Search

There has been a proliferation of new research discovery tools that aid scientists in finding relevant publications. To obtain a general overview of this development, this article generates a conceptual typology of all possible research discovery tools by drawing from the information-theoretical concepts of redundancy/variety. Bibliometric links between scholarly publications can thus exhibit 'redundancy' (i.e. expectable linkages between academic works) or 'variety' (i.e. original co-occurrence patterns). On the redundancy-reproducing end of the typology are machines that harness extant co-citations or keyword queries, such as academic search engines and paper recommender systems. The variety end of the spectrum harbours services that enable categorial browsing or that suggest publications randomly, such as journals' tables of contents or random paper bots. The typology has implications for understanding how the design of research discovery platforms may ultimately shape aggregate citational networks of science. [ABSTRACT FROM AUTHOR]

To better understand the topic of this colloquium, we have created a series of databases related to knowledge domains (dynamic systems [small world/Milgram], information visualization [Tufte], co-citation [Small], bibliographic coupling [Kessler], and scientometrics [Scientometrics]). I have used a software package called HistCiteTM which generates chronological maps of subject (topical) collections resulting from searches of the ISI Web of Science® or ISI citation indexes (SCI, SSCI, and/or AHCI) on CD-ROM. When a marked list is created on WoS, an export file is created which contains all cited references for each source document captured. These bibliographic collections, saved as ASCII files, are processed by HistCite in order to generate chronological and other tables as well as historiographs which highlight the most-cited works in and outside the collection. HistCite also includes a module for detecting and editing errors or variations in cited references as well as a vocabulary analyzer which generates both ranked word lists and word pairs used in the collection. Ideally the system will be used to help the searcher quickly identify the most significant work on a topic and trace its year-by-year historical development. In addition to the collections mentioned above, historiographs based on collections of papers that cite the Watson-Crick 1953 classic paper identifying the helical structure of DNA were created. Both year-by-year as well as month-by-month displays of papers from 1953 to 1958 were necessary to highlight the publication activity of those years. [ABSTRACT FROM AUTHOR]

Publication in quality journals has become a major indicator of research performance in UK universities. This paper investigates the notion of 'quality journal' and finds dizzying circularity in its definitions. Actually, what a quality journal is does not really matter: agreement that there are such things matters very much indeed. As so often happens with indicators of performance, the indicator has become the target. So, the challenge is to publish in quality journals, and the challenge rewards gamesmanship. Vested interests have become particularly skilful at the game, and at exercising the winners' prerogative of changing the rules. All but forgotten in the desperation to win the game is publication as a means of communicating research findings for the public benefit. The paper examines the situation in management studies, but the problem is much more widespread. It concludes that laughter is both the appropriate reaction to such farce, and also, perhaps, the stimulus to reform. [ABSTRACT FROM AUTHOR]

Discusses the scope of information science, problems involved, and the areas of research needed for development. In particular, the processes occurring in thinking, memory, and knowledge are considered as they arise in the production and receipt of information. (Author/RAA)

Discusses evidence-based medicine (EBM) and the need for systematic use of databases like MEDLINE with more sophisticated search strategies to optimize the retrieval of relevant papers. Describes an empirical study of hospital libraries that examined requests for information and search strategies using both structured and unstructured forms. (Author/LRW)

The Semantic Web (SW) is one of the main efforts aiming to enhance human and machine interaction by representing data in an understandable way for machines to mediate data and services. It is a fast-moving and multidisciplinary field. This study conducts a thorough bibliometric analysis of the field by collecting data from Web of Science (WOS) and Scopus for the period of 1960-2009. It utilizes a total of 44,157 papers with 651,673 citations from Scopus, and 22,951 papers with 571,911 citations from WOS. Based on these papers and citations, it evaluates the research performance of the SW by identifying the most productive players, major scholarly communication media, highly cited authors, influential papers and emerging stars. [ABSTRACT FROM AUTHOR]

In the context of e-government and more specifically that of parliament, this paper tackles the problem of finding Members of Parliament (MPs) according to their profiles which have been built from their speeches in plenary or committee sessions. The paper presents a common solution for two problems: firstly, a member of the public who is concerned about a certain issue might want to know who the best MP is for dealing with their problem (recommending task); and secondly, each new piece of textual information that reaches the house must be correctly allocated to the appropriate MP according to its content (filtering task). This paper explores both these ways of searching for relevant people conceptually by encapsulating them into a single problem: that of searching for the relevant MP’s profile given an information need. Our research work proposes various profile construction methods (by selecting and weighting appropriate terms) and compares these using different retrieval models to evaluate their quality and suitability for different types of information needs in order to simulate real and common situations. [ABSTRACT FROM AUTHOR]

