Search

Similarity measure is calculated based on the syntactical representation of terms. Similarity measure used in data mining task likes clustering, and classification returns irrelevant information. The semantic similarity calculated based on the relatedness between wordpairs of terms returns better result. Many researchers proposed approaches for getting word similarity by using different sources like ontologies, thesauri etc. This Paper provides an overview of six existing semantic similarity measures and compared those semantic similarity measures using the wordpairs of sports domain ontology. This paper describes about how WordNet is used to retrieve the synonyms and using synsets how semantic similarity measures are calculated. Finally, comparison of selected semantic similarity measures for the given wordpairs with respect to the considered knowledge base domain ontology and WordNet is presented. [ABSTRACT FROM AUTHOR]

With the development of information technologies, more and more information retrieval tools, containing a wide variety of information, can be found. Internet resources, universal and specialized search engines, virtual libraries are becoming the main information retrieval tools for students. The article introduces an exploratory (pilot) research aiming to find out the differences of information retrieval between Lithuanian and Portuguese students. Šiauliai State College offers a unique study programme Information Management, having a signed contract of a double diploma with Porto (Portugal) Polytechnic Institute Porto Accounting and Business School (ISCAP). 10 Portuguese and 10 Lithuanian students participated in the research. The online questionnaire was forwarded to the students of ISCAP Porto Accounting and Business School and to the second year students of the study programme of Information Management at Šiauliai State College. The research revealed that the respondents Lithuanian students look for scientific papers and books, whereas Portuguese students search for basic information and scientific papers. All the respondents of the survey identified Google Scholar as the most important search engine for scientific information retrieval. [ABSTRACT FROM AUTHOR]

The process of data analytics on large-scale government administrative data — that belong to various domains like education, transport, energy, and health — can be enhanced by retrieving pertinent documents from diverse data sources. Without a supporting framework of metadata, big data analytics can be daunting. Even though statistical algorithms can perform extensive analyses on a variety of data with little help from metadata, applying these techniques to heterogeneous data may not always result in reliable findings. Recently, semantics-aware (or semantic search) search techniques received much attention as they utilize implicit knowledge to enhance the search. Similarly, traditional search engines rely on the inherent linkages within the underlying data model to improve their search quality. In the case of general-purpose information retrieval systems, to gather information from the internet (open access data) or to access open government administrative data, a domain agnostic ontology shall be employed to supply background knowledge. This paper draws on research undertaken by the authors at IIIT Bangalore Center for Open Data Research (CODR) in developing a semantics-aware data lake framework to host and analyze government administrative data. In this study, we present an ontology-based document retrieval solution where an ontology serves as an intermediary to close the gap between what the user seeks and what the search retrieves. Although our study settings are based on the Government of Karnataka (GoK, India), we believe the findings have wider resonance. Our experimental results based on agricultural data from the GoK look promising. [ABSTRACT FROM AUTHOR]

Information retrieval aims to retrieve the documents that answer users' queries. A typical search process consists of different phases for which a variety of components have been defined in the literature; each one having a set of hyper-parameters to tune. Different studies focused on how and how much the components and their hyper-parameters affect the system performance in terms of effectiveness, others on the query factor. The aim of these studies is to better understand information retrieval system effectiveness. This paper reviews the literature of this domain. It depicts how data analytics has been used in IR for a better understanding system effectiveness. This review concludes lack of full understanding of system effectiveness according to the context although it has been possible to adapted the query processing to some contexts successfully. This review also concludes that, even if it is possible to distinguish effective from non effective system on average on a query set, nor the system component analysis nor the query features analysis were successful in explaining when and why a particular system fails on a particular query. [ABSTRACT FROM AUTHOR]

The availability of tremendous amount of information has necessitated the need for retrieving useful information from a huge collection. Information retrieval systems assist the user to retrieve relevant information from huge corpus depending on user need specified in the form of query. In this paper, we discuss various information retrieval models and methods for evaluating a model. Also we discuss an application where an IR model can provide a solution. [ABSTRACT FROM AUTHOR]

