Search

The inverted index is the backbone of modern web search engines. For each word in a collection of web documents, the index records the list of documents where this word occurs. Given a set of query words, the job of a search engine is to output a ranked list of the most relevant documents containing the query. However, if the query consists of an arbitrary string--which can be a partial word, multiword phrase, or more generally any sequence of characters--then word boundaries are no longer relevant and we need a different approach. In string retrieval settings, we are given a set D = {d1, d2, d3,..., dD} of D strings with n characters in total taken from an alphabet set Σ = [σ], and the task of the search engine, for a given query pattern P of length p, is to report the "most relevant" strings in D containing P. The query may also consist of two or more patterns. The notion of relevance can be captured by a function score(P, dr ), which indicates how relevant document dr is to the pattern P. Some example score functions are the frequency of pattern occurrences, proximity between pattern occurrences, or pattern-independent PageRank of the document. The first formal framework to study such kinds of retrieval problems was given by Muthukrishnan [SODA 2002]. He considered two metrics for relevance: frequency and proximity. He took a threshold-based approach on these metrics and gave data structures that use O(n log n) words of space. We study this problem in a somewhat more natural top-k framework. Here, k is a part of the query, and the top k most relevant (highest-scoring) documents are to be reported in sorted order of score. We present the first linear-space framework (i.e., using O(n) words of space) that is capable of handling arbitrary score functions with near-optimal O(p + k log k) query time. The query time can be made optimal O(p + k) if sorted order is not necessary. Further, we derive compact space and succinct space indexes (for some specific score functions). This space compression comes at the cost of higher query time. At last, we extend our framework to handle the case of multiple patterns. Apart from providing a robust framework, our results also improve many earlier results in index space or query time or both. [ABSTRACT FROM AUTHOR]

Radio frequency identification (RFID) technology has long been known for its ability to uniquely identify objects. Recent years have witnessed a significant increase in storage capacity on the tag, which is giving rise to a new set of application scenarios. As the tag itself can carry relevant context information, processes can be managed locally rather than relying on a centralised system infrastructure. This in turn results in a massive interoperability challenge. We propose to solve this problem by combining RFID technology with ontologies to create smart objects in the context of manufacturing process control. The idea is to store information originating from an SAP ERP system using the PSL Ontology (ISO 18629) for representing processes and time directly on the RFID tags. As an item flows through a manufacturing process, information about the item can be stored on its tag. This information, along with the PSL axioms, can be used to make inferences about the manufacturing process and the item in particular. In this paper, we discuss our formalisation of an ontology for the SAP data model and show an example of translating data from an SAP ERP system into PSL axioms, and answering queries about a manufacturing process. [ABSTRACT FROM AUTHOR]

Context: The information retrieval research provides alternatives to recover traces from existing software information. In the software product line (SPL) engineering context, further research on information retrieval methods is required to explore the existence of products' source code and support the SPL adoption by providing traceability information. Objective: This work evaluates the methods' retrieval quality targeting the extraction of feature-code trace links in a set of variant-rich software development projects. Method: We propose a research method comprising two experiments with five information retrieval methods (Classic vector, Latent semantic indexing, Neural network, Extended boolean and BM25), applied to forty-one projects. The SPLTrac suite automates the research operation, using the information retrieval methods results as input to perform a five-step quantitative data analysis procedure based on parametric and non-parametric statistical techniques. The quality measurement is expressed by four dependent variables: precision, recall, F-measure and execution time. Results: With a homogeneous result for execution time between methods, there are discrepancies for the other metrics. While Extended boolean presents the best results for precision and F-measure, BM25 provides the greatest recall results. Conclusions: Such evidence indicates there is not a dominant method in terms of retrieval quality, since they need further improvements to achieve better performance in all quality perspectives. [ABSTRACT FROM AUTHOR]

The article explores how learning theory affects classroom behavior in law schools and advocates for the integration of spaced retrieval into the law school curriculum. Topics discussed are studies supporting spaced retrieval and its component parts of spaced repetition and retrieval theory, studies showing the effectiveness of spaced retrieval, strength of spaced retrieval research, and classroom exercises, experiential learning tools and assessments that facilitate spaced retrieval adoption.

Shredded document recovery is an important research sub-field of information security. The paper document is broken into a large number of similar shreds by the shredder. Usually these shreds imply some important information. If they are restored, it will have an important impact on judicial investigation, military command, and archaeological research. Therefore, it has a great value in research of shredded document recovery. In consideration of large differences between languages and great demands for Chinese document recovery, our study will focus on the shredded Chinese document. In this paper, a method of shredded document recovery based on character stroke feature is proposed. We use horizontal strokes to match Chinese characters. Finally the shredded document is restored. In the test of actual sample, the method proposed in this paper has achieved good recovery result. The average accuracy is 13.96% higher than the traditional algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we present a supervised dictionary learning method for optimizing the feature-based Bag-of-Words (BoW) representation towards Information Retrieval. Following the cluster hypothesis, which states that points in the same cluster are likely to fulfill the same information need, we propose the use of an entropy-based optimization criterion that is better suited for retrieval instead of classification. We demonstrate the ability of the proposed method, abbreviated as EO-BoW, to improve the retrieval performance by providing extensive experiments on two multi-class image datasets. The BoW model can be applied to other domains as well, so we also evaluate our approach using a collection of 45 time-series datasets, a text dataset, and a video dataset. The gains are three-fold since the EO-BoW can improve the mean Average Precision, while reducing the encoding time and the database storage requirements. Finally, we provide evidence that the EO-BoW maintains its representation ability even when used to retrieve objects from classes that were not seen during the training. [ABSTRACT FROM PUBLISHER]