An analysis is presented of the publications by, and citations to, 57 male and 48 female academics in five departments of librarianship and information science. After taking account of differences in subject and differences in numbers of academics, it is shown that male academics publish significantly more papers on average than do female authors, but that there is no significant difference in the numbers of citations to published papers. [ABSTRACT FROM AUTHOR]

This letter provides some additional perspectives on the development of British information science. The authors have been personally involved in the discipline since the early 1970s and have had the privilege of knowing many of the pioneers in information science research and exploitation. The opportunity is taken add some personal perspectives to the paper and add further references to the bibliography. [ABSTRACT FROM PUBLISHER]

The emergence of research on various aspects of 'information behaviour' is explored and its growth as a subject of academic research is documented. The origin of the field as a potential aid to the development of library and information services is noted, as is the transition from this status to that of a subject for research at PhD level and beyond. The development of the field has thus led to a division between the needs of academia for theoretically grounded work, and the needs of the field of practice for guidance for service development. There is, today, a disconnection between research and practice, to a significant extent: early research was undertaken by practitioners but today academic research dominates the scene. Suggestions are made as to how this disconnection can be repaired. [ABSTRACT FROM AUTHOR]

Interdisciplinary studies are becoming increasingly popular, and research domains of many experts are becoming diverse. This phenomenon brings difficulty in recommending experts to review interdisciplinary submissions. In this study, an Author–Subject–Topic (AST) model is proposed with two versions. In the model, reviewers' subject information is embedded to analyse topic distributions of submissions and reviewers' publications. The major difference between the AST and Author–Topic models lies in the introduction of a 'Subject' layer, which supervises the generation of hierarchical topics and allows sharing of subjects among authors. To evaluate the performance of the AST model, papers in Information System and Management (a typical interdisciplinary domain) in a famous Chinese academic library are investigated. Comparative experiments are conducted, which show the effectiveness of the AST model in topic distribution analysis and reviewer recommendation for interdisciplinary studies. [ABSTRACT FROM AUTHOR]

In this paper we provide a method that allows the visualization of similarity relationships present between items of collaborative filtering recommender systems, as well as the relative importance of each of these. The objective is to offer visual representations of the recommender system’s set of items and of their relationships; these graphs show us where the most representative information can be found and which items are rated in a more similar way by the recommender system’s community of users. The visual representations achieved take the shape of phylogenetic trees, displaying the numerical similarity and the reliability between each pair of items considered to be similar. As a case study we provide the results obtained using the public database Movielens 1M, which contains 3900 movies. [ABSTRACT FROM PUBLISHER]

The purpose of this research is to map and analyse the conceptual and thematic structure of library and information science (LIS) research from the perspective of the co-word analysis. The bibliographical records consist of all the research papers published in the LIS core journals between 1990 and 2016 and indexed in Web of Science. 'CiteSpace' was used to visualise the co-word network of LIS studies. The frequency of co-occurrence and centrality scores in the overall structure of the field showed that the word 'Science' is the most significant and pivotal keyword among the nodes in the co-word network of LIS literature, and in this respect, the word 'Library' is in the second place. However, the results of the social network analysis uncovered that in spite of the high frequency of the word 'library', the pivotal role of the term has been exposed to decline over the time. The results of the analysis of co-word clusters showed that 'information seeking and retrieval' is the most important research focus in the intellectual structure of LIS literature during 1990-- 2016. Also, analysis of the hot spots of the LIS research based on Kleinberg algorithm indicated that the words 'Internet' and 'World Wide Web' have attracted the most attention by LIS scholars during the years under study. [ABSTRACT FROM AUTHOR]

Text summarization solves the problem of presenting the information needed by a user in a compact form. There are different approaches to creating well-formed summaries. One of the newest methods is the Latent Semantic Analysis (LSA). In this paper, different LSA-based summarization algorithms are explained, two of which are proposed by the authors of this paper. The algorithms are evaluated on Turkish and English documents, and their performances are compared using their ROUGE scores. One of our algorithms produces the best scores and both algorithms perform equally well on Turkish and English document sets. [ABSTRACT FROM PUBLISHER]