PURPOSE: Despite advances in molecular therapeutics, few anticancer agents achieve durable responses. Rational combinations using two or more anticancer drugs have the potential to achieve a synergistic effect and overcome drug resistance, enhancing antitumor efficacy. A publicly accessible biomedical literature search engine dedicated to this domain will facilitate knowledge discovery and reduce manual search and review. METHODS: We developed RetriLite, an information retrieval and extraction framework that leverages natural language processing and domain-specific knowledgebase to computationally identify highly relevant papers and extract key information. The modular architecture enables RetriLite to benefit from synergizing information retrieval and natural language processing techniques while remaining flexible to customization. We customized the application and created an informatics pipeline that strategically identifies papers that describe efficacy of using combination therapies in clinical or preclinical studies. RESULTS: In a small pilot study, RetriLite achieved an F 1 score of 0.93. A more extensive validation experiment was conducted to determine agents that have enhanced antitumor efficacy in vitro or in vivo with poly (ADP-ribose) polymerase inhibitors: 95.9% of the papers determined to be relevant by our application were true positive and the application's feature of distinguishing a clinical paper from a preclinical paper achieved an accuracy of 97.6%. Interobserver assessment was conducted, which resulted in a 100% concordance. The data derived from the informatics pipeline have also been made accessible to the public via a dedicated online search engine with an intuitive user interface. CONCLUSION: RetriLite is a framework that can be applied to establish domain-specific information retrieval and extraction systems. The extensive and high-quality metadata tags along with keyword highlighting facilitate information seekers to more effectively and efficiently discover knowledge in the combination therapy domain. [ABSTRACT FROM AUTHOR]

Medical images are growing dramatically both in quantity and application in the data era and the emerging Big Data problem. Searching and finding the proper medical image among such a huge number of medical images is not possible unless using a proper medical search engine. Indexing is the backend process in information retrieval systems in which documents are annotated with the index entry to be retrieved more accurately and efficiently. Most of the indexing techniques for medical images are content-based that are more complex and time consuming than the text-based ones. In this paper, a text-based medical image indexing technique is proposed that use medical images' attributes and fragments them with the hybrid fragmentation approach (that are used in distributed database design) and re-form each of such attribute fragments into a hierarchy, constructing a multidimensional index. Hybrid fragmentation approach uses both Horizontal and vertical fragmentation of medical images' attributes provided in header of medical image standard formats (such as the DICOM). Horizontal fragmentation uses values of image attributes (i.e., image content and properties dependent), whilst the vertical fragmentation uses pairwise affinity and correlation of the attributes in the application domain (i.e., application dependent). So, the proposed hybrid fragmentation approach based indexing of medical images aim to consider both the image properties and application statistics together to provide a better functionality. As the experimental performance evaluation results illustrate, the proposed multidimensional indexing can provide better precision of information retrieval rather that a single index or a set of multiple indexes, since that considers semantic relationship of the medical image's attributes via the hybrid (horizontal and vertical) fragmentation. Moreover, the hybrid fragmentations approach based indexing also outperforms the vertical fragmentation-based multidimensional medical image indexing technique in terms of precision, recall and response time. [ABSTRACT FROM AUTHOR]

Retrieving required medical images from a huge amount of images is one of the most widely used features in medical information systems, including medical imaging search engines. For example, diagnostic decision making has traditionally been accompanied by patient data (image or non-image) and previous medical experiences from similar cases. Indexing as part of search engines (or retrieval system), increases the speed of a search. The goal of this study, is to provide an effective and efficient indexing technique for medical images search engines. In this paper, in order to archive this goal, a multidimensional indexing technique for medical images is designed using the normalization technique that is used to reduce redundancy in relational database design. Data structure of the proposed multidimensional index and also different required operations are designed to create and handle such a multidimensional index. Time complexity of each operation is analyzed and also average memory space required to store any medical image (along with its related metadata) is calculated as the space complexity analysis of the proposed indexing technique. The results show that the proposed indexing technique has a good performance in terms of memory usage, as well as execution time for the usual operations. Moreover, and may be more important, the proposed indexing techniques improves the precision and recall of the information retrieval system (i.e., search engine) which uses this technique for indexing medical images. Besides, a user of such search engine can retrieve medical images which s/he has specified its attributes is some different aspects (dimensions), e.g., tissue, image modality and format, sickness and trauma, etc. So, the proposed multidimensional indexing techniques can improve effectiveness of a medical image information retrieval system (in terms of precision and recall), while having a proper efficiency (in terms of execution time and memory usage), and can improve the information retrieval process for healthcare search engines. [ABSTRACT FROM AUTHOR]