Motivation: Medical Subject Headings (MeSH) indexing, which is to assign a set of MeSH main headings to citations, is crucial for many important tasks in biomedical text mining and information retrieval. Large-scale MeSH indexing has two challenging aspects: the citation side and MeSH side. For the citation side, all existing methods, including Medical Text Indexer (MTI) by National Library of Medicine and the state-of-the-art method, MeSHLabeler, deal with text by bag-of-words, which cannot capture semantic and context-dependent information well. Methods: We propose DeepMeSH that incorporates deep semantic information for large-scale MeSH indexing. It addresses the two challenges in both citation and MeSH sides. The citation side challenge is solved by a new deep semantic representation, D2V-TFIDF, which concatenates both sparse and dense semantic representations. The MeSH side challenge is solved by using the 'learning to rank' framework of MeSHLabeler, which integrates various types of evidence generated from the new semantic representation. Results: DeepMeSH achieved a Micro F-measure of 0.6323, 2% higher than 0.6218 of MeSHLabeler and 12% higher than 0.5637 of MTI, for BioASQ3 challenge data with 6000 citations. [ABSTRACT FROM AUTHOR]

Due to an increasing demand for video surveillance, there is an explosive growth of surveillance videos, which causes a big challenge in video storage, browsing, and retrieval. The video synopsis technique is thus developed to extract and rearrange the moving objects so as to handle the massive video browsing challenge. However, the traditional video synopsis (TVS) method only considers the processing videos captured by a single camera, ignoring object interactions in multicamera videos. To address this issue, we propose a novel multicamera joint video synopsis (JVS) algorithm for multicamera surveillance videos. First, a key time stamp (KTS) selection method is designed to find an object’s appearing, merging, splitting, and disappearing moments in the frame sequence, called tube, of that object. Second, tubes are rearranged by minimizing a global energy function that involves the overall camera views. Compared with the energy function used in TVS, the proposed global energy function considers the chronological orders of tubes not only in the same camera view but also among different camera views. Moreover, the chronological disorder cost term is formulated based on the KTS labels, and improved by considering the visual similarity between two tubes. Finally, the multicamera synopsis videos are separately generated by stitching together the globally rearranged tubes and background images of the same camera view. Extensive experiments show that the proposed JVS method is better than the traditional single-camera video synopsis method in preserving the chronological orders of moving objects among multicamera synopsis videos. [ABSTRACT FROM PUBLISHER]

Bug fixing has a key role in software quality evaluation. Bug fixing starts with the bug localization step, in which developers use textual bug information to find location of source codes which have the bug. Bug localization is a tedious and time consuming process. Information retrieval requires understanding the programme's goal, coding structure, programming logic and the relevant attributes of bug. Information retrieval (IR) based bug localization is a retrieval task, where bug reports and source files represent the queries and documents, respectively. In this paper, we propose BugCatcher, a newly developed bug localization method based on multi-level re-ranking IR technique. We evaluate BugCatcher on three open source projects with approximately 3400 bugs. Our experiments show that multi-level reranking approach to bug localization is promising. Retrieval performance and accuracy of BugCatcher are better than current bug localization tools, and BugCatcher has the best Top N, Mean Average Precision (MAP) and Mean Reciprocal Rank (MRR) values for all datasets. [ABSTRACT FROM AUTHOR]

Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to deal with multimodal data, such as in image annotation tasks. Another popular approach to model the multimodal data is through deep neural networks, such as the deep Boltzmann machine (DBM). Recently, a new type of topic model called the Document Neural Autoregressive Distribution Estimator (DocNADE) was proposed and demonstrated state-of-the-art performance for text document modeling. In this work, we show how to successfully apply and extend this model to multimodal data, such as simultaneous image classification and annotation. First, we propose SupDocNADE, a supervised extension of DocNADE, that increases the discriminative power of the learned hidden topic features and show how to employ it to learn a joint representation from image visual words, annotation words and class label information. We test our model on the LabelMe and UIUC-Sports data sets and show that it compares favorably to other topic models. Second, we propose a deep extension of our model and provide an efficient way of training the deep model. Experimental results show that our deep model outperforms its shallow version and reaches state-of-the-art performance on the Multimedia Information Retrieval (MIR) Flickr data set. [ABSTRACT FROM PUBLISHER]

