Effectiveness Of Information Retrieval Research Articles

A PSO Strategy of Finding Relevant Web Documents using a New Similarity Measure

In the world of the Internet and World Wide Web, which offers a tremendous amount of information, an increasing emphasis is being given to searching services and functionality. Currently, a majority of web portals offer their searching utilities, be it better or worse. These can search for the content within the sites, mainly text the textual content of documents. In this paper a novel similarity measure called SMDR (Similarity Measure for Documents Retrieval) is proposed to help retrieve more similar documents from the repository thus contributing considerably to the effectiveness of Web Information Retrieval (WIR) process. Bio-inspired PSO methodology is used with the intent to reduce the response time of the system and optimizes WIR process, hence contributes to the efficiency of the system. This paper also demonstrates a comparative study of the proposed system with the existing method in terms of accuracy, sensitivity, F-measure and specificity. Finally, extensive experiments are conducted on CACM collections. Better precision-recall rates are achieved than the existing system. Experimental results demonstrate the effectiveness and efficiency of the proposed system.

Document-based and term-based linear methods for pseudo-relevance feedback

Query expansion is a successful approach for improving Information Retrieval effectiveness. This work focuses on pseudo-relevance feedback (PRF) which provides an automatic method for expanding queries without explicit user feedback. These techniques perform an initial retrieval with the original query and select expansion terms from the top retrieved documents. We propose two linear methods for pseudo-relevance feedback, one document-based and another term-based, that models the PRF task as a matrix decomposition problem. These factorizations involve the computation of an inter-document or inter-term similarity matrix which is used for expanding the original query. These decompositions can be computed by solving a least squares regression problem with regularization and a non-negativity constraint. We evaluate our proposals on five collections against state-of-the-art baselines. We found that the term-based formulation provides high figures of MAP, nDCG and robustness index whereas the document-based formulation provides very cheap computation at the cost of a slight decrease in effectiveness.

Profile-Based Semantic Method using Heuristics for Web Search Personalization

User profiles play a critical role in personalizing user search. It assists search systems in retrieving relevant information that is searched on the web considering the user needs. Researchers presented a vast number of profile-based approaches that aims to improve the effectiveness of information retrieval. However, these approaches are syntactic-based which fail to achieve the user satisfaction. By the means that the search results do not meet user preferences, due to the fact that the search is keyword-based rather than semantic-based. Exploiting user profiles with the application of semantic web technology into personalization might produce a step forward in future retrieval systems. By adopting profiling approach and using ontology base characteristics, a semantic-based method using heuristics and KNN algorithm is proposed. It engages searching ontology base domains horizontally and vertically to discover and extract the closest concept to the meaning of the query keyword. The extracted concept is used to expand the user query to personalize the search result and present the customized information for individuals.

Enriching User Queries Using DBpedia Features and Relevance Feedback

Query expansion is a method for improving the effectiveness of information retrieval through the reformulation of queries by providing additional contextual information to the original queries. In this paper, we present a method to help a user redefine their queries using DBpedia’s property “dct:subject”. To achieve this purpose, we suggested an approach (SimLOD) that expands queries concepts with indexed terms, from the top 10 retrieved documents, that have similar features with the queries concepts terms’ features. The proposed approach allowed us to get higher precision results than our baseline that uses queries concepts to research documents.

Модель оценки эффективности информационного поиска в рамках концепции Семантического Web

Модель оценки эффективности информационного поиска в рамках концепции Семантического Web

What makes an effective clinical query and querier?

In this paper, we perform an in‐depth study into how clinicians represent their information needs and the influence this has on information retrieval (IR) effectiveness. While much research in IR has considered the effectiveness of IR systems, there is still a significant gap in the understanding of how users contribute to the effectiveness of these systems. The paper aims to contribute to this by studying how clinicians search for information. Multiple representations of an information need—from verbose patient case descriptions to ad‐hoc queries—were considered in order to understand their effect on retrieval. Four clinicians provided queries and performed relevance assessment to form a test collection used in this study. The different query formulation strategies of each clinician, and their effectiveness, were investigated. The results show that query formulation had more impact on retrieval effectiveness than the particular retrieval systems used. The most effective queries were short, ad‐hoc keyword queries. Different clinicians were observed to consistently adopt specific query formulation strategies. The most effective queriers were those who, given their information need, inferred novel keywords most likely to appear in relevant documents. This study reveals aspects of how people search within the clinical domain. This can help inform the development of new models and methods that specifically focus on the query formulation process to improve retrieval effectiveness.

