Information Retrieval Model Research Articles

BAYESIAN RETRIEVAL USING A SIMILARITY-BASED LEMMATIZER

The present paper describes a Bayesian network approach to Information Retrieval (IR) from Web documents. The network structure provides an intuitive representation of uncertainty relationships and the embedded conditional probability table is used by inference algorithms in an attempt to identify documents that are relevant to the user's needs, expressed in the form of Boolean queries. Our research has been directed in constructing a probabilistic IR framework that focus on assisting users to perform Ad-hoc retrieval of documents from the various domains such as economics, news, sports, etc. Furthermore, users can integrate feedback regarding the relevance of the retrieved documents in an attempt to improve performance on upcoming requests. Towards these goals, we have expanded the traditional Bayesian network IR system and tested it on several Greek web corpora on different application domains. We have developed two different approaches with regards to the structure: a simple one, where the structure is manually provided, and an automated one, where data mining is used in order to extract the network's structure. Results have depicted competitive performance against successful IR models of different theoretical backgrounds, such as the vector space utilizing tf-idf and the probabilistic model of BM25 in terms of precision-recall curves. In order to further improve the performance of the IR system, we have implemented a novel similarity-based lemmatization framework, reducing thus the ambiguity posed by the plethora of morphological variations of the languages in question. The employed lemmatization framework comprises of 3 core components (i.e. the word segregation, the data cleansing and the lemmatization modules) and is language-independent (i.e. can be applied to other languages with morphological peculiarities and thus improve Ad-hoc retrieval) since it achieves the mapping of an input word to its normalized form by employing two state-of-the-art language independent distance metric models, meaning the Levenshtein Edit distance and the Dice coefficient similarity measure, combined with a language model describing the most frequent inflectional suffixes of the examined language. Experimental results support our claim on the significance of this incorporation to Greek texts web retrieval as results improve by a factor of 4% to 11%.

Ontology-Oriented Inference-Based Learning Content Management System

The world is witnessing the electronic revolution in many fields of life such as health, education, government and commerce. E-learning is considered one of the hot topics in the e-revolution as it brings with it rapid change and greater opportunities to increase learning ability in colleges and schools. The fields of Learning Management Systems (LMS) and Learning Content Management Systems (LCMS) are full of open source and commercial products, however LCMS systems in general inherit the drawbacks of information system such as weakness in user expected information retrieval and semantic modelling and searching of contents & courses. In this paper, we propose a new prototype of LCMS that uses the Semantic Web technologies and Ontology Reasoner with logical rules, as an inference engine to satisfy the constraints and criteria specified by a user, and retrieves relevant content from the domain ontology in an organized fashion. This enables construction of a user-specific course, by semantic querying for topics of interest. We present the development of an Ontology-oriented Inference-based Learning Content Management System OILCMS, its architecture, conception and strengths.

Effects of Terms Recognition Mistakes on Requests Processing for Interactive Information Retrieval

In this work the author proposes to describe a new model of information retrieval. They studied the effects of using weighted index terms recognition mistakes in a document indexing system and evaluate indexing performance when short and long requests are used. The effect of weighting index terms in the document collection and in the requests is analyzed. Given the typical requests submitted to term indexing system, it seems easy to consider that the effects of term recognition mistakes in user requests must be severely destructive on the effectiveness of the system. The experimental study reported in this paper shows that the use of classical term Indexing technique for processing this kind of request is robust to considerably high levels of term recognition mistakes, in particular for long requests. Moreover, both standard pertinence feedback and pseudo pertinence feedback can be employed to improve the effectiveness of user request processing.

Model of Recommendation System for for Indexing and Retrieving the Learning Object based on Multiagent System

This paper proposes a multiagent system application model for indexing, retrieving and recommendation learning objects stored in different and heterogeneous repositories. The objects within these repositories are described by filled fields using different metadata standards. The searching mechanism covers several different learning object repositories and the same object can be described in these repositories by the use of different types of fields. Aiming to improve accuracy and coverage in terms of recovering a learning object and improve the signification of the results we propose an information retrieval model based on the multiagent system approach and an ontological model to describe the knowledge domain covered. Keywords: AI in education, multi-agent systems, learning objects, recommendation systems.

Model of Recommendation System for for Indexing and Retrieving the Learning Object based on Multiagent System

This paper proposes a multiagent system application model for indexing, retrieving and recommendation learning objects stored in different and heterogeneous repositories. The objects within these repositories are described by filled fields using different metadata standards. The searching mechanism covers several different learning object repositories and the same object can be described in these repositories by the use of different types of fields. Aiming to improve accuracy and coverage in terms of recovering a learning object and improve the signification of the results we propose an information retrieval model based on the multiagent system approach and an ontological model to describe the knowledge domain covered.Keywords: AI in education, multi-agent systems, learning objects, recommendation systems.