In this article, we investigate the cross-media retrieval between images and text, that is, using image to search text (I2T) and using text to search images (T2I). Existing cross-media retrieval methods usually learn one couple of projections, by which the original features of images and text can be projected into a common latent space to measure the content similarity. However, using the same projections for the two different retrieval tasks (I2T and T2I) may lead to a tradeoff between their respective performances, rather than their best performances. Different from previous works, we propose a modality-dependent cross-media retrieval (MDCR) model, where two couples of projections are learned for different cross-media retrieval tasks instead of one couple of projections. Specifically, by jointly optimizing the correlation between images and text and the linear regression from one modal space (image or text) to the semantic space, two couples of mappings are learned to project images and text from their original feature spaces into two common latent subspaces (one for I2T and the other for T2I). Extensive experiments show the superiority of the proposed MDCR compared with other methods. In particular, based on the 4,096-dimensional convolutional neural network (CNN) visual feature and 100-dimensional Latent Dirichlet Allocation (LDA) textual feature, the mAP of the proposed method achieves the mAP score of 41.5%, which is a new state-of-the-art performance on the Wikipedia dataset. [ABSTRACT FROM AUTHOR]

Since the last two decades, XML has gained momentum as the standard for web information management and complex data representation. Also, collaboratively built semi-structured information resources, such as Wikipedia, have become prevalent on the Web and can be inherently encoded in XML. Yet most methods for processing XML and semi-structured information handle mainly the syntactic properties of the data, while ignoring the semantics involved. To devise more intelligent applications, one needs to augment syntactic features with machine-readable semantic meaning. This can be achieved through the computational identification of the meaning of data in context, also known as (a.k.a.) automated semantic analysis and disambiguation, which is nowadays one of the main challenges at the core of the Semantic Web. This survey paper provides a concise and comprehensive review of the methods related to XML-based semi-structured semantic analysis and disambiguation. It is made of four logical parts. First, we briefly cover traditional word sense disambiguation methods for processing flat textual data. Second, we describe and categorize disambiguation techniques developed and extended to handle semi-structured and XML data. Third, we describe current and potential application scenarios that can benefit from XML semantic analysis, including: data clustering and semantic-aware indexing, data integration and selective dissemination, semantic-aware and temporal querying, web and mobile services matching and composition, blog and social semantic network analysis, and ontology learning. Fourth, we describe and discuss ongoing challenges and future directions, including: the quantification of semantic ambiguity, expanding XML disambiguation context, combining structure and content, using collaborative/social information sources, integrating explicit and implicit semantic analysis, emphasizing user involvement, and reducing computational complexity. [ABSTRACT FROM PUBLISHER]

In mobile communication, spatial queries pose a serious threat to user location privacy because the location of a query may reveal sensitive information about the mobile user. In this paper, we study approximate k nearest neighbor (kNN) queries where the mobile user queries the location-based service (LBS) provider about approximate k nearest points of interest (POIs) on the basis of his current location. We propose a basic solution and a generic solution for the mobile user to preserve his location and query privacy in approximate kNN queries. The proposed solutions are mainly built on the Paillier public-key cryptosystem and can provide both location and query privacy. To preserve query privacy, our basic solution allows the mobile user to retrieve one type of POIs, for example, approximate k nearest car parks, without revealing to the LBS provider what type of points is retrieved. Our generic solution can be applied to multiple discrete type attributes of private location-based queries. Compared with existing solutions for kNN queries with location privacy, our solution is more efficient. Experiments have shown that our solution is practical for kNN queries. [ABSTRACT FROM PUBLISHER]

CavBase is a database containing information about the three-dimensional geometry and the physicochemical properties of putative protein binding sites. Analyzing CavBase data typically involves computing the similarity of pairs of binding sites. In contrast to sequence alignment, however, a structural comparison of protein binding sites is a computationally challenging problem, making large scale studies difficult or even infeasible. One possibility to overcome this obstacle is to precompute pairwise similarities in an all-against-all comparison, and to make these similarities subsequently accessible to data analysis methods. Pairwise similarities, once being computed, can also be used to equip CavBase with a neighborhood structure. Taking advantage of this structure, methods for problems such as similarity retrieval can be implemented efficiently. In this paper, we tackle the problem of performing an all-against-all comparison using CavBase, consisting of more than 200,000 protein cavities, by means of parallel computation and cloud computing techniques. We present the conceptual design and technical realization of a large-scale study to create a similarity database called CavSimBase. We illustrate how CavSimBase is constructed, is accessed, and is used to answer biological questions by data analysis and similarity retrieval. [ABSTRACT FROM PUBLISHER]