As more and more documents become electronically available, finding documents in large databases that fit users' needs is becoming increasingly important. In the past, the document search problem was dealt with using the database query approach or the text-based search approach. In this paper, we investigate this problem, focusing on the SCI/SSCI databases from ISI. Specifically, we design our search methodology based on the four fields commonly seen in a scientific research document: abstract, title, keywords, and reference list. Of these four, only the abstract field can be viewed as a normal text, while the other three have their own characteristics to differentiate them from texts. Therefore, we first develop a method to compute the similarity value for each field. Our next problem is combining the four similarity values into a final value. One approach is to assign weights to each and compute the weighted sum. We have not adopted this simple weighting method, however, because it is difficult to determine appropriate weights. Instead, we use the back propagation neural network to combine them. Finally, extensive experiments have been carried out using real documents drawn from TKDE journal, and the results indicate that in all situations our method has a much higher accuracy than the traditional text-based search approach. [ABSTRACT FROM AUTHOR]

To be faced with a document collection and not to be able to find the information you know exists somewhere within it is a problem as old as the existence of document collections. Information architecture is the discipline dealing with the modern version of this problem: how to organize web sites so that users can actually find what they are looking for. Information architects have so far applied known and well-tried tools from library science to solve this problem, and now topic maps are sailing up as another potential tool for information architects. This raises the question of how topic maps compare with the traditional solutions, and that is the question this paper attempts to address. The paper argues that topic maps go beyond the traditional solutions in the sense that they provide a framework within which they can be represented as they are, but also extended in ways which significantly improve information retrieval. [ABSTRACT FROM AUTHOR]

A similarity measure is used in information retrieval systems to retrieve and rank the relevant documents. In this paper, a new fuzzy-based approach to develop hybrid similarity measure is proposed and implemented. The proposed approach overcomes the limitations of extensively used similarity measures such as Cosine, Jaccard, Euclidean and Okapi-BM25 along with Genetic Algorithm-based hybrid similarity measures proposed by researchers. This approach uses fuzzy rules to infer the weights of different similarity measures. In this paper, the experiments are performed on CACM and CISI benchmark data collections. The performance of the proposed approach is evaluated in terms of precision, recall and average precision and average recall of retrieved relevant documents. The results are compared with different similarity measures available in literature. The results show the marked improvement in performance of information retrieval systems using the proposed fuzzy logic-based hybrid similarity measure. [ABSTRACT FROM PUBLISHER]

Describes how database tomography can be used to enhance information retrieval from large textual databases through the process of simulated nucleation. Principles of simulated nucleation; Primary uses of database tomography; Information on a high-quality information retrieval approach; Overview of database tomography.

The information explosion on the Internet has placed high demands on search engines. Despite the improvements in search engine technology, the precision of current search engines is still unsatisfactory. Moreover, the queries submitted by users are short, ambiguous and imprecise. This leads to a number of problems in dealing with similar queries. The problems include lack of common keywords, selection of different documents by the search engine and lack of common clicks etc. These problems render the traditional query clustering methods unsuitable for query recommendations. In this paper, we propose a new query recommendation system. For this, we have identified conceptually related queries by capturing users’ preferences using click-through graphs of web search logs and by extracting the best features, relevant to the queries, from the snippets. The proposed system has an online feature extraction phase and an offline phase in which feature filtering and query clustering are performed. Query clustering is carried out by a new tripartite agglomerative clustering algorithm, Query-Document-Concept Clustering, in which the documents are used innovatively to decouple queries and features/concepts in a tripartite graph structure. This results in clusters of similar queries, associated clusters of documents and clusters of features. We model the query recommendation problem in four different ways. Two models are non-personalized and personalized content-ignorant models. Other two are non-personalized and personalized content-aware models. Three similarity measures are introduced to estimate different kinds of similarities. Experimental results show that the proposed approach has better precision, recall and F-measure than the existing approaches. [ABSTRACT FROM PUBLISHER]

Disambiguation between multiple translation choices is very important in dictionary-based cross-language information retrieval. In prior work, disambiguation techniques have used term co-occurrence statistics from the collection being searched. Experimentally these techniques have worked well but are based upon heuristic assumptions. In this paper, a theoretically grounded alternative is proposed, one which uses sense disambiguation based upon context terms within the source text. Specifically this paper introduces the concept of translation probabilities incorporating a context term and extends the IBM Model 1 for estimating context-based translation probabilities from a sentence-aligned bilingual corpus. Experimental results in English to Italian bilingual searches show significant performance improvement of the context-based translation probabilities over the case without any disambiguation. [ABSTRACT FROM AUTHOR]

