Information Retrieval Methods Research Articles

The Cabinet of Ministers of Ukraine as a subject of legal relationships of legal responsibility in the pharmaceutical sphere

The aim of work is research of legal status of central executive authorities as subjects of legal relations of legal responsibility arising in the pharmaceutical sphere of Ukraine from the standpoint of pharmaceutical law. Materials and methods. The research material is a modern regulatory and legal framework governing the activities of central executive bodies exercising regulatory influence on the activities of the participants in the pharmaceutical sector of the healthcare industry. During the work, information retrieval, analysis, systematization and generalization methods were used. Results. The Cabinet of Ministers of Ukraine is one of the participants of legal relations in the field of healthcare and the pharmaceutical sector in particular. On the other hand, the CMU is one of the main regulators of such legal relations, and has a significant impact on the general state policy in the field of health care, and on the behavior of individual participants in the pharmaceutical industry. Given this, the issue of the legal responsibility of the government, both a collegial body in general and its individual members in particular, is very important and relevant.

A learning to rank approach for cross-language information retrieval exploiting multiple translation resources

AbstractCross-language information retrieval (CLIR), finding information in one language in response to queries expressed in another language, has attracted much attention due to the explosive growth of multilingual information in the World Wide Web. One important issue in CLIR is how to apply monolingual information retrieval (IR) methods in cross-lingual environments. Recently, learning to rank (LTR) approach has been successfully employed in different IR tasks. In this paper, we use LTR for CLIR. In order to adapt monolingual LTR techniques in CLIR and pass the barrier of language difference, we map monolingual IR features to CLIR ones using translation information extracted from different translation resources. The performance of CLIR is highly dependent on the size and quality of available bilingual resources. Effective use of available resources is especially important in low-resource language pairs. In this paper, we further propose an LTR-based method for combining translation resources in CLIR. We have studied the effectiveness of the proposed approach using different translation resources. Our results also show that LTR can be used to successfully combine different translation resources to improve the CLIR performance. In the best scenario, the LTR-based combination method improves the performance of single-resource-based CLIR method by 6% in terms of Mean Average Precision.

Systematic reviews of health economic evaluations: A structured analysis of characteristics and methods applied.

The number of systematic reviews of health economic evaluations (SR-HEs) is increasing. We aimed at providing a detailed overview of the characteristics and applied methods in recently published SR-HEs. We searched MEDLINE (03/2017) for SR-HEs published since 2015 using validated search filters. We included studies that performed a systematic review of full economic evaluations and searched at least one electronic database. We extracted data in a standardized, beforehand piloted form that was deduced from the items of the PRISMA and CHEERS checklists. Data were extracted by one reviewer, and a 10% random sample was verified by a second. We prepared descriptive statistical measures to describe the SR-HEs. We included 202 SR-HEs. We identified similarities especially in the methods for information retrieval. Study selection, data extraction, and assessment of quality and transferability were frequently not reported or performed without taking measures to reduce errors (eg, independent study selection). A wide range of different tools was applied for critical appraisal. Moreover, the reporting of included economic evaluations and the synthesis of their results showed strong variations. Overall, we identified few common features in the applied methods for SR-HEs. The information retrieval processes are largely standardized, but many studies did not use validated search filters. For the other systematic review steps, the methodological approaches varied. In particular, important challenges seem to be the methodological quality and transferability assessment as well as presentation and (quantitative) synthesis of results. Efforts are needed for increasing standardization, quality of applied methods, and reporting of SR-HEs.

The WPI patent test collection

To support the objectives of the journal, to publish new research and insights covering a broad spectrum of Intellectual Property information retrieval and patent analytics related practices and methods, the editors, together with the team at IFI CLAIMS® Patent Services, have put together a patent research collection, publicly available and for free, to foster scientific good practice: comparability, reproducibility, transparency and repeatability of experiments and results. The WPI collection is for this reason static. It will not be updated with new data. Otherwise, changes in experimental results would be impossible to trace with confidence to the new methods being proposed and not seen as a result of changes in the dataset. The WPI collection complements existing test collections, which are vertical (one domain or one authority over many years). Instead, the WPI collection is horizontal: it includes all technical domains from the major patenting authorities over the relatively short time span of two years.

Addressing Formulas for Elements of 3D Sparse Matrices in 1D Arrays

3D sparse matrices play significant role in many modern information retrieval methods. A huge number of computations with such matrices are performed by these methods, like clustering, latent semantic indexing. Since many of the elements in such matrices contain zeroes, thus a need to save space arises, either in memory or disk. Thus their storage in memory or disk should be very carefully designed. To save the space in memory or disk, efficient storage of elements and their retrieval is required. This paper explains two types of 3D sparse matrices known as lower triangular and upper triangular. The basic concept behind this paper is to calculate the formulas for accessing the address of elements of 3D sparse matrices in 1D array using row major and column major.

