.

 AlternativeAlgebraic modelIn thisretrieval model we further discuss two models that are latent semantic indexingand neural network modelLatentSemantic IndexingLatentSemantic indexing (LSI) helps accurate retrieval information in large database.The similarity of the documents depends on the contexts of the existing and notexisting words. LSI comprises the idea of singular value decomposition (SVD)and vector space model.

Latent semantic indexing only takes the documents whichhave semantic similarity i-e having same topic, but they aren’t similar in thevector space and then represents in reduced-vector-space having highestsimilarity. To compute LSI by using SVD a matrix A is decomposed into further 3matrices A = U?V TWhere:? isdiagonal matrixU is anorthogonal matrix andV istranspose of an orthogonal matrixJinWang et all (Jin Wang, 9May2012) proposesa model which uses bag of word model for the analysis of human motions in videoframe.Ontology-basedInformation RetrievalThe mostemerging field if information retrieval and extraction now a days isontology-based information retrieval (OBIE).

OBIE is defined as the use ofontologies in order to retrieve information. Ontology means theconceptualization specification of the terms or the words. Ontologies areparticular domain-specific generally so that it means different domains withdifferent ontologies. As they are domain-specific so they have relationshipbetween the class and the entities. They are application dependent. On thebasis of similarities and dissimilarities an ontology-tree is hierarchalrepresentation of classes or entities and their relationship between differentgrouping and classification of entities.

Figure 2 Ontology based information extraction (Ritesh Shah, February 2014) NeuralNetwork ModelNeuralnetwork models are the models that consists of interconnected neurons and haslabelled and directed graphical structure. Neural networks graphs has somenodes that perform some calculations in order to get output. Directed graphscontains nodes or vertices and they also have some connections that connectsthe nodes while labelled graphs are the graphs in which all the connectionshave some label to identify the properties of all connections. The nodes in thegraph behaves as processing unit , edges behaves as synaptic connection andsome weights are also assigned to edges in the graph in neural network model.

  In information retrieval the neural networkmodels contains query-term nodes and document-term nodes. The query-term nodesinitiate the retrieval process by sending the signals to the document-termnodes. The document-term nodes then sends signals to document nodes. IgorMOKRIŠ, Lenka SKOVAJSOVÁ (Igor MOKRIŠ, 2006)describes the neural network retrieval model in the “Slovak” language.   Conclusion:DifferentInformation-retrieval techniques are discussed with advantages and disadvantagesin this survey paper.

Each model has its own different criteria to extract therelevant document for user’s requested query. So we came to the point that fewmethods do best for some applications while few do best for other applicationsin data retrieval. Every method has its own criteria to extract and deal withthe given query for a certain information need.

Information-retrieval systemsare being used in different organizations and still the new-model are beingworked upon to get relevant results. Model Related work Methods Advantages limitations Exact match Model            i.            David E. Losada           ii.            Set theory based and Boolean algebra       iii.            Representation of query by Boolean expression       iv.            Terms combined with operators AND,OR,NOT         v.

            Proximity       vi.            Stemming i.                     Easy to implement ii.                    Exact match model iii.                  Computationally efficient   i.

                     No term weighting used in document and query ii.                    Add too much complexity and detail iii.                  Difficulty for end-users to form a correct Boolean query iv.                  No ranking v.                    No partial matching Vector space model   i.                     Waiting scheme used ii.                    Cosine similarity iii.                  Rank documents by similarity i.

                     Improve retrieval performance by term weighting ii.                    Similarity can be used for different elements i.                     Term independence assumption ii.

                    Users cannot specify relationships between terms Probabilistic Model   i.                     Probability rank principle based ii.                    relevance and non-relevance based of data i.                     Ranking of document ii.                    Does not consider index inside a document i.                     Binary word-in-doc weights ii.                    Independence of terms iii.

                  Only partial ranking of documents iv.                  Prior knowledge based Language based models   Probability estimation of events in text Query likelihood model Speech recognition Term based for each document in entire collection Length normalization of term frequencies     Data sparsity Bayesian network Model       directed graphical model random variable relationship is captured by directed edges Deals with noisy data Describe interaction between query and document space Query specification based on Boolean expressions Expensive Computation Bad performance for small collection Inference Network Model     Random-variables concerned with query ,set of document and index words Provide a framework with possible strategies of Rankin used Boolean query formulation Latent Semantic Indexing     Concept based retrieval of text Use SVD Retrieval of the documents even if there is no share of keyword in the query Solves problem of ambiguities(polysemy & synonymy) Expensive Works on small collection Ontology-based Information Retrieval              i.            Entities classification based in hierarchal manner         ii.            Keyword matching based Capability to reuse and share of ontology with other applications High time consumption Difficulties come in creating ontological-tree Addition of new concept in existing ontology require considerable time and effort Neural Network Model     Neural based Weights assigned to edge of neurons Easy to use but requires some statistical trainings Deals with large collection of data Detect relationship between query and retrieve documents Difficult to design expensive Complicated in nature Does not deal with small documents ReferencesCerulo, G.

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