This paper is a personal view of the development of the role(s) of information professionals in the 50 years since ITS was founded. One of the justifications for the formation of ITS was that the then current professional bodies were not relevant to the needs of those employed in industrial information services. This paper reviews briefly the situation when ITS was formed, from the viewpoint of professional requirements and the developments since. The conclusion is drawn that today's information world, very different from that of 50 years ago, faces similar problems of a lack of suitable qualifications and a confusion in the roles of different actors in the field. [ABSTRACT FROM AUTHOR]

The paper presents a new method for handling of morphological variation of query terms in best-match IR. The method is based on enhanced inflectional stems. Use of inflectional stems has earlier been shown to be a good retrieval method in inflected indexes in a best-match environment for a highly inflected and compound-rich language, Finnish. In this paper the earlier stem method is elaborated upon by enhancing the stems with regular expressions. Contrary to our expectations the results show that the enhanced stem queries do not outperform basic inflectional stems, but neither are they considerably worse with long queries. With short web-like queries they perform relatively better than with long queries and outperform clearly stemming (Finnish stemmer of Snowball) and plain, unprocessed query words. The main benefits of the proposed method, besides fairly good precision and recall (P-R) performance, are shorter and more manageable queries, which is of practical importance, e.g. with large web indexes. [ABSTRACT FROM AUTHOR]

Pseudo relevance feedback-based query expansion is a popular automatic query expansion technique. However, a survey of work done in the area shows that it has a mixed chance of success. This paper captures the limitations of pseudo relevance feedback (PRF)-based query expansion and proposes a method of enhancing its performance by hybridizing corpus-based information, with a genetic fuzzy approach and semantic similarity notion. First the paper suggests use of a genetic fuzzy approach to select an optimal combination of query terms from a pool of terms obtained using PRF-based query expansion. The query terms obtained are further ranked on the basis of semantic similarity with original query terms. The experiments were performed on CISI collection, a benchmark dataset for information retrieval. It was found that the results were better in both terms of recall and precision. The main observation is that the hybridization of various techniques of query expansion in an intelligent way allows us to incorporate the good features of all of them. As this is a preliminary attempt in this direction, there is a large scope for enhancing these techniques. [ABSTRACT FROM PUBLISHER]

Most research in Arabic roots extraction focuses on removing affixes from Arabic words. This process adds processing overhead and may remove non-affix letters, which leads to the extraction of incorrect roots. This paper advises a new approach to dealing with this issue by introducing a new algorithm for extracting Arabic words’ roots. The proposed algorithm, which is called the Word Substring Stemming Algorithm, does not remove affixes during the extraction process. Rather, it is based on producing the set of all substrings of an Arabic word, and uses the Arabic roots file, the Arabic patterns file and a concrete set of rules to extract correct roots from substrings. The experiments have shown that the proposed approach is competitive and its accuracy is 83.9%, Furthermore, its accuracy can be enhanced more in the sense that, for about 9.9% of the tested words, the WSS algorithm retrieves two candidates (in most cases) for the correct root. [ABSTRACT FROM PUBLISHER]

This paper analyses selected literature on basic-level categories, explores related theories and discusses theoretical explanations of the phenomenon of basic-level categories. A substantial body of research has proposed that basic-level categories are the first categories formed during perception of the environment, the first learned by children and those most used in language. Experimental studies suggest that high-level (or superordinate) categories lack informativeness because they are represented by only a few attributes and low-level (or subordinate) categories lack cognitive economy because they are represented by too many attributes. Studies in library and information science have demonstrated the prevalence of basic-level categories in knowledge organization and representation systems such as thesauri and in image indexing and retrieval; and it has been suggested that the universality of basic-level categories could be used for building crosswalks between classificatory systems and user-centred indexing. However, while there is evidence of the pervasiveness of basic-level categories, they may actually be unstable across individuals, domains or cultures and thus unable to support broad generalizations. This paper discusses application of Heidegger’s notion of handiness as a framework for understanding the relational nature of basic-level categories. [ABSTRACT FROM PUBLISHER]