Text Summarization and Categorization for Scientific and Health-Related Data

The increasing amount of unstructured health-related data has created a need for intelligent processing, summarizing, and categorizing these data to extract knowledge from them. My research goal in this dissertation is to develop Natural Language Processing (NLP) and Information Retrieval (IR) methods for better processing and understanding health-related textual information to promote health care and wellbeing of individuals.

Atonal Music: Can Uncertainty Lead to Pleasure?

In recent years, the field of neuroaesthetics has gained considerable attention with music being a favored object of study. The majority of studies concerning music have, however, focused on the experience of Western tonal music (TM), which is characterized by tonal hierarchical organization, a high degree of consonance, and a tendency to provide the listener with a tonic as a reference point during the listening experience. We argue that a narrow focus on Western TM may have led to a one-sided view regarding the qualities of the aesthetic experience of music since Western art music from the 20th and 21st century like atonal music (AM) – while lacking a tonal hierarchical structure, and while being highly dissonant and hard to predict – is nevertheless enjoyed by a group of avid listeners. We propose a research focus that investigates, in particular, the experience of AM as a novel and compelling way with which to enhance our understanding of both the aesthetic appreciation of music and the role of predictive models in the context of musical pleasure. We use music theoretical analysis and music information retrieval methods to demonstrate how AM presents the listener with a highly uncertain auditory environment. Specifically, an analysis of a corpus of 100 musical segments is used to illustrate how tonal classical music and AM differ quantitatively in terms of both key and pulse clarity values. We then examine person related, extrinsic and intrinsic factors, that point to potential mechanisms underlying the appreciation and pleasure derived from AM. We argue that personality traits like “openness to experience,” the framing of AM as art, and the mere exposure effect are key components of such mechanisms. We further argue that neural correlates of uncertainty estimation could represent a central mechanism for engaging with AM and that such contexts engender a comparatively weak predictive model in the listener. Finally we argue that in such uncertain contexts, correct predictions may be more subjectively rewarding than prediction errors since they signal to the individual that their predictive model is improving.

INTERNATIONAL LEGAL STANDARDS FOR CONDUCTING BIOMEDICAL EXPERIMENTS ON ANIMALS: STATUS AND PROSPECTS OF DEVELOPMENT

The article highlights the basic principles of the use of animals in biomedical research raised the main problems of vivisection. It also suggests specific steps to enhance the humanization of this process. The conclusions are the following: it is very important to prevent cruelty to animals in the context of resolving bioethical and legal problems associated with the implementation of the principle of humanism and the protection of animal rights. Modern science and the practice of preclinical and clinical tests should be involved into serious challenge in order to improve and universalize the process of medical and biological research technology in vitro. The purpose of the article is to study the main international legal principles of using animals in biomedical experiments, as well as to determine the main problems on this issue and to outline the ways to resolve them. The research material is a modern international regulatory framework that establishes the basic principles for the animals treatment. The methods of information retrieval, analysis, systematization, and generalization were used in this article. As it is well-known, human treatment of people begins with a human attitude towards animals. Nowadays the international community has adopted enough legal acts regulating the proper behavior with animals, including those specially cultivated for such studies (laboratory) when conducting biomedical experiments. Recently, both, in the national legislation of European countries and international legislation, there have been trends in the protection of both the general rights of animals as well as intensification of their protection in the field of biomedical research. It is imperative, both at the international and national levels, to adopt programs for development and support of scientific study of the in vitro biosystems introduction into the practice of toxicological studies. At the national level, it is necessary to criminalize the violation of international standards for conducting biomedical research using animals, strengthen other types of responsibility, and create a special system of state bodies responsible for the policy of using animals for biomedical purposes and exercising control in this area. Both, international and national legislation should impose a direct ban on the use of in vivo technologies in case of the possibility of using in vitro technologies.