Modeling and mining term association for improving biomedical information retrieval performance

BackgroundThe growth of the biomedical information requires most information retrieval systems to provide short and specific answers in response to complex user queries. Semantic information in the form of free text that is structured in a way makes it straightforward for humans to read but more difficult for computers to interpret automatically and search efficiently. One of the reasons is that most traditional information retrieval models assume terms are conditionally independent given a document/passage. Therefore, we are motivated to consider term associations within different contexts to help the models understand semantic information and use it for improving biomedical information retrieval performance.ResultsWe propose a term association approach to discover term associations among the keywords from a query. The experiments are conducted on the TREC 2004-2007 Genomics data sets and the TREC 2004 HARD data set. The proposed approach is promising and achieves superiority over the baselines and the GSP results. The parameter settings and different indices are investigated that the sentence-based index produces the best results in terms of the document-level, the word-based index for the best results in terms of the passage-level and the paragraph-based index for the best results in terms of the passage2-level. Furthermore, the best term association results always come from the best baseline. The tuning number k in the proposed recursive re-ranking algorithm is discussed and locally optimized to be 10.ConclusionsFirst, modelling term association for improving biomedical information retrieval using factor analysis, is one of the major contributions in our work. Second, the experiments confirm that term association considering co-occurrence and dependency among the keywords can produce better results than the baselines treating the keywords independently. Third, the baselines are re-ranked according to the importance and reliance of latent factors behind term associations. These latent factors are decided by the proposed model and their term appearances in the first round retrieved passages.

A novel neighborhood based document smoothing model for information retrieval

In this paper, a novel neighborhood based document smoothing model for information retrieval has been proposed. Lexical association between terms is used to provide a context sensitive indexing weight to the document terms, i.e. the term weights are redistributed based on the lexical association with the context words. A generalized retrieval framework has been presented and it has been shown that the vector space model (VSM), divergence from randomness (DFR), Okapi Best Matching 25 (BM25) and the language model (LM) based retrieval frameworks are special cases of this generalized framework. Being proposed in the generalized retrieval framework, the neighborhood based document smoothing model is applicable to all the indexing models that use the term-document frequency scheme. The proposed smoothing model is as efficient as the baseline retrieval frameworks at runtime. Experiments over the TREC datasets show that the neighborhood based document smoothing model consistently improves the retrieval performance of VSM, DFR, BM25 and LM and the improvements are statistically significant.

Cross-language information retrieval models based on latent topic models trained with document-aligned comparable corpora

In this paper, we study different applications of cross-language latent topic models trained on comparable corpora. The first focus lies on the task of cross-language information retrieval (CLIR). The Bilingual Latent Dirichlet allocation model (BiLDA) allows us to create an interlingual, language-independent representation of both queries and documents. We construct several BiLDA-based document models for CLIR, where no additional translation resources are used. The second focus lies on the methods for extracting translation candidates and semantically related words using only per-topic word distributions of the cross-language latent topic model. As the main contribution, we combine the two former steps, blending the evidences from the per-document topic distributions and the per-topic word distributions of the topic model with the knowledge from the extracted lexicon. We design and evaluate the novel evidence-rich statistical model for CLIR, and prove that such a model, which combines various (only internal) evidences, obtains the best scores for experiments performed on the standard test collections of the CLEF 2001---2003 campaigns. We confirm these findings in an alternative evaluation, where we automatically generate queries and perform the known-item search on a test subset of Wikipedia articles. The main importance of this work lies in the fact that we train translation resources from comparable document-aligned corpora and provide novel CLIR statistical models that exhaustively exploit as many cross-lingual clues as possible in the quest for better CLIR results, without use of any additional external resources such as parallel corpora or machine-readable dictionaries.

An Ontology-Based Information Retrieval Model for Vegetables E-Commerce

With the rapid increment of the information on the web, traditional information retrieval based on the keywords is far from user's satisfaction in recall and precision. In order to improve the recall ratio and the precision radio of IR engine in the vegetables e-commerce, an information retrieval model based on the vegetables e-commerce ontology is presented in this paper, vegetables e-commerce ontology was constructed by gathering and the analyzing vegetables e-commerce domain information on the web. The vegetables e-commerce ontology is composed of some kinds of vegetable classes and hierarchy relationship of vegetables classes. In the process of information retrieval, domain ontology helps to index information and information inference. An ontology-based information retrieval model is implemented, and which has more functions than the keyword-based web information retrieval engines. The experiment results show that the recall ratio and the precision ratio of ontology-based information retrieval model are higher than that of the information retrieval engine based on keyword at a certain extent.