In the scientific field, mathematical formulae are a significant factor in communicating the ideas and the fundamental principles of any scientific knowledge. Nowadays, the scientific research community generates a huge number of documents that comprise both textual and mathematical formulae. For the retrieval of textual information, numerous retrieval systems are present that generate excellent results. Nevertheless, these textual information retrieval systems are insufficient to handle the structure and scripting styles of the mathematical formulae. The recent past has perceived the research, which intends to retrieve the textual and mathematical formulae, but their impoverished results are symptomatic to the scope of improvement. In this article, we have implemented the formula-embedding approach, which encodes the formulae into fixed dimensional embedding vectors. For encoding of formula, we have used universal sentence encoder–based sentence-embedding model, which relies on transformer architecture and deep averaging network. The proposed models take the latex formula as an input and produce an output of fixed dimensional embedding representation. To achieve more promising results, the transformer model follows stacked self-attentions, point-wise fully connected layers and positional encoding for both the encoder and decoder. The obtained results have been compared with state-of-the-art existing approaches, and the comparison study revealed that the proposed approach offers better retrieval accuracy in terms of n D C G ′ = 0.217, M A P ′ = 0.178 and P@10 = 0.378 measures. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented in which the editor discusses various reports published within the issue including one on information retrieval studies, another on the use of cluster analysis and naive Bayesian classification methods with the lexical, phonological and grammatical data collected in the BBC Voices project on the use of the English language in Great Britain, and another one on paper-based maps and mapping techniques.

This paper evaluates the assumption that users expect search engines to retrieve the same results for queries regardless of the language or the location of the originator. The dependency of the Google search engine on the language and location from which the query is submitted has been evaluated. The most popular queries in Arabic language were selected and translated into English for comparison using the Google translator. When studying keyword traffic on both Google search based keyword tool and Google Insights for Search, results showed that 67% of the Arab Internet users prefer to use English queries instead of their Arabic counterpart. When studying Google responses to some popular queries we have found that Google ranking algorithm depends on the language of the query more than on the keyword popularity. Although results justify search engines’ favouritism of giving documents in English priority over those of other languages, nonetheless, future search engine indexers should separate the document language from its content in a structure that makes the language a pluggable attribute for those indexed documents. [ABSTRACT FROM PUBLISHER]

This paper reports on the findings of a user study which investigated the effects of structure, highlighting and quantity of information on university students’ interaction with metadata surrogates of learning object repositories (LORs) during the relevance judgement process. The literature review revealed a lack of studies investigating how metadata surrogates should be designed to meet users’ needs and improve the effectiveness of relevance judgement. In order to address this issue, different versions of a prototype system, called META-LOR 2, were developed and evaluated in terms of the time needed for users to find relevant information, the accuracy of their relevance judgements and their satisfaction. To evaluate the prototype, a user study was conducted where participants were asked to complete a set of tasks and fill in satisfaction questionnaires. The findings showed that participants performed better and were more satisfied with those versions of the prototype which highlighted metadata elements that included the query terms, used clusters or categories to organize metadata elements in the surrogates and included metadata elements which were relevant to the query or task at hand. The paper concludes with some recommendations for improving the design of metadata surrogates in search result interfaces. [ABSTRACT FROM PUBLISHER]

Search engines have become the most important medium for Internet users to find pages on the web. They help customers to decrease their information overload, and enhance the sales of commercial web sites in different ways. For these reasons, the exploration of and changes in (human) online searching behaviour has become a subject of particular importance. This paper will help search engine and web site administrators and developers to monitor online searching behaviour properly and to derive strategies from the information gained. We define standard parameters against which search engines can be measured and compared. These parameters also reflect the online searching behaviour of search engine users. Therefore, an overview of studies conducted in the last few years is given. Statistics used in different papers are compared to extract standard parameters for online searching behaviour. In the next step, search queries from four different search engines covering periods of between 10 and 13 months are compared using these parameters. Using an automatic process, we retrieved around 99% of all search queries from three different search engine live tickers. For the first time, different data sets over a long period are compared. We observe that some patterns stay stable in a number of different search engines, and shed light on patterns that shorter analyses could not adequately examine. Our observations do not support the assumption that web search has become more business driven. For this reason, we introduce the concept of evergreens in search queries. One implication is that search engines should simplify web search interfaces for users since Boolean operators and special search features are rarely used. We also present the evergreen topics in search queries. [ABSTRACT FROM AUTHOR]

This paper seeks to provide a brief overview of those developments which have taken the theory and practice of image and video retrieval into the digital age. Drawing on a voluminous literature, the context in which visual information retrieval takes place is followed by a consideration of the conceptual and practical challenges posed by the representation and recovery of visual material on the basis of its semantic content. An historical account of research endeavours in content-based retrieval, directed towards the automation of these operations in digital image scenarios, provides the main thrust of the paper. Finally, a look forwards locates visual information retrieval research within the wider context of content-based multimedia retrieval. [ABSTRACT FROM AUTHOR]

This paper explores the history of 'the social' in information science. It traces the influence of social scientific thinking on the development of the field's intellectual base. The continuing appropriation of both theoretical and methodological insights from domains such as social studies of science, science and technology studies, and socio-technical systems is discussed. [ABSTRACT FROM AUTHOR]