A Literature Review on Patent Information Retrieval Techniques

Objective: Patents are critical intellectual assets for any competitive business. They can prove to be a gold mine if retrieved, analyzed and utilized appropriately. Patentability search is an important step in the patent process and missing out any relevant patent may cause expensive legal consequences. As worldwide patent collection is growing rapidly, retrieval of this enormous knowledge source has become complex and exhaustive. This paper attempts to review the studies carried out in enhancing the relevance effectiveness of patent information retrieval. Method/Analysis: Literature review presents various research works that have been carried out to yield better results in patent retrieval task by refining existing information retrieval techniques or by using standard approaches at the various stages of the patent retrieval task. This work exclusively looks at literatures dealing with retrieval of patent text. Findings: Patent retrieval is not a completely solved research domain and general information retrieval approaches do not prove effective in this domain as patents are special documents posing various retrieval challenges. The review also highlights future research directions and will help researchers working in the domain of patent retrieval. Application/Improvement: Considering the various techniques and frameworks available and their limitations, there is a lot of scope in the field of patent retrieval techniques which makes room for further research to be taken up in this domain. Keywords: Information Retrieval, Patent Retrieval, Patent Search, Query Expansion, Relevance Feedback, Semantic Analysis

Reversible N-grams Stemming Stripping Algorithm for Classification of Text Data

Abstract—Stemming methods traces the root or stem of a word that is possibly used for Information retrieval (IR) tasks for increasing the recall rate to enhance most relevant searches. There are numerous ways ranging from manual and automatic, language dependent to language independent of methods available for performing the task of stemming. Those algorithms are designed for the purpose of overcoming the challenges involved with the existing methods. This paper represents a comparative study of various available stemming algorithms widely used to enhance the effectiveness and efficiency of information retrieval.

Enriching an Authority File of Scientific Conferences with Information Extracted from the Web

Authority files maintain variant forms to refer to the same entity and they are very useful in digital libraries. However, collect data and keep an updated authority file is not a trivial task. This paper proposes an approach for the enrichment of a publication venue authority file by extracting information on conferences from their web pages. Collecting additional data is important to improve the effectiveness of data disambiguation tools and information retrieval, such as those that measure the quality of a scientific publication based on bibliometrics (e.g., Journal Impact Factor). Most applications use only basic citation metadata, such as author's names, work and publication venue titles. However, data external to the publication, contained in the publication venue web page, can be very useful in the disambiguation task. Our approach includes the steps for querying a web search engine, classifying documents obtained in the result sets and extracting information from the relevant pages. We evaluated two methods for classifying documents, one based on genre and content and one based on content only. The experiments show good results to trace a history of conference editions, with data such as URL, year of each edition and dates of changing in their names.

Spectral Clustering Wikipedia Keyword-Based Search Results

The paper presents an application of spectral clustering algorithms used for grouping Wikipedia search results. The main contribution of the paper is a representation method for Wikipedia articles that has been based on combination of words and links and it has been used to categorize search result in this repository. We evaluate the proposed approach with Primary Component Analysis and show, on the test data, how usage of cosine transformation to create combined representations influence data variability. On sample test datasets we also show how combined representation improves the data separation that increases overall results of data categorization. The paper reviews the three main spectral clustering methods and we test their usability for text categorization comparing them using external validation criteria with standard clustering quality measures. Discussion on descriptiveness of evaluation measures and performed experiments on test datasets allows us to select the one spectral clustering algorithm that has been implemented in our system. We give a brief description of the system architecture that groups on-line Wikipedia articles retrieved with user-specified keywords. Using the system we show how clustering increases information retrieval effectiveness for Wikipedia data repository.

A Comparative Study of Open-Domain and Specific-Domain Word Sense Disambiguation Based on Quranic Information Retrieval

Information retrieval is the process of analysing typed query as well as to retrieve relevant document according to the user query. Several issues can significantly affect the effectiveness of information retrieval. One of the common issue is the ambiguity lies on the words where a single word could yield several meanings. The process of identifying the exact sense of word is called Word Sense Disambiguation (WSD). Quran is the holly book for nearly 1.5 billion Muslims around the world. In particularly, Quran contains numerous words that can undergone multiple meanings. Therefore, there is a vital demand to apply WSD approach on Quran, in order, to improve the information retrieval. Several WSD approaches have been proposed for Quranic retrieval. However, these approaches are divided into two main categories; open-domain WSD approach and specific-domain WSD approach. Open-domain WSD is an approach that utilizes an open-domain dictionary such as WordNet, that is exploited to provide the exact sense. Whereas, domain-specific WSD approach aims to utilize a restricted training data that contain specific senses related to the domain of Quran. Hence, this study aims to establish a comparative study to investigate the two WSD categories including domain-specific and open-domain. For the domain-specific approach, a predefined example data has been collected to train Yarwosky algorithm which is a semisupervised machine learning technique. Then, based on the training, such algorithm can classify the exact sense for the words. In contrast, WordNet which is an open-domain dictionary has been used in this study with semantic distances, in order, to identify the similarity between the query word and the results of WordNet’s concepts. That dataset that has been used in this study is a Quranic translation. The experimental results have shown the mixed superiority of Yarwosky algorithm and WordNet WSD approach.