Aggregated Search in XML Documents

In this paper, we are interested in aggregated search in structured XML documents. We present a structured information retrieval model based on possibilistic networks. Relations terms-elements and elements-document are modeled through possibility and necessity. In this model, the user's query starts a process of propagation to recover the elements. Thus, instead of retrieving a list of elements that are likely to answer partially the user's query, our objective is to build a virtual elements that contain relevant and non-redundant elements, that are likely to answer better the query that elements taken separately. Indeed, the possibilistic network structure provides a natural representation of links between a document, its elements and its content, and allows an automatic selection of relevant and non-redundant elements. We evaluated our approach using a sub-collection of INEX (INitiative for the Evaluation of XML retrieval) and presented some results for evaluating the impact of the aggregation approach.

Relating ontologies with a fuzzy information model

More people than ever before have access to information with the World Wide Web; information volume and number of users both continue to expand. Traditional search methods based on keywords are not effective, resulting in large lists of documents, many of which unrelated to users’ needs. One way to improve information retrieval is to associate meaning to users’ queries by using ontologies, knowledge bases that encode a set of concepts about one domain and their relationships. Encoding a knowledge base using one single ontology is usual, but a document collection can deal with different domains, each organized into an ontology. This work presents a novel way to represent and organize knowledge, from distinct domains, using multiple ontologies that can be related. The model allows the ontologies, as well as the relationships between concepts from distinct ontologies, to be represented independently. Additionally, fuzzy set theory techniques are employed to deal with knowledge subjectivity and uncertainty. This approach to organize knowledge and an associated query expansion method are integrated into a fuzzy model for information retrieval based on multi-related ontologies. The performance of a search engine using this model is compared with another fuzzy-based approach for information retrieval, and with the Apache Lucene search engine. Experimental results show that this model improves precision and recall measures.

A new term‐weighting scheme for naïve Bayes text categorization

PurposeAutomatic text categorization has applications in several domains, for example e‐mail spam detection, sexual content filtering, directory maintenance, and focused crawling, among others. Most information retrieval systems contain several components which use text categorization methods. One of the first text categorization methods was designed using a naïve Bayes representation of the text. Currently, a number of variations of naïve Bayes have been discussed. The purpose of this paper is to evaluate naïve Bayes approaches on text categorization introducing new competitive extensions to previous approaches.Design/methodology/approachThe paper focuses on introducing a new Bayesian text categorization method based on an extension of the naïve Bayes approach. Some modifications to document representations are introduced based on the well‐known BM25 text information retrieval method. The performance of the method is compared to several extensions of naïve Bayes using benchmark datasets designed for this purpose. The method is compared also to training‐based methods such as support vector machines and logistic regression.FindingsThe proposed text categorizer outperforms state‐of‐the‐art methods without introducing new computational costs. It also achieves performance results very similar to more complex methods based on criterion function optimization as support vector machines or logistic regression.Practical implicationsThe proposed method scales well regarding the size of the collection involved. The presented results demonstrate the efficiency and effectiveness of the approach.Originality/valueThe paper introduces a novel naïve Bayes text categorization approach based on the well‐known BM25 information retrieval model, which offers a set of good properties for this problem.

Analysis of a Vector Space Model, Latent Semantic Indexing and Formal Concept Analysis for Information Retrieval

Abstract Latent Semantic Indexing (LSI), a variant of classical Vector Space Model (VSM), is an Information Retrieval (IR) model that attempts to capture the latent semantic relationship between the data items. Mathematical lattices, under the framework of Formal Concept Analysis (FCA), represent conceptual hierarchies in data and retrieve the information. However, both LSI and FCA use the data represented in the form of matrices. The objective of this paper is to systematically analyze VSM, LSI and FCA for the task of IR using standard and real life datasets.

Design of Personalized Intelligent Information Retrieval Model Based on Agent

The features of vast distributed dynamic information on Web caused the problem of “overload” and “mislead” while query. Intelligent agent is a way to solve it. After considering the problems of users’ personal interests during the information retrieve adequately, the paper proposes an intelligent information retrieval model based-on Agent. This system integrated domain knowledge and used many arithmetic of learning user’s interest. Each Agent co-operates to finish information retrieval task, manifest the characteristics of intellectualization and individuality of in information retrieval. It is a good way to realize the highly effective intelligent retrieval system research.