The Earth Mover's Distance (EMD) similarity join has a number of important applications such as near duplicate image retrieval and distributed based pattern analysis. However, the computational cost of EMD is super cubic and consequently the EMD similarity join operation is prohibitive for datasets of even medium size. We propose to employ the Hadoop platform to speed up the operation. Simply porting the state-of-the-art metric distance similarity join algorithms to Hadoop results in inefficiency because they involve excessive distance computations and are vulnerable to skewed data distributions. We propose a novel framework, named Heads-Join, which transforms data into the space of EMD lower bounds and performs pruning and partitioning at a low cost because computing these EMD lower bounds has constant or linear complexity. We investigate both range and top-$k$ joins, and design efficient algorithms on three popular Hadoop computation paradigms, i.e., MapReduce, Bulk Synchronous Parallel, and Spark. We conduct extensive experiments on both real and synthetic datasets. The results show that Heads-Join outperforms the state-of-the-art metric similarity join technique, i.e., Quickjoin, by up to an order of magnitude and scales out well. [ABSTRACT FROM PUBLISHER]

We present scalable and parallel versions of Lipmaa's computationally-private information retrieval (CPIR) scheme [20] , which provides log-squared communication complexity. In the proposed schemes, instead of binary decision diagrams utilized in the original CPIR, we employ an octal tree based approach, in which non-sink nodes have eight child nodes. Using octal trees offers two advantages: i) a serial implementation of the proposed scheme in software is faster than the original scheme and ii) its bandwidth usage becomes less than the original scheme when the number of items in the data set is moderately high (e.g., 4,096 for 80-bit security level using Damgård-Jurik cryptosystem). In addition, we present a highly-optimized parallel algorithm for shared-memory multi-core/processor architectures, which minimizes the number of synchronization points between the cores. We show that the parallel implementation is about 50 times faster than the serial implementation for a data set with 4,096 items on an eight-core machine. Finally, we propose a hybrid algorithm that scales the CPIR scheme to larger data sets with small overhead in bandwidth complexity. We demonstrate that the hybrid scheme based on octal trees can lead to more than two orders of magnitude faster parallel implementations than serial implementations based on binary trees. Comparison with the original as well as the other schemes in the literature reveals that our scheme is the best in terms of bandwidth requirement. [ABSTRACT FROM PUBLISHER]

The development of information technology at the end of the 20th century fundamentally transformed people's daily lives and greatly influenced education as well, actually not all the undergraduate students of translation have the skills to access, utilize, and benefit from the continually updating online information. This poses a challenge to traditional orthodox translation teaching which is still dominant. Therefore, it is necessary to introduce Web Information Retrieval (WIR) skills to the translation learners. This research focuses on the translation curriculum reform and the effects of incorporating WIR in translation teaching. From data tests, questionnaires, and curriculum experiment prove that WIR incorporating significantly improves translation quality, translators and translation instructors both request that WIR skills should be incorporated in the teaching of language Translation. [ABSTRACT FROM AUTHOR]

With the rise of electronic channels it has become easier for businesses to consult various types of information sources in information-seeking processes. Governments are urged to rethink their role as reliable information source and the roles of their (electronic) service channels to provide efficient service support. This article addresses how governments cope with the availability of numerous sources and channels and focuses on similarities, differences and interdependencies between source and channel selection processes. Individual and group interviews were held with businesses throughout the Netherlands. The results indicate that some factors influence source and channel choices (e.g. task characteristics), others influence only channel choice (e.g. situational factors, channel characteristics). Source and relationship characteristics uncover interdependencies between both, since these source-related concepts influence channel choices. Further insight is needed to increase our understanding and come to an integrated theory of source and channel choices in information-seeking processes. Points for practitioners The channels used by the government are in their own control (i.e. information quality); however, other sources and their channels are outside their control. Management of these sources can be realized through strategic partnerships with crucial sources. The first implication is that governments provide content to other sources, which these sources can use for advice to its customers. The second implication concerns the use of cross-referrals between sources and their channels. For instance, the government website contains a recommendation to call an advisor for specific information since this source-channel combination provides only general information. [ABSTRACT FROM AUTHOR]

This article presents experimental results showing that 4- and 5-year-old children are capable of drawing free choice inferences from disjunctive statements and from statements containing free choice indefinites, despite not being able to compute inferences of exclusivity for disjunctive statements, or other scalar implicatures. The findings appear to challenge accounts that attempt to unify the two kinds of inferences (Kratzer & Shimoyama 2002; Alonso Ovalle 2005; Fox 2007; Klinedinst 2007; Chemla 2010; van Rooij 2010; Franke 2011; Chierchia 2013).We discuss, however, the compatibility of the child data with a recent approach in the experimental literature, which attributes children's failures to compute scalar implicatures to a difficulty with alternatives (Chierchia et al. 2001; Gualmini et al. 2001; Reinhart 2006; Barner et al. 2011; Singh et al. 2013). Based on the results of two experiments, we propose an explanation for children's selective success on scalar inferences, according to which scalar inferences are generally unproblematic for children, unless they necessitate lexical retrieval of the required alternatives. [ABSTRACT FROM AUTHOR]

Over the last few years, one major research direction of information retrieval includes user behaviour prediction. For that reason many models have been proposed aiming at the accurate evaluation of information retrieval systems and the best prediction of web search users׳ behaviour. In this paper we propose a new evaluation metric for information retrieval systems which employs two relevance factors; a relevance judgement grade and a popularity grade that represents users׳ vote for a document. We show that this new metric performs better than other evaluation metrics when expressing user behaviour. Moreover, in order to test the performance of our metric on different and scalable ranking algorithms, we develop a pairwise text similarity algorithm using cosine similarity, implemented on the MapReduce model, and then perform experiments on rankings generated by the algorithm. [ABSTRACT FROM AUTHOR]