This paper is a personal take on the history of evaluation experiments in information retrieval. It describes some of the early experiments that were formative in our understanding, and goes on to discuss the current dominance of TREC (the Text REtrieval Conference) and to assess its impact. [ABSTRACT FROM AUTHOR]

This paper describes research that aims to define the information needs of mobile individuals, to implement a mobile information system that can satisfy those needs, and finally to evaluate the performance of that system with end-users. First a review of the emerging discipline of geographic information retrieval (GIR) is presented as background to the more specific issue of mobile information retrieval. Following this, a user needs study is described evaluating the requirements of potential users of a mobile information system; the study finds that there is a strong geographic component to users' information needs. Next, four geographic post-query filters are described which attempt to represent the region of space associated with an individual's query made at some specific spatial location. These filters are spatial proximity (distance in space), temporal proximity (travel time), speed-heading prediction surfaces (likelihood of visiting locations) and visibility (locations that can be seen). Two of these filters — spatial proximity and speed-heading prediction surfaces — are implemented in a mobile information system and subsequently evaluated with users in an outdoor setting. The results of evaluation suggest that retrieved information to which post-query geographic filters have been applied is considered more relevant than unfiltered information, and that users find information sorted by spatial proximity to be more relevant than that sorted by a prediction surface of likely future locations. The paper closes with a discussion of the wider implications of these results for developers of mobile information systems and location-based services. [ABSTRACT FROM AUTHOR]

Digital libraries offer diverse information resources in digital format. These resources may include any digital collection of books, journals, papers, proceedings and so on. The major challenge for the library is to provide access to all these resources in an integrated and user-friendly way. It is essential to provide an effective method for the users to enable them to make concept-based queries on these resources. In order to help the information users, we propose an index-based model, ATVLIB. This model has a search engine that uses data structures such as signature pattern, hash table and B+ trees. Extensive study shows that the proposed model has better performance than the existing method. [ABSTRACT FROM AUTHOR]

Context has long been considered very useful to help the user assess the actual relevance of a document. In web searching, context can help assess the relevance of a web page by showing how the page is related to other pages in the same web site, for example. Such information is very difficult to convey and visualize in a user friendly way. In this paper we present the design, implementation and evaluation of a graphical visualization tool aimed at helping users to determine the relevance of a web page by displaying the structure of the web site the page belongs to. The results of an initial evaluation suggest that this visualization technique helps the user navigate large web sites and find useful information in an effective way, without increasing the cognitive load of the user. [ABSTRACT FROM AUTHOR]

Exact match queries, wildcard match queries, and k- mismatch queries are widely used in various molecular biology applications including the searching of ESTs (Expressed Sequence Tags) and DNA transcription factors. In this paper, we suggest an efficient indexing and processing mechanism for such queries. Our indexing method places a sliding window at every possible location of a DNA sequence and extracts its signature by considering the occurrence frequency of each nucleotide. It then stores a set of signatures using a multi-dimensional index such as the R*-tree. Also, by assigning a weight to each position of a window, it prevents signatures from being concentrated around a few spots in indexing space. Our query processing method converts a query sequence into a multi-dimensional rectangle and searches the index for the signatures overlapping with the rectangle. Experiments with real biological data sets have revealed that the proposed approach is at least 4.4 times, 2.1 times, and several orders of magnitude faster than the previous one in performing exact match, wildcard match, and k-mismatch queries, respectively. [ABSTRACT FROM AUTHOR]

The paper traces the development of user-based research in the Department of Information Studies at the University of Sheffield over the past 40 years, and highlights and assesses the different research questions addressed and main characteristics of projects undertaken. Four phases of user-based studies relating to information seeking and retrieval are identified including: preliminary studies on library users; empirical studies on information user groups by the Centre for research on User Studies; the development of a theoretical framework for user studies; and the integration of information seeking and retrieval research. The review concludes that user-based studies have evolved linking together studies on information use and provision, information seeking behaviour and the design and evaluation of actual retrieval systems. The unifying element has been the emphasis placed on methodological issues. More recent work is transcending user-orientated and system perspectives giving way to a more integrated approach and emergent interaction paradigm for user studies. [ABSTRACT FROM AUTHOR]

Discusses methods of information retrieval using the Web of Science via a Web server with pulmonary hypertension as a subject example. Boolean search; Bibliographic details; Provision of a fast and useful retrieval mechanism.