Evaluation of web search engine based on ranking of results and its features

Search engines help the user to surf the web. Due to the vast number of web pages, it is highly impossible for the user to retrieve the appropriate web page he needs. Thus, web search ranking algorithms play an important role in ranking web pages, so that the user could retrieve the page which is most relevant to the user's query. Measuring the information retrieval effectiveness of World Wide Web search engines is costly because of human relevance judgements involved. However, both for business enterprises and people it is important to know the most effective web search engines, since such search engines help their users find higher number of relevant web pages with less effort. Furthermore, this information can be used for several practical purposes. This paper presents a study of the applicability of two user-effort-sensitive evaluation measures on five web search engines (Google, Ask, Yahoo, AOL and Bing). Twenty queries were collected from the list of most hit queries in the last year from various search engines and based upon that search engines are evaluated.

Advanced information processing of MEMS motion sensors for gesture interaction

Abstract. Sensor-based gesture interaction technology has been widely adopted in consumer electronics. Nevertheless, bias, drift, and noise existing in sensor signals are difficult to eliminate, and accurate movement trajectory information is still needed to achieve flexible interaction application. This paper presents micro-electro-mechanical system (MEMS) motion sensor information processing algorithms designed on a gesture interaction system which integrates multiple low-cost MEMS motion sensors with ZigBee wireless technology to support embodied communication while acting together with machines. Sensor signal processing systems mainly solve noise removal, signal smoothing, gravity influence separation, coordinate system conversion, and position information retrieval. The attitude information which is an important movement parameter and required by position estimation is calculated with a quaternion-based extended Kalman filter (EKF). The effectiveness of the movement information retrieval of this gesture interface is verified by experiments and test analysis, both in static and moving cases. In the end, related applications of the described sensor information processing are discussed.

EFFECTIVENESS EVALUATION OF SEARCH IN INFORMATION SYSTEMS WITH CONSOLIDATED INFORMATION

To evaluate the effectiveness of information retrieval there is proposed to share the found documents on pertinent, relevant and irrelevant. Search Performance is ratio to determine the number of pertinent and relevant documents to the number of irrelevant documents and search engine properties have been submitted by the coefficient. The goal of this paper is to develop integrated criterion of evaluating the effectiveness of information retrieval on the results of the issuance of consolidated information systems. The concept of consolidated informationis given. The method of evaluating the effectiveness of information retrieval is built. It demonstrates the usage of the division found and published documents on pertinent, relevant and irrelevant. There is given integral indicator of the relevance of the document search query that takes into account the negative and positive features. Evaluation of effectiveness presented as the total value of the different components. The experiment was performed on the basis of information search in one or several search machines and information on the various search engines for one set of keywords. The approach to building assessment information retrieval is of practical importance because quantitative values obtained local assessments give grounds to optimize the set of keywords and determine the most appropriate information collection and search engines.

How well does Google work with Persian documents?

The performance evaluation of an information retrieval system is a decisive aspect of the measure of the improvements in search technology. The Google search engine, as a tool for retrieving information on the Web, is used by almost 92% of Iranian users. The purpose of this paper is to study Google’s performance in retrieving relevant information from Persian documents. The information retrieval effectiveness is based on the precision measures of the search results done to a website that we have built with the documents of a TREC standard corpus. We asked Google for 100 topics available on the corpus and we compared the retrieved webpages with the relevant documents. The obtained results indicated that the morphological analysis of the Persian language is not fully taken into account by the Google search engine. The incorrect text tokenisation, considering the stop words as the content keywords of a document and the wrong ‘variants encountered’ of words found by Google are the main reasons that affect the relevance of the Persian information retrieval on the Web for this search engine.