Multimedia content sharing within social networks has become one of the most interesting and trending research fields over the last few years. This undoubted emerge of related research works is rather twofold, namely it includes both the analysis and management techniques of the content itself, as well as new ways for its accompanied meaningful interpretation and exploitation. In this paper, we review the recent advances in the above fields in the humanistic framework of the popular Flickr social network. In addition, the major research challenges in the area are demonstrated and discussed, which include current state-of-the-art approaches with respect to interesting humanistic data collection and interpretation research fields, such as multimedia information retrieval, (semi-) automatic tag manipulation, travel applications, semantic knowledge extraction, human activity tracking, as well as related benchmarking efforts. At the end of this survey, we also discuss the main challenges and propose a number of future research directions for interested fellow researchers to continue investigation in the field. [ABSTRACT FROM AUTHOR]

Describing visual image contents by semantic concepts is an effective and straightforward way to facilitate various high level applications. Inferring semantic concepts from low-level pictorial feature analysis is challenging due to the semantic gap problem, while manually labeling concepts is unwise because of a large number of images in both online and offline collections. In this paper, we present a novel approach to automatically generate intermediate image descriptors by exploiting concept co-occurrence patterns in the pre-labeled training set that renders it possible to depict complex scene images semantically. Our work is motivated by the fact that multiple concepts that frequently co-occur across images form patterns which could provide contextual cues for individual concept inference. We discover the co-occurrence patterns as hierarchical communities by graph modularity maximization in a network with nodes and edges representing concepts and co-occurrence relationships separately. A random walk process working on the inferred concept probabilities with the discovered co-occurrence patterns is applied to acquire the refined concept signature representation. Through experiments in automatic image annotation and semantic image retrieval on several challenging datasets, we demonstrate the effectiveness of the proposed concept co-occurrence patterns as well as the concept signature representation in comparison with state-of-the-art approaches. [ABSTRACT FROM PUBLISHER]

Recently, we have proposed a retrieval technique based on an original polarimetric two-scale model (PTSM), which is able to estimate the volumetric water content of bare soils from polarimetric synthetic aperture radar (SAR) data. In this paper, to extend the field of application of our retrieval technique to moderately vegetated soils, we combine the PTSM with a randomly oriented dipole-cloud volumetric scattering model, thus obtaining a polarimetric two-scale two-component model (PTSTCM). By using this model we show that, in principle, suitable combinations of the polarimetric SAR channels, i.e., “modified copolarized ratio” and “modified copolarized correlation coefficient,” are related only to the surface parameters because the dependence on the unknown volumetric contribution intensity cancels out. This allows us to retrieve soil moisture from L-band SAR data not only for bare soils but also in moderately vegetated areas, interested by a nonnegligible volumetric scattering contribution, provided that the double-bounce scattering component is negligible. In addition, describing the surface component by using the PTSM allows us to mitigate the well-known problem of overestimating the volume component, which affects most model-based target decompositions and that may lead to the so-called “negative power problem.” Both the performance and validity limits of the estimation method are assessed by comparing the obtained soil-moisture retrieval results to “in situ” measurements. To this aim, data from SMEX'03 and AGRISAR'06 campaigns available in literature are considered. They refer to sites with a flat topography. In particular, we employ the AGRISAR database, which includes data from several fields covering a period that spans all the phases of vegetation growth, to explore the validity range of the method in terms of vegetation height. Results of PTSTCM are also compared with those of available three-component methods (3CMs) employing more simplified surface scattering models. It has turned out that the use of the PTSTCM provides more accurate results for low vegetation (average modulus of soil moisture relative error for vegetation height smaller than 50 cm: 18.5% for the PTSTCM and 34% for the 3CM). Conversely, for higher vegetation, 3CMs should be more conveniently employed (average modulus of relative error for vegetation height greater than 50 cm: 17.5% for the 3CM and about 100% for the PTSTCM). A simple method to adaptively and automatically (i.e., based on measured data) select between PTSTCM and 3CM on a pixel-by-pixel basis is finally suggested, leading to a less than 20% average modulus of relative error on the retrieved soil moisture for the considered fields over the entire vegetation growth cycle. [ABSTRACT FROM PUBLISHER]