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]

Blogs are one of the main user-generated contents on the web and are growing in number rapidly. The characteristics of blogs require the development of specialized search methods which are tuned for the blogosphere. In this paper, we focus on blog retrieval, which aims at ranking blogs with respect to their recurrent relevance to a user’s topic. Although different blog retrieval algorithms have already been proposed, few of them have considered temporal properties of the input queries. Therefore, we propose an efficient approach to improving relevant blog retrieval using temporal property of queries. First, time sensitivity of each query is automatically computed for different time intervals based on an initially retrieved set of relevant posts. Then a temporal score is calculated for each blog and finally all blogs are ranked based on their temporal and content relevancy with regard to the input query. Experimental analysis and comparison of the proposed method are carried out using a standard dataset with 45 diverse queries. Our experimental results demonstrate that, using different measurement criteria, our proposed method outperforms other blog retrieval methods. [ABSTRACT FROM AUTHOR]

Search engines assist users in expressing their information needs more accurately by reformulating the issued queries automatically and suggesting the generated formulations to the users. Many approaches to query suggestion draw on the information stored in query logs, recommending recorded queries that are textually similar to the current user’s query or that frequently co-occurred with it in the past. In this paper, we propose an approach that concentrates on deducing the actual information need from the user’s query. The challenge therein lies not only in processing keyword queries, which are often short and possibly ambiguous, but especially in handling the complexity of natural language that allows users to express the same or similar information needs in various differing ways. We expect a higher-level semantic representation of a user’s query to more accurately reflect the information need than the explicit query terms alone can. To this aim, we employ latent Dirichlet allocation as a probabilistic topic model to reveal latent semantics in the query log. Our evaluations show that, whereas purely topic-based query suggestion performs the worst, the interpolation of our proposed topic-based model with the baseline word-based model that generates suggestions based on matching query terms achieves significant improvements in suggestion quality over the already well performing purely word-based approach. [ABSTRACT FROM AUTHOR]

Finding the k-nearest neighbours of every node in a dataset is one of the most important data operations with wide application in various areas such as recommendation and information retrieval. However, a major challenge is that the execution time of existing approaches grows rapidly as the number of nodes or dimensions increases. In this paper, we present greedy filtering, an efficient and scalable algorithm for finding an approximate k-nearest neighbour graph. It selects a fixed number of nodes as candidates for every node by filtering out node pairs that do not have any matching dimensions with large values. Greedy filtering achieves consistent approximation accuracy across nodes in linear execution time. We also present a faster version of greedy filtering that uses inverted indices on the node prefixes. Through theoretical analysis, we show that greedy filtering is effective for datasets whose features have Zipfian distribution, a characteristic observed in majority of large datasets. We also conduct extensive comparative experiments against (a) three state-of-the-art algorithms, and (b) three algorithms in related research domains. Our experimental results show that greedy filtering consistently outperforms other algorithms in various types of high-dimensional datasets. [ABSTRACT FROM AUTHOR]

Approximate string matching (ASM) is a challenging problem, which aims to match different string expressions representing the same object. In this paper, detailed experimental studies were conducted on the subject of toponym matching, which is a new domain where ASM can be performed, and the creation of a single string-matching measure that can perform toponym matching process regardless of the language was attempted. For this purpose, an ASM measure called DAS, which comprises name similarity, word similarity and sentence similarity phases, was created. Considering the experimental results, the retrieval performance and system accuracy of DAS were much better than those of other well-known five measures that were compared on toponym test datasets. In addition, DAS had the best metric values of mean average precision in six languages, and precision/recall graphs confirm this result. [ABSTRACT FROM AUTHOR]

The paper surveys empirical studies on the relations between information searching and learning, and presents some reflections about learning in a search process based on the findings. First, the meaning of the concepts ‘learning’ and ‘searching’ is briefly defined. Learning is conceptualized as changes in one’s knowledge structures. Then it is described more in detail how learning occurs in the search process. The point of departure is to focus on tasks that require the restructuring of knowledge structures and to analyse how gradual stabilization of those structures is related to accessing and interacting with information sources. After that, empirical studies on searching and learning are categorized by identifying independent and dependent variables in those studies. In conclusion, some general remarks on the topic are presented. [ABSTRACT FROM AUTHOR]

The increasing popularity of the social networking service, Twitter, has made it more involved in day-to-day communications, strengthening social relationships and information dissemination. Conversations on Twitter are now being explored as indicators within early warning systems to alert of imminent natural disasters such as earthquakes and aid prompt emergency responses to crime. Producers are privileged to have limitless access to market perception from consumer comments on social media and microblogs. Targeted advertising can be made more effective based on user profile information such as demography, interests and location. While these applications have proven beneficial, the ability to effectively infer the location of Twitter users has even more immense value. However, accurately identifying where a message originated from or an author’s location remains a challenge, thus essentially driving research in that regard. In this paper, we survey a range of techniques applied to infer the location of Twitter users from inception to state of the art. We find significant improvements over time in the granularity levels and better accuracy with results driven by refinements to algorithms and inclusion of more spatial features. [ABSTRACT FROM AUTHOR]