Over the last decade, a huge number of medical visual data are widely used for diagnose, treatment, and follow-up. Retrieving needed medical image(s) from a huge number of images is one of the most widely used features in medical information systems, especially in medical image search engines. Indexing as part of search engines (or information retrieval systems), increases the speed (efficiency) of search and the information retrieval process. In this paper, a multidimensional indexing technique for medical images is presented that can improve effectiveness and efficiency of medical image search engines. The structure of the proposed multi-dimensional indexing technique and its main operations (i.e., creation, insertion, deletion and search) is designed and evaluated. In order to create this multidimensional index, the "vertical fragmentation" approach (which is usually applied for distributed database design) is used to determine the each of dimensions; roughly speaking, dimensions are different aspects of the medical images for a/some information need (e.g., image type and format, type of disorder, etc.). Accordingly, data structure of the proposed multidimensional indexing technique (which is named "Diamond") is formed by using the vertical fragmentation of medical image attributes (to differentiate the dimensions), and then using agglomerative hierarchical clustering to build up the hierarchy in each dimension. The proposed multi-dimensional indexing technique is implemented using the open-source search engine Lucene and compared with the built-in indexing technique available in the Lucene search engine, and also with the Terrier Platform (available for the benchmarking of information retrieval systems). In this evaluation, efficiency and effectiveness measures of the proposed multidimensional indexing technique (Diamond) are evaluated experimentally, beside the analysis of the designed data structure and its operations. For the experimental evaluation data set, images from Tabriz Behbood Hospital and a subset of TCIA images were used. Experimental evaluation results show that Diamond, the proposed multidimensional indexing technique improves both efficiency and effectiveness for a medical image search engine. [ABSTRACT FROM AUTHOR]

Distracted driving due to smartphone use is one of the key reasons for road accidents. However, the 6G super-heterogeneous network systems and highly differentiated application scenarios require highly elastic and endogenous information services involving the use of smart apps, and related information retrieval by drivers in modern Vehicle-to-People (V2P) Networks. The tension raised due to the conflicting attention requirements of driving and information retrieval can be resolved by designing information retrieval solutions that demand minimal user interaction. In this paper, we construct a Personalized Search Query Generator (PSQG) to reduce driver-mobile interaction during information retrieval in the 6 G era. This system has a query generator and a query recommendation component that update two sets of relationships dynamically: one is the query and the title, another is search and recommendation. The proposed system learns a user's intent based on historical query records and recommends personalized queries, thus reducing the driver-mobile interaction time. We deploy the system into a real search engine and conduct several online experiments. These experiments are conducted using a custom constructed dataset comprising ten million samples. We use the BLEU-score metric and perform A/B testing. The results demonstrate that our system can assist users in making precise queries efficiently. The proposed system can improve drivers’ safety if used in smartphones and other information retrieval systems in vehicles. [ABSTRACT FROM AUTHOR]

Evaluation of information retrieval systems is a fundamental topic in Library and Information Science. The aim of this paper is to connect the system-oriented and the user-oriented approaches to relevant philosophical schools. By reviewing the related literature, it was found that the evaluation of information retrieval systems is successful if it benefits from both system-oriented and user-oriented approaches (composite). The system-oriented approach is rooted in Parmenides' philosophy of stability (immovable) which Plato accepts and attributes to the world of forms; the user-oriented approach is rooted in Heraclitus' flux philosophy (motion) which Plato defers and attributes to the tangible world. Thus, using Plato's theory is a comprehensive approach for recognizing the concept of relevance. The theoretical and philosophical foundations determine the type of research methods and techniques. Therefore, Plato's dialectical method is an appropriate composite method for evaluating information retrieval systems. [ABSTRACT FROM AUTHOR]

This guide outlines general issues in searching for studies; describes the main sources of potential studies; and discusses how to plan the search process, design, and carry out search strategies, manage references found during the search process and document and report the search process. [ABSTRACT FROM AUTHOR]