Medical image retrieval can assist physicians in finding information supporting their diagnosis and fulfilling information needs. Systems that allow searching for medical images need to provide tools for quick and easy navigation and query refinement as the time available for information search is often short. Relevance feedback is a powerful tool in information retrieval. This study evaluates relevance feedback techniques with regard to the content they use. A novel relevance feedback technique that uses both text and visual information of the results is proposed. The two information modalities from the image examples are fused either at the feature level using the Rocchio algorithm or at the query list fusion step using a common late fusion rule. Results using the ImageCLEF 2012 benchmark database for medical image retrieval show the potential of relevance feedback techniques in medical image retrieval. The mean average precision (mAP) is used as the evaluation metric and the proposed method outperforms commonly-used methods. The baseline without feedback reached 16 % whereas the relevance feedback with 20 images reached up to 26.35 % with three steps and when using 100 images up to 34.87 % in four steps. Most improvements occur in the first two steps of relevance feedback and then results start to become relatively flat. This might also be due to only using positive feedback as negative feeback often also improves results after more steps. The effect of relevance feedback in automatically spelling corrected and translated queries is investigated as well. Results without mistakes were better than spell-corrected results but the spelling correction more than double results over non-corrected retrieval. Multimodal relevance feedback has shown to be able to help visual medical information retrieval. Next steps include integrating semantics into relevance feedback techniques to benefit from the structured knowledge of ontologies and experimenting on the fusion of text and visual information. [ABSTRACT FROM AUTHOR]

Providing access to relevant biomedical literature in a clinical setting has the potential to bridge a critical gap in evidence-based medicine. Here, our goal is specifically to provide relevant articles to clinicians to improve their decision-making in diagnosing, treating, and testing patients. To this end, the TREC 2014 Clinical Decision Support Track evaluated a system's ability to retrieve relevant articles in one of three categories (Diagnosis, Treatment, Test) using an idealized form of a patient medical record . Over 100 submissions from over 25 participants were evaluated on 30 topics, resulting in over 37k relevance judgments. In this article, we provide an overview of the task, a survey of the information retrieval methods employed by the participants, an analysis of the results, and a discussion on the future directions for this challenging yet important task. [ABSTRACT FROM AUTHOR]

The majority of Internet users search for medical information online; however, many do not have an adequate medical vocabulary. Users might have difficulties finding the most authoritative and useful information because they are unfamiliar with the appropriate medical expressions describing their condition; consequently, they are unable to adequately satisfy their information need. We investigate the utility of bridging the gap between layperson and expert vocabularies; our approach adds the most appropriate expert expression to queries submitted by users, a task we call query clarification. We evaluated the impact of query clarification. Using three different synonym mappings and conducting two task-based retrieval studies, users were asked to answer medically-related questions using interleaved results from a major search engine. Our results show that the proposed system was preferred by users and helped them answer medical concerns correctly more often, with up to a 7 % increase in correct answers over an unmodified query. Finally, we introduce a supervised classifier to select the most appropriate synonym mapping for each query, which further increased the fraction of correct answers (12 %). [ABSTRACT FROM AUTHOR]

Terminology extraction is an essential task in domain knowledge acquisition, as well as for information retrieval. It is also a mandatory first step aimed at building/enriching terminologies and ontologies. As often proposed in the literature, existing terminology extraction methods feature linguistic and statistical aspects and solve some problems related (but not completely) to term extraction, e.g. noise, silence, low frequency, large-corpora, complexity of the multi-word term extraction process. In contrast, we propose a cutting edge methodology to extract and to rank biomedical terms, covering all the mentioned problems. This methodology offers several measures based on linguistic, statistical, graphic and web aspects. These measures extract and rank candidate terms with excellent precision: we demonstrate that they outperform previously reported precision results for automatic term extraction, and work with different languages (English, French, and Spanish). We also demonstrate how the use of graphs and the web to assess the significance of a term candidate, enables us to outperform precision results. We evaluated our methodology on the biomedical GENIA and LabTestsOnline corpora and compared it with previously reported measures. [ABSTRACT FROM AUTHOR]

It has been shown that accessing the patients' own electronic health records (EHR) can enhance their medical understanding and provide clinically relevant benefits. However, languages that are difficult for non-medical professionals to comprehend are prevalent in the EHR notes. The valuable and authoritative information contained in the EHR is thus less accessible to the patients, who ultimately stand to benefit the most from the information. To address this challenge, we are developing a system to retrieve EHR note-specific online consumer-oriented health education materials. We explored several query generation methods to convert long EHR notes to effective queries, including topic models and key concept identification. Our experiments show that queries using key concepts identified by a learning based model with pseudo-relevance feedback significantly outperform the baseline system of using the full text note. [ABSTRACT FROM AUTHOR]

Radiation dose monitoring has become an essential service that hospitals must perform. Depending on the system in place, this can result in the collection of large quantities of data, ripe for analysis. These data should include a wide variety of variables for each study because assessment of the propriety of the patient's dose is dependent on many factors, including patient age and size, as well as the body section that is being scanned. Moreover, the scanners themselves have many properties that affect patient dose, such as model, pitch and kVp. In this paper, we propose an engine that seamlessly integrated with a clinical PACS to retrieve radiation dosage data. We devised several schemes to analyze these data through visualization and mining techniques that examine it at different scopes. We demonstrate the utility of such visual methods at examining large, noisy, and multi-dimensional data, which is embodied in the collected radiation data. [ABSTRACT FROM AUTHOR]