Personalized Web Search Using Clickthrough Data and Web Page Rating

Personalization of Web search is to carry out retrieval for each user incorporating his/her interests. We propose a novel technique to construct personalized information retrieval model from the users’ clickthrough data and Web page ratings. This model builds on the user-based collaborative filtering technology and the top-N resource recommending algorithm, which consists of three parts: user profile, user-based collaborative filtering, and the personalized search model. Firstly, we conduct user’s preference score to construct the user profile from clicked sequence score and Web page rating. Then it attains similar users with a given user by user-based collaborative filtering algorithm and calculates the recommendable Web page scoring value. Finally, personalized informaion retrieval be modeled by three case applies (rating information for the user himself; at least rating information by similar users; not make use of any rating information). Experimental results indicate that our technique significantly improves the search performance.

Semantically enhanced Uyghur Information Retrieval Model

Traditional Uyghur search engine lacks semantic information, aiming to solve this problem, a semantically enhanced Uyghur information retrieval model was proposed based on the characteristics of Uyghur language. Firstly word stemming was carried out and web pages were represented by the form of 3-triples to construct the Uyghur knowledge base, then the matching between ontologies and web pages was established by computing concept similarity and relation similarity. Semantic inverted index was built to save the association between semantic entities and web pages, and user query analysis was implemented by expanding the queries and analyzing the relations between the queries, finally by combining the benefits of both keyword-based and semantic-based methods, ranking algorithm was implemented. By comparing with the Google search engine and the Lucene based method, the experiments validate the effectiveness and the feasibility of the model preliminarily. © 2012 ACADEMY PUBLISHER.

Applying human computation mechanisms to information retrieval

AbstractCrowdsourcing and Games with a Purpose (GWAP) have each received considerable attention in recent years. These two human computation mechanisms assist with tasks that cannot be solved by computers alone. Despite this increased attention, much of this transformation has been limited to a few aspects of Information Retrieval (IR). In this paper, we examine these two mechanisms' applicability to IR. Using an IR model, we apply criteria to determine the suitability of these crowdsourcing and GWAP mechanisms to each step of the model. Our analysis illustrates that these mechanisms can apply to several of these steps with good returns.

Extracting Collocations from Bengali Text Corpus

Automatic collocation extraction is very important in various applications in the field of natural language processing such as machine translation, word sense disambiguation, information retrieval, and language modelling in speech processing, lexicography and many more. The success of extracting collocations depends on the technique of preprocessing. A systematic pre-processing technique is described in this paper. Then the pre-processed data is used to extract collocation by using two methods: Point-wise Mutual Information and Fuzzy Bi-gram Index. The paper mainly focuses on bi-gram extraction from a Bengali news corpus. Collocations of higher length i.e., n-grams (n>2) are then obtained when the extracted collocations of lower lengths are treated as individual words.

An I/O Bandwidth-Sensitive Sparse Matrix-Vector Multiplication Engine on FPGAs

Sparse matrix-vector multiplication (SMVM) is a fundamental core of many high-performance computing applications, including information retrieval, medical imaging, and economic modeling. While the use of reconfigurable computing technology in a high-performance computing environment has shown recent promise in accelerating a wide variety of scientific applications, existing SMVM architectures on FPGA hardware have been limited in that they require either numerous pipeline stalls during computation (due to zero padding) or excessive input preprocessing during run-time. For large-scale sparse matrix scenarios, both of these shortcomings can result in unacceptable performance overheads, limiting the overall value of using FPGAs in a high-performance computing environment. In this paper, we present a scalable and efficient FPGA-based SMVM architecture which can handle arbitrary matrix sparsity patterns without excessive preprocessing or zero padding and can be dynamically expanded based on the available I/O bandwidth. Our experimental results using a commercial FPGA-based acceleration system demonstrate that our reconfigurable SMVM engine is highly efficient, with benchmark-dependent speedups over an optimized software implementation that range from to in terms of computation time.

Research and Design on E-government Information Retrieval Model

At present, most government websites have not given full play to the internet's characteristic such as share, interaction. The public satisfaction is poor. Therefore, this article puts forward an e-government information retrieval model based on the metadata. Firstly it builds an e-government information resource database, and all kinds of different types of resources are uniformly stored in the resource database; Secondly, the resources are described by metadata, and e-government information resources metadata scheme is designed; Thirdly, the metadata extraction algorithm is designed to extract the metadata elements of e-government information resources; Finally, e-government information resources storage and retrieval schemes are designed. The experiment result shows that the information retrieval method based on metadata can improve the e-government information retrieval efficiency and precision, and realizes the share of e-government resources. Thus it is a practical approach for the task of e-government information retrieval.