Conventional approaches to relevance ranking typically optimize ranking models by each query separately. The traditional cluster hypothesis also does not consider the dependency between related queries. The goal of this paper is to leverage similar search intents to perform ranking consistency so that the search performance can be improved accordingly. Different from the previous supervised approach, which learns relevance by click‐through data, we propose a novel cocluster hypothesis to bridge the gap between relevance ranking and ranking consistency. A nearest‐neighbors test is also designed to measure the extent to which the cocluster hypothesis holds. Based on the hypothesis, we further propose a two‐stage unsupervised approach, in which two ranking heuristics and a cost function are developed to optimize the combination of consistency and uniqueness (or inconsistency). Extensive experiments have been conducted on a real and large‐scale search engine log. The experimental results not only verify the applicability of the proposed cocluster hypothesis but also show that our approach is effective in boosting the retrieval performance of the commercial search engine and reaches a comparable performance to the supervised approach. [ABSTRACT FROM AUTHOR]

Purpose - Most digital libraries (DL) are now available online. They also provide the Z39.50 standard protocol which allows computer-based systems to effectively retrieve information stored in the DLs. The major difficulty lies in inconsistency between database schemas of multiple DLs. The purpose of this paper is to present a system known as Argumentation-based Digital Library Search (ADLSearch), which facilitates information retrieval across multiple DLs. Design/methodology/approach - The proposed approach is based on argumentation theory for schema matching reconciliation from multiple schema matching algorithms. In addition, a distributed architecture is proposed for the ADLSearch system for information retrieval from multiple DLs. Findings - Initial performance results are promising. First, schema matching can improve the retrieval performance on DLs, as compared to the baseline technique. Subsequently, argumentationbased retrieval can yield better matching accuracy and retrieval efficiency than individual schema matching algorithms. Research limitations/implications - The work discussed in this paper has been implemented as a prototype supporting scholarly retrieval from about 800 DLs over the world. However, due to complexity of argumentation algorithm, the process of adding new DLs to the system cannot be performed in a real-time manner. Originality/value - In this paper, an argumentation-based approach is proposed for reconciling the conflicts from multiple schema matching algorithms in the context of information retrieval from multiple DL. Moreover, the proposed approach can also be applied for similar applications which require automatic mapping from multiple database schemas. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Processing the normal text is quite easier and the information can be efficiently retrieved. There are various algorithms have been already proposed for normal text retrieval. Whereas retrieving the geospatial information are very complex due to nature of the data. It needs supplementary processes to be performed. Since geospatial data contains complex information like location and direction. To effectively handle the geospatial queries, a novel Semantic and Feature Aggregated Information Retrieval is proposed in this paper. Preliminary, there are four steps need to be perform, they are Clustering, Indexing, Retrieval and Ranking. Also, Context based Query Weighting (CQW) approach is proposed to cluster the documents present in the corpus and indexing is based on multilevel hashing. Feature Probability and Density (FPD) technique is utilized to retrieve the document which matches the user query information. The Semantic Density (SD) technique is used to rank the retrieved documents. The experimental results shows that the proposed SFAIR technique provides better results than the existing technique. [ABSTRACT FROM AUTHOR]

Internet searches that occur in learning contexts are very different in nature from traditional “lookup” or “known item” searches: students usually perform searches to gather information about or master a certain topic, and the search engine is used as an aid in the exploration of a domain of knowledge. This paper presents SDE (Search Discover Explore), an exploratory search engine for educational resources that was built on top of the knowledge provided by Wikipedia: the set of its articles provides the search space (the set of topics that users can investigate), and the relationships between Wikipedia articles inform the suggestions that the search engine provides to students to go deeper in the exploration of a certain domain of knowledge. SDE indexes several hundreds of thousands of educational resources from high-quality Web sources, such as Project Gutenberg and Open Education Europe, among many others. This paper also reports the results of the evaluation of SDE by experts in Technology Enhanced Learning in several workshops that took place across Europe in the context of the European FP7 project iTEC. These results enable us to conclude that the exploratory search paradigm, making use of knowledge mined from Wikipedia, is a very promising approach for building information retrieval systems to be used in learning contexts. [ABSTRACT FROM AUTHOR]