This paper presents a Graph Inference retrieval model that integrates structured knowledge resources, statistical information retrieval methods and inference in a unified framework. Key components of the model are a graph-based representation of the corpus and retrieval driven by an inference mechanism achieved as a traversal over the graph. The model is proposed to tackle the semantic gap problem-the mismatch between the raw data and the way a human being interprets it. We break down the semantic gap problem into five core issues, each requiring a specific type of inference in order to be overcome. Our model and evaluation is applied to the medical domain because search within this domain is particularly challenging and, as we show, often requires inference. In addition, this domain features both structured knowledge resources as well as unstructured text. Our evaluation shows that inference can be effective, retrieving many new relevant documents that are not retrieved by state-of-the-art information retrieval models. We show that many retrieved documents were not pooled by keyword-based search methods, prompting us to perform additional relevance assessment on these new documents. A third of the newly retrieved documents judged were found to be relevant. Our analysis provides a thorough understanding of when and how to apply inference for retrieval, including a categorisation of queries according to the effect of inference. The inference mechanism promoted recall by retrieving new relevant documents not found by previous keyword-based approaches. In addition, it promoted precision by an effective reranking of documents. When inference is used, performance gains can generally be expected on hard queries. However, inference should not be applied universally: for easy, unambiguous queries and queries with few relevant documents, inference did adversely affect effectiveness. These conclusions reflect the fact that for retrieval as inference to be effective, a careful balancing act is involved. Finally, although the Graph Inference model is developed and applied to medical search, it is a general retrieval model applicable to other areas such as web search, where an emerging research trend is to utilise structured knowledge resources for more effective semantic search. [ABSTRACT FROM AUTHOR]

Information Retrieval (IR) is concerned with indexing and retrieving documents including information relevant to a user's information need. Relevance Feedback (RF) is a class of effective algorithms for improving Information Retrieval (IR) and it consists of gathering further data representing the user's information need and automatically creating a new query. In this paper, we propose a class of RF algorithms inspired by quantum detection to re-weight the query terms and to re-rank the document retrieved by an IR system. These algorithms project the query vector on a subspace spanned by the eigenvector which maximizes the distance between the distribution of quantum probability of relevance and the distribution of quantum probability of non-relevance. The experiments showed that the RF algorithms inspired by quantum detection can outperform the state-of-the-art algorithms. [ABSTRACT FROM PUBLISHER]

In the age of Web 2.0, community user contributed questions and answers provide an important alternative for knowledge acquisition through web search. Question retrieval in current community-based question answering (CQA) services do not, in general, work well for long and complex queries, such as the questions. The main reasons are the verboseness in natural language queries and the word mismatch between the queries and the candidate questions in the CQA archive during retrieval. To address these two problems, existing solutions try to refine the search queries by distinguishing the key concepts in the queries and expanding the queries with relevant content. However, using the existing query refinement approaches can only identify the key and non-key concepts, while the differences between the key concepts are overlooked. Moreover, the existing query expansion approaches, not only overlook the weights of key concepts in the queries, but also fail to consider concept level expansion for them. In this paper, we explore a key concept identification approach for query refinement and a pivot language translation based approach to explore key concept paraphrasing. We further propose a new question retrieval model which can seamlessly integrate the key concepts and their paraphrases. The experimental results demonstrate that the integrated retrieval model significantly outperforms the state-of-the-art models in question retrieval. [ABSTRACT FROM PUBLISHER]

One of the key factors for enabling sustainable construction is promoting the acquisition of knowledge about sustainable construction practices (SCPs). Information retrieval (IR) is at the heart of knowledge acquisition. However, retrieving the right information about SCPs for the right context is not an easy task; information about SCPs is complex, interdisciplinary, multifaceted, and context sensitive. To enhance the process of IR, there has been recent calls for an information systems epistemology (the theory of knowledge and knowing) to support enhanced design and use of information systems based on a better understanding of knowledge and knowing. To address this need, this paper proposes an epistemology-based semantic model for representing and reasoning-in a context-aware manner-about the knowledge of SCPs and the process of knowing (i.e., acquiring) this knowledge. The proposed semantic model of SCPs is composed of a hierarchy of concepts, interconcept relations, and axioms. The model is intended to facilitate domain-specific, context-aware retrieval of information about SCPs. The model was evaluated through answering competency questions, automated consistency and redundancy checking, expert interviews, and application-oriented evaluation. The model was implemented in an IR system and has shown to improve the retrieval results significantly. This research contributes to the body of knowledge by offering an epistemology-based semantic model for supporting the retrieval of more relevant and specific information based on multidimensional contexts. The model, and its implementation in the IR system, would allow construction professionals, stakeholders, and information users to retrieve more relevant information about SCPs, which would in turn support enhanced decision making. [ABSTRACT FROM AUTHOR]

