Semantics and result disambiguation for keyword search on tree data

Keyword search is a popular technique for searching tree-structured data (e.g., XML, JSON) on the web because it frees the user from learning a complex query language and the structure of the data sources. However, the convenience of keyword search comes with drawbacks. The imprecision of the keyword queries usually results in a very large number of results of which only very few are relevant to the query. Multiple previous approaches have tried to address this problem. Some of them exploit structural and semantic properties of the tree data in order to filter out irrelevant results while others use a scoring function to rank the candidate results. These are not easy tasks though and in both cases, relevant results might be missed and the users might spend a significant amount of time searching for their intended result in a plethora of candidates. Another drawback of keyword search on tree data, also due to the incapacity of keyword queries to precisely express the user intent, is that the query answer may contain different types of meaningful results even though the user is interested in only some of them. Both problems of keyword search on tree data are addressed in this dissertation. First, an original approach for answering keyword queries is proposed. This approach extracts structural patterns of the query matches and reasons with them in order to return meaningful results ranked with respect to their relevance to the query. The proposed semantics performs comparisons between patterns of results by using different types of ho-momorphisms between the patterns. These comparisons are used to organize the patterns into a graph of patterns which is leveraged to determine ranking and filtering semantics. The experimental results show that the approach produces query results of higher quality compared to the previous ones. To address the second problem, an original approach for clustering the keyword search results on tree data is introduced. The clustered output allows the user to focus on a subset of the results, and to save time and effort while looking for the relevant results. The approach performs clustering at different levels of granularity to group similar results together effectively. The similarity of the results and result clusters is decided using relations on structural patterns of the results defined based on homomor-phisms between path patterns. An originality of the clustering approach is that the clusters are ranked at different levels of granularity to quickly guide the user to the relevant result patterns. An efficient stack-based algorithm is presented for generating result patterns and constructing the clustering hierarchy. The extensive experimentation with multiple real datasets show that the algorithm is fast and scalable. It also shows that the clustering methodology allows the users to effectively retrieve their intended results, and outperforms a recent state-of-the-art clustering approach. In order to tackle the second problem from a different aspect, diversifying the results of keyword search is addressed. Diversification aims to provide the users with a ranked list of results which balances the relevance and redundancy of the results. Measures for quantifying the relevance and dissimilarity of result patterns are presented and a heuristic for generating a diverse set of results using these metrics is introduced

Novelty detection in topic tracking

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2010.Thesis (Master's) -- Bilkent University, 2010.Includes bibliographical references leaves 51-56.News portals provide many services to the news consumers such as information retrieval, personalized information filtering, summarization and news clustering. Additionally, many news portals using multiple sources enable their users to evaluate developments from different perspectives by richening the content. However, increasing number of sources and incoming news makes it difficult for news consumers to find news of their interest in news portals. Different types of organizational operations are applied to ease browsing over the news for this reason. New event detection and tracking (NEDT) is one of these operations which aims to organize news with respect to the events that they report. NEDT may not also be enough by itself to satisfy the news consumers’ needs because of the repetitions of information that may occur in the tracking news of a topic due to usage of multiple sources. In this thesis, we investigate usage of novelty detection (ND) in tracking news of a topic. For this aim, we built a Turkish ND experimental collection, BilNov, consisting of 59 topics with an average of 51 tracking news. We propose usage of three methods; cosine similarity-based ND method, language model-based ND method and cover coefficient-based ND method. Additionally, we experiment on category-based threshold learning which has not been worked on previously in ND literature. We also provide some experimental pointers for ND in Turkish such as restriction of document vector lengths and smoothing methods. Finally, we experiment on TREC Novelty Track 2004 dataset. Experiments conducted by using BilNov show that language model-based ND method outperforms other two methods significantly and category-based threshold learning has promising results when compared to general threshold learning.Aksoy, CemM.S

Different epidemiology of bloodstream infections in COVID-19 compared to non-COVID-19 critically ill patients: A descriptive analysis of the Eurobact II study

Background: The study aimed to describe the epidemiology and outcomes of hospital-acquired bloodstream infections (HABSIs) between COVID-19 and non-COVID-19 critically ill patients. Methods: We used data from the Eurobact II study, a prospective observational multicontinental cohort study on HABSI treated in ICU. For the current analysis, we selected centers that included both COVID-19 and non-COVID-19 critically ill patients. We performed descriptive statistics between COVID-19 and non-COVID-19 in terms of patients’ characteristics, source of infection and microorganism distribution. We studied the association between COVID-19 status and mortality using multivariable fragility Cox models. Results: A total of 53 centers from 19 countries over the 5 continents were eligible. Overall, 829 patients (median age 65 years [IQR 55; 74]; male, n = 538 [64.9%]) were treated for a HABSI. Included patients comprised 252 (30.4%) COVID-19 and 577 (69.6%) non-COVID-19 patients. The time interval between hospital admission and HABSI was similar between both groups. Respiratory sources (40.1 vs. 26.0%, p < 0.0001) and primary HABSI (25.4% vs. 17.2%, p = 0.006) were more frequent in COVID-19 patients. COVID-19 patients had more often enterococcal (20.5% vs. 9%) and Acinetobacter spp. (18.8% vs. 13.6%) HABSIs. Bacteremic COVID-19 patients had an increased mortality hazard ratio (HR) versus non-COVID-19 patients (HR 1.91, 95% CI 1.49–2.45). Conclusions: We showed that the epidemiology of HABSI differed between COVID-19 and non-COVID-19 patients. Enterococcal HABSI predominated in COVID-19 patients. COVID-19 patients with HABSI had elevated risk of mortality. Trial registration ClinicalTrials.org number NCT03937245. Registered 3 May 2019

VASCULAR COMPLICATIONS OF ENDOVASCULAR AORTIC ANEURISM REPAIR: SHORT AND LONG TERM FOLLOW-UP

Objective: An intervention is recommended in patients, who present with an infrarenal abdominal aortic aneurysm (AAA) of more than 5.5 cm in diameter. Endovascular repair, which has been carried out more than 20 years provides reduced perioperative mortality rates and shorter intensive care and hospital stay, and less bleeding. However, it is not without complications in the short and long term. Vascular complications constitute a major part of them and may contribute to the mortality and mobidity rates. This study aims to evaluate the incidence of vascular complications of Endovascular Aortic Aneurysm Repair (EVAR) in short and long term

Semilobar holoprosencephaly with associated cyclopia and radial aplasia: first trimester diagnosis by means of integrating 2D-3D ultrasound

Holoprosencephaly (HPE) is commonly associated with facial malformations. We present a case of semilobar HPE associated with distal limb defect which was detected at 12 weeks of gestation