The age of information has done it simple for storing huge amount of data. In actual fact, a considerable segment of existing information is accumulated in the text databases that have huge set of documents from different sources like research articles, news articles, books, e-mail messages, web pages and digital libraries. In many text databases, stored data are in the semi-structured format in that they are neither entirely structured nor entirely unstructured. IR (Information Retrieval) field has been growing in parallel using database systems for several years. Contrasting to the databases system fields that have concentrated mainly on transaction and query processing of the structured data, IR is concerned with firm and retrieval of data from a huge quantity of text-oriented documents. Thus, IR tackles with unstructured and/or semi-structured databases. Information security requirements within a firm have experience major variations in the past some decades. By the establishment of computer, the necessary for automated equipment for securing files as well as other information that stored on the computer turned into evident. This is particularly in case of shared information resources via public network. This is the origin for having a secure computer system / the need for computer security. Computer Security can be achieved by Intrusion Detection Systems. In this paper, we address these issues by applying Similarity Search in two diversified fields: Digital Libraries and Computer Security. The paper discusses a fast and efficient similarity search technique for approximate retrieval of books metadata in Digital Libraries. In DLI the books retrieval takes place just by using metadata such as title, year, edition, author, publishing of a book. Though, if metadata is missing, incorrect or unfinished, then it creates the library retrieval system inefficient, incorrect leads too much confusion to the user. In this context even if the query from the user matches partially or fully with a stored pattern, the information related to that be retrieved. The paper talks about a method that functions rapid and effective, language independent, and flexible library retrieval system signature based similarity search. This system is able to retrieve not only the metadata that exactly matches the query but also fairly accurate identical because of missing words, jumbled words and spell mistakes. Fundamentally, signature file approach is used here. A signature file approach looks like the most capable for huge database as it has superior text retrieval features and requires little storage overhead. [ABSTRACT FROM AUTHOR]

This study has developed a combined indicator to evaluate the performance of different search engines. Documentary analysis, survey, and evaluative methods are employed in the present study. The research was conducted in two stages. First, a combined indicator was designed to measure search engines. To this end, 72 criteria for measuring the performance of search engines were identified, out of which 22 criteria were selected. Accordingly, 10 criteria were selected in six general classes through a survey of subject matter experts. Validation of our proposed combined indicator was obtained by Delphi method and using the opinions of experts in the fields of information science and information system. Second, web search engines were evaluated based on the proposed combined indicator. The statistical population of this part of the research consisted of two categories: (1) general web search engines, and (2) general subjects. The sample size of the first category contained four search engines Yahoo, Google, DuckDuckGo, and Bing, and the second category involved 40 search terms under 10 general categories. The results showed that the combined indicator had six general criteria: (1) relevance, (2) ranking, (3) novelty ratio, (4) coverage ratio, (5) ratio of unrelated documents, and (6) proportion of duplication hits. According to this indicator, Google is at the top, followed by Bing. This study proposes a new indicator for evaluating search engine performance, which can measure the efficiency of search engines. Therefore, its use to measure the performance of search engines is recommended to researchers and search engine developers. [ABSTRACT FROM AUTHOR]

Search engines (e.g. Google.com, Yahoo.com, and Bing.com) have become the dominant model of online search. Large and small ecommerce provide built-in search capability to their visitors to examine the products they have. While most large business are able to hire the necessary skills to build advanced search engines, small online business still lack the ability to evaluate the results of their search engines, which means losing the opportunity to compete with larger business. The purpose of this paper is to build an open-source model that can measure the relevance of search results for online businesses as well as the accuracy of their underlined algorithms. We used data from a Kaggle.com competition in order to show our model running on real data. [ABSTRACT FROM AUTHOR]

Bibliometric‐enhanced information retrieval uses bibliometrics (e.g., citations) to improve ranking algorithms. Using a data‐driven approach, this article describes the development of a bibliometric‐enhanced ranking algorithm for legal information retrieval, and the evaluation thereof. We statistically analyze the correlation between usage of documents and citations over time, using data from a commercial legal search engine. We then propose a bibliometric boost function that combines usage of documents with citation counts. The core of this function is an impact variable based on usage and citations that increases in influence as citations and usage counts become more reliable over time. We evaluate our ranking function by comparing search sessions before and after the introduction of the new ranking in the search engine. Using a cost model applied to 129,571 sessions before and 143,864 sessions after the intervention, we show that our bibliometric‐enhanced ranking algorithm reduces the time of a search session of legal professionals by 2 to 3% on average for use cases other than known‐item retrieval or updating behavior. Given the high hourly tariff of legal professionals and the limited time they can spend on research, this is expected to lead to increased efficiency, especially for users with extremely long search sessions. [ABSTRACT FROM AUTHOR]