Pooling-based continuous evaluation of information retrieval systems

The dominant approach to evaluate the effectiveness of information retrieval (IR) systems is by means of reusable test collections built following the Cranfield paradigm. In this paper, we propose a new IR evaluation methodology based on pooled test-collections and on the continuous use of either crowdsourcing or professional editors to obtain relevance judgements. Instead of building a static collection for a finite set of systems known a priori, we propose an IR evaluation paradigm where retrieval approaches are evaluated iteratively on the same collection. Each new retrieval technique takes care of obtaining its missing relevance judgements and hence contributes to augmenting the overall set of relevance judgements of the collection. We also propose two metrics: Fairness Score, and opportunistic number of relevant documents, which we then use to define new pooling strategies. The goal of this work is to study the behavior of standard IR metrics, IR system ranking, and of several pooling techniques in a continuous evaluation context by comparing continuous and non-continuous evaluation results on classic test collections. We both use standard and crowdsourced relevance judgements, and we actually run a continuous evaluation campaign over several existing IR systems.

Exploiting salient semantic analysis for information retrieval

ABSTRACTRecently, many Wikipedia-based methods have been proposed to improve the performance of different natural language processing (NLP) tasks, such as semantic relatedness computation, text classification and information retrieval. Among these methods, salient semantic analysis (SSA) has been proven to be an effective way to generate conceptual representation for words or documents. However, its feasibility and effectiveness in information retrieval is mostly unknown. In this paper, we study how to efficiently use SSA to improve the information retrieval performance, and propose a SSA-based retrieval method under the language model framework. First, SSA model is adopted to build conceptual representations for documents and queries. Then, these conceptual representations and the bag-of-words (BOW) representations can be used in combination to estimate the language models of queries and documents. The proposed method is evaluated on several standard text retrieval conference (TREC) collections. Experiment results on standard TREC collections show the proposed models consistently outperform the existing Wikipedia-based retrieval methods.

Incorporating Intra-Query Term Dependencies in an Aspect Query Language Model

Query language modeling based on relevance feedback has been widely applied to improve the effectiveness of information retrieval. However, intra-query term dependencies i.e., the dependencies between different query terms and term combinations have not yet been sufficiently addressed in the existing approaches. This article aims to investigate this issue within a comprehensive framework, namely the Aspect Query Language Model AM. We propose to extend the AM with a hidden Markov model HMM structure to incorporate the intra-query term dependencies and learn the structure of a novel aspect HMM AHMM for query language modeling. In the proposed AHMM, the combinations of query terms are viewed as latent variables representing query aspects. They further form an ergodic HMM, where the dependencies between latent variables nodes are modeled as the transitional probabilities. The segmented chunks from the feedback documents are considered as observables of the HMM. Then the AHMM structure is optimized by the HMM, which can estimate the prior of the latent variables and the probability distribution of the observed chunks. Our extensive experiments on three large-scale text retrieval conference TREC collections have shown that our method not only significantly outperforms a number of strong baselines in terms of both effectiveness and robustness but also achieves better results than the AM and another state-of-the-art approach, namely the latent concept expansion model. © 2014Wiley Periodicals, Inc.

Clustering digital forensic string search output

This research comparatively evaluates four competing clustering algorithms for thematically clustering digital forensic text string search output. It does so in a more realistic context, respecting data size and heterogeneity, than has been researched in the past. In this study, we used physical-level text string search output, consisting of over two million search hits found in nearly 50,000 allocated files and unallocated blocks. Holding the data set constant, we comparatively evaluated k-Means, Kohonen SOM, Latent Dirichlet Allocation (LDA) followed by k-Means, and LDA followed by SOM. This enables true cross-algorithm evaluation, whereas past studies evaluated singular algorithms using unique, non-reproducible datasets. Our research shows an LDA + k-Means using a linear, centroid-based user navigation procedure produces optimal results. The winning approach increased information retrieval effectiveness, from the baseline random walk absolute precision rate of 0.04, to an average precision rate of 0.67. We also explored a variety of algorithms for user navigation of search hit results, finding that the performance of k-means clustering can be greatly improved with a non-linear, non-centroid-based cluster and document navigation procedure, which has potential implications for digital forensic tools and use thereof, particularly given the popularity and speed of k-means clustering.

Automatic ranking of retrieval models using retrievability measure

Analyzing retrieval model performance using retrievability (maximizing findability of documents) has recently evolved as an important measurement for recall-oriented retrieval applications. Most of the work in this domain is either focused on analyzing retrieval model bias or proposing different retrieval strategies for increasing documents retrievability. However, little is known about the relationship between retrievability and other information retrieval effectiveness measures such as precision, recall, MAP and others. In this study, we analyze the relationship between retrievability and effectiveness measures. Our experiments on TREC chemical retrieval track dataset reveal that these two independent goals of information retrieval, maximizing retrievability of documents and maximizing effectiveness of retrieval models are quite related to each other. This correlation provides an attractive alternative for evaluating, ranking or optimizing retrieval models' effectiveness on a given corpus without requiring any ground truth available (relevance judgments).