We present a novel measure for ranking evaluation, called Twist (τ). It is a measure for informational intents, which handles both binary and graded relevance. τ stems from the observation that searching is currently a that searching is currently taken for granted and it is natural for users to assume that search engines are available and work well. As a consequence, users may assume the utility they have in finding relevant documents, which is the focus of traditional measures, as granted. On the contrary, they may feel uneasy when the system returns nonrelevant documents because they are then forced to do additional work to get the desired information, and this causes avoidable effort. The latter is the focus of τ, which evaluates the effectiveness of a system from the point of view of the effort required to the users to retrieve the desired information. We provide a formal definition of τ, a demonstration of its properties, and introduce the notion of effort/gain plots, which complement traditional utility-based measures. By means of an extensive experimental evaluation, τ is shown to grasp different aspects of system performances, to not require extensive and costly assessments, and to be a robust tool for detecting differences between systems. [ABSTRACT FROM AUTHOR]

Numerous past studies have demonstrated the effectiveness of the relevance model ( RM) for information retrieval ( IR). This approach enables relevance or pseudo-relevance feedback to be incorporated within the language modeling framework of IR. In the traditional RM, the feedback information is used to improve the estimate of the query language model. In this article, we introduce an extension of RM in the setting of relevance feedback. Our method provides an additional way to incorporate feedback via the improvement of the document language models. Specifically, we make use of the context information of known relevant and nonrelevant documents to obtain weighted counts of query terms for estimating the document language models. The context information is based on the words (unigrams or bigrams) appearing within a text window centered on query terms. Experiments on several Text REtrieval Conference ( TREC) collections show that our context-dependent relevance model can improve retrieval performance over the baseline RM. Together with previous studies within the BM25 framework, our current study demonstrates that the effectiveness of our method for using context information in IR is quite general and not limited to any specific retrieval model. [ABSTRACT FROM AUTHOR]

Normalization of citation scores using reference sets based on Web of Science subject categories ( WCs) has become an established ('best') practice in evaluative bibliometrics. For example, the Times Higher Education World University Rankings are, among other things, based on this operationalization. However, WCs were developed decades ago for the purpose of information retrieval and evolved incrementally with the database; the classification is machine-based and partially manually corrected. Using the WC 'information science & library science' and the WCs attributed to journals in the field of 'science and technology studies,' we show that WCs do not provide sufficient analytical clarity to carry bibliometric normalization in evaluation practices because of 'indexer effects.' Can the compliance with 'best practices' be replaced with an ambition to develop 'best possible practices'? New research questions can then be envisaged. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of observations of methane and its two major isotopologues, CH3D and 13CH4, from the Atmospheric Chemistry Experiment (ACE) satellite between 2004 and 2013. Additionally, atmospheric methane chemistry is modeled using the Whole Atmospheric Community Climate Model (WACCM). ACE retrievals of methane extend from 6 km for all isotopologues to 75 km for 12CH4, 35km for CH3D, and 50km for 13CH4. While total methane concentrations retrieved from ACE agree well with the model, values of δD-CH4 and δ 13C-CH4 show δ bias toward higher δ compared to the model and balloon-based measurements. Errors in spectroscopic constants used during the retrieval process are the primary source of this disagreement. Calibrating 5D and 513C from ACE using WACCM in the troposphere gives improved agreement in 5D in the stratosphere with the balloon measurements, but values of δ13C still disagree. A model analysis of methane's atmospheric sinks is also performed. [ABSTRACT FROM AUTHOR]

In this work we present an analysis of aerosol microphysical properties during a mineral dust event taking advantage of the combination of different state-of-the-art retrieval techniques applied to active and passive remote sensing measurements and the evaluation of some of those techniques using independent data acquired from in situ aircraft measurements. Data were collected in a field campaign performed during a mineral dust outbreak at the Granada, Spain, experimental site (37.16° N, 3.61° W, 680ma.s.l.) on 27 June 2011. Column-integrated properties are provided by sun- and star-photometry, which allows for a continuous evaluation of the mineral dust optical properties during both day and nighttime. Both the linear estimation and AERONET (Aerosol Robotic Network) inversion algorithms are applied for the retrieval of the column-integrated microphysical particle properties. In addition, vertically resolved microphysical properties are obtained from a multi-wavelength Raman lidar system included in EARLINET (European Aerosol Research Lidar Network), by using both LIRIC (Lidar Radiometer Inversion Code) algorithm during daytime and an algorithm applied to the Raman measurements based on the regularization technique during nighttime. LIRIC retrievals reveal the presence of dust layers between 3 and 5 kma.s.l. with volume concentrations of the coarse spheroid mode up to 60 µm³ cm-3. The combined use of the regulariza- tion and LIRIC methods reveals the night-to-day evolution of the vertical structure of the mineral dust microphysical properties and offers complementary information to that from column-integrated variables retrieved from passive remote sensing. Additionally, lidar depolarization profiles and LIRIC retrieved volume concentration are compared with aircraft in situ measurements. This study presents for the first time a comparison of the total volume concentration retrieved with LIRIC with independent in situ measurements, obtaining agreement within the estimated uncertainties for both methods and quite good agreement for the vertical distribution of the aerosol layers. Regarding the depolarization, the first published data set of the CAS-POL for polarization ratios is presented here and qualitatively compared with the lidar technique. [ABSTRACT FROM AUTHOR]