The widespread use of colloquial dialects among the younger generation of Arabs is depriving many of them the fruits of information freedom. Although most Arabs have no problem with reading text in formal Arabic, widely known as Modern Standard Arabic (MSA), the younger generation is more adept at colloquial Arabic, mainly owing to the widespread use of social media. The current search engines cater mostly to MSA. This means that materials written in colloquial are off-limits to those who use MSA, and similarly the MSA contents are off-limits for those who communicate in colloquial only. To achieve the full potential of an information-retrieval system, we need a successful scheme that interprets queries whether they are in MSA, colloquial Arabic or a combination of both. In this paper we design an information-retrieval system that addresses our concern against the backdrop of one of the local dialects in Saudi Arabia. Our system is based on modifying an MSA stemming technique and a set of colloquial ↔ MSA conversion rules that are lexicon based. We tested the system using 44 queries on a corpus of over 1400 documents (MSA, colloquial, mix). The average precision was 84.3%, while the average recall was 96.5%. In the second test we compared the precision of the retrieved documents by our system vs Google and Yahoo! search engines. The respective average precisions were 78.2, 51.9 and 56.2%. [ABSTRACT FROM AUTHOR]

Mobile phones have been identified as one of the technologies that can be used to overcome the challenges of information dissemination regarding serious diseases. Short message services, a much used function of cell phones, for example, can be turned into a major tool for accessing databases. This paper focuses on the design and development of a short message services-based information access algorithm to carefully screen information on human immunodeficiency virus/acquired immune deficiency syndrome within the context of a frequently asked questions system. However, automating the short message services-based information search and retrieval poses significant challenges because of the inherent noise in its communications. The developed algorithm was used to retrieve the best-ranked question–answer pair. Results were evaluated using three metrics: average precision, recall and computational time. The retrieval efficacy was measured and it was confirmed that there was a significant improvement in the results of the proposed algorithm when compared with similar retrieval algorithms. [ABSTRACT FROM PUBLISHER]

Stop word identification is one of the most important tasks for many text processing applications such as information retrieval. Stop words occur too frequently in documents in a collection and do not contribute significantly to determining the context or information about the documents. These words are worthless as index terms and should be removed during indexing as well as before querying by an information retrieval system. In this paper, we propose an automatic aggregated methodology based on term frequency, normalized inverse document frequency and information model to extract the light stop words from Persian text. We define a ‘light stop word’ as a stop word that has few letters and is not a compound word. In the Persian language, a complete stop word list can be derived by combining the light stop words. The evaluation results, using a standard corpus, show a good percentage of coincidence between the Persian and English stop words and a significant improvement in the number of index terms. Specifically, the first 32 Persian light stop words have a great impact on the index size reduction and the set of stop words can reduce the number of index terms by about 27%. [ABSTRACT FROM PUBLISHER]

One of the main issues associated with search engines is the query–document vocabulary mismatch problem, a long-standing problem in Information Retrieval (IR). This problem occurs when a user query does not match the content of stored documents, and it affects most search tasks. Automatic query expansion (AQE) is one of the most common approaches used to address this problem. Various AQE techniques have been proposed; these mainly involve finding synonyms or related words for the query terms. Word embedding (WE) is one of the methods that are currently receiving significant attention. Most of the existing AQE techniques focus on expanding the individual query terms rather the entire query during the expansion process, and this can lead to query drift if poor expansion terms are selected. In this article, we introduce Deep Averaging Networks (DANs), an architecture that feeds the average of the WE vectors produced by the Word2Vec toolkit for the terms in a query through several linear neural network layers. This average vector is assumed to represent the meaning of the query as a whole and can be used to find expansion terms that are relevant to the complete query. We explore the potential of DANs for AQE in Arabic document retrieval. We experiment with using DANs for AQE in the classic probabilistic BM25 model as well as for two recent expansion strategies: Embedding-Based Query Expansion approach (EQE1) and Prospect-Guided Query Expansion Strategy (V2Q). Although DANs did not improve all outcomes when used in the BM25 model, it outperformed all baselines when incorporated into the EQE1 and V2Q expansion strategies. [ABSTRACT FROM AUTHOR]