In the ongoing days, the development of sight and sound substance and information stockpiling produces colossally. Clients can extricate any kind of data they require from recordings. This outcomes in quick development of video information, client's discover complexity while procurement their important data. To address this, EBR (Event Based Ranking) propose another way to deal with group and rank mixed media occasions based recordings as per client's advantage. Clients are generally keen on the best positioned and occasion pertinent recordings of returned query output. An occasion based positioning methodology which empowers clients to iteratively peruse the video as per their inclination. The proposed conspire has new way to deal with order and rank occasions based recordings. It improves the exactness of video recovery which incorporates certain functionalities for customized look. The data of clients' input is used in re-positioning technique to additionally enhance the recovering exactness. It gives the customized lastly re-positioned pertinent outcomes to shape a brought together precise query output. EBR is more precise in foreseeing and positioning client particular inclinations and diminishes the time many-sided quality. This Paper proposed a calculation comprises of: video rank calculation, occasion term suggestion, and client criticism and client session. [ABSTRACT FROM AUTHOR]

Objective: The Cochrane Handbook of Systematic Reviews contains search filters to find randomized controlled trials (RCTs) in Ovid MEDLINE: one maximizing sensitivity and another balancing sensitivity and precision. These filters were originally published in 1994 and were adapted and updated in 2008. To determine the performance of these filters, the authors tested them and thirty-six other MEDLINE filters against a large new gold standard set of relevant records. Methods: We identified a gold standard set of RCT reports published in 2016 from the Cochrane CENTRAL database of controlled clinical trials. We retrieved the records in Ovid MEDLINE and combined these with each RCT filter. We calculated their sensitivity, relative precision, and f-scores. Results: The gold standard comprised 27,617 records. MEDLINE searches were run on July 16, 2019. The most sensitive RCT filter was Duggan et al. (sensitivity=0.99). The Cochrane sensitivity-maximizing RCT filter had a sensitivity of 0.96 but was more precise than Duggan et al. (0.14 compared to 0.04 for Duggan). The most precise RCT filters had 0.97 relative precision and 0.83 sensitivity. Conclusions: The Cochrane Ovid MEDLINE sensitivity-maximizing RCT filter can continue to be used by Cochrane reviewers and to populate CENTRAL, as it has very high sensitivity and a slightly better precision relative to more sensitive filters. The results of this study, which used a very large gold standard to compare the performance of all known RCT filters, allows searchers to make better informed decisions about which filters to use for their work. [ABSTRACT FROM AUTHOR]

Bandit algorithms, named after casino slot machines sometimes known as “one-armed bandits”, fall into a broad category of stochastic scheduling problems. In the setting with multiple arms, each arm generates a reward with a given probability. The gambler’s aim is to find the arm producing the highest payoff and then continue playing in order to accumulate the maximum reward possible. However, having only a limited number of plays, the gambler is faced with a dilemma: should he play the arm currently known to produce the highest reward or should he keep on trying other arms in the hope of finding a better paying one? This problem formulation is easily applicable to many real-life scenarios, hence in recent years there has been an increased interest in developing bandit algorithms for a range of applications. In information retrieval and recommender systems, bandit algorithms, which are simple to implement and do not require any training data, have been particularly popular in online personalization, online ranker evaluation and search engine optimization. This survey provides a brief overview of bandit algorithms designed to tackle specific issues in information retrieval and recommendation and, where applicable, it describes how they were applied in practice. [ABSTRACT FROM AUTHOR]

Nowadays, in information systems such as web search engines and databases, diversity is becoming increasingly essential and getting more and more attention for improving users' satisfaction. In this sense, query result diversification is of vital importance and well worth researching. Some issues such as the definition of diversification and efficient diverse query processing are more challenging to handle in information systems. Many researchers have focused on various dimensions of diversify problem. In this survey, we aim to provide a thorough review of a wide range of result diversification techniques including various definitions of diversifications, corresponding algorithms, diversification technique specified for some applications including database, search engines, recommendation systems, graphs, time series and data streams as well as result diversification systems. We also propose some open research directions, which are challenging and have not been explored up till now, to improve the quality of query results. [ABSTRACT FROM AUTHOR]