OP26 Search Approaches In Information Retrieval Presented In HTAi SuRe Info

IntroductionIt is a challenge to stay up-to-date with the latest developments in information retrieval for health technology assessment (HTA). Summarized Research in Information Retrieval for HTA (SuRe Info) is a well-established open-access website with a selection of up-to-date key papers presented in summarized overviews. SuRe Info is maintained by the HTAi Interest Sub-Group on Information Resources; its main target group are information specialists. SuRe Info is updated twice a year by experienced information specialists. Publications on information retrieval methods are identified by running topic-specific search strategies in relevant databases. Publications fulfilling the SuRe Info inclusion criteria receive a structured abstract together with a brief critical appraisal prepared by one SuRe Info information specialist and checked by another. The key messages from the appraisals are summarized in topic-specific chapters.MethodsThe structure and work of SuRe Info is presented on the basis of the chapter “Value of using different search approaches”.ResultsThe chapter “Value of using different search approaches” was last updated in September 2018. It examines various alternative search approaches in bibliographic databases beyond the conventional Boolean search. According to this chapter, the most well-known approach, offering the most evidence, is citation searching (direct or indirect citation relationships). In contrast, little research has so far been conducted on other approaches such as full-text searches, automated retrieval methods or hand searching.ConclusionsSuRe Info is an important resource for information specialists to keep up-to-date with the literature on information retrieval. In particular when information specialists work alone and not within a larger team, it is necessary to rely on collaboration projects such as SuRe Info.

The Use of Ontology in Retrieval: A Study on Textual, Multilingual, and Multimedia Retrieval

Web contains a vast amount of data, which are accumulated, studied, and utilized by a huge number of users on a daily basis. A substantial amount of data on the Web is available in an unstructured format, such as Web pages, books, journals, and files. Acquiring appropriate information from such humongous data has become quite challenging and a time-consuming task. Trivial keyword-based information retrieval systems highly depend on the statistics of data, thus facing word mismatch problem due to inevitable semantic and context variations of a certain word. Therefore, this marks the desperate need to organize such massive data into a structured format so that information can be easily processed in a large context by taking data semantics into account. Ontologies are not only being extensively employed in the semantic Web to store unstructured information in an organized and structured way but it has also raised the performance of diverse information retrieval approaches to a great extent. Ontological information retrieval systems retrieve data based on the similarity of semantics between the user query and the indexed data. This paper reviews modern ontology-based information retrieval methods for textual, multimedia, and cross-lingual data types. Furthermore, we compare and categorize the most recent approaches used in the above-mentioned information retrieval methods along with their major drawbacks and advantages.

Search Personalization Based on Social-Network-Based Interestedness Measures

Personalized search methods try to tailor the search results to the user's needs and preferences. A popular source of information for personalization is social networks of users. In this paper, we aim at proposing a social-network-based personalized information retrieval (PIR) method that: i) is holistic (not just considering some local neighborhood of the searcher); ii) is efficient in term of computational/storage cost; iii) its personalization component has a probabilistic basis. To the best of our knowledge, no such method exists. We propose an interestedness measure and several variations of it for quantifying the interestedness of each user in another user based on a social network of users. The general idea is to try to mimic a searcher's behavior in the real world to estimate the interestedness. The measures are then exploited to personalize the retrieval results. We evaluate the resulting PIR methods and compare them in terms of retrieval effectiveness and computational/storage cost. We also compare them with some baseline methods. In summary, our analyses suggest that retrieval based on at least one of the measures performs well in terms of both retrieval effectiveness and computational/storage cost.

Retrieving knowledgeable information from cloud using trapezoid-based multi-keyword query ranking with weighted distributed function

Collection of information is available in cloud and growing at an exponential rate and this scope makes the organisation and accessing of data critical for efficient use of knowledgeable information. In order to obtain the desired outcome, users heavily rely upon the cloud information retrieval methods. Many research works have been conducted in the field of knowledge related information retrieval. However, effective ranking of complex query pose significant challenges. In this paper, to efficiently rank the query on cloud services a method called, weighted fuzzy multi-keyword rank query (WFM-KRQ) is introduced. This method is based on associating a query with a fuzzy rule, applying a weighted distribution function and a pruning technique based on a threshold value. The proposed encrypted RepeatKeyRotate algorithm effectively improves the performance rate of cloud data sharing services by reducing the processing time and improving the retrieval rate on handling complex queries when compared to the state-of-the-art works.

Research on massive information query and intelligent analysis method in a complex large-scale system.

With the rapid growth of big data and network information, it is particularly important to perform information query and intelligent analysis on unstructured massive data in large-scale complex systems. The existing methods of directly collating, sorting, summarizing, and storing retrieval of documents cannot meet the needs of information management and rapid retrieval of massive data. This paper takes the standardized storage, effective extraction and standardized database construction of massive resume information in social large-scale complex systems as an example, and proposes a massive information query and intelligent analysis method. The method utilizes the semi-structured features of the resume document, constructs the extraction rule model of various resume data to extract the massive resume information. On the basis of HBase distributed storage, with the help of parallel computing technology to optimize the storage and query efficiency, which ensures the intelligent analysis and retrieval of massive resume information. The experimental results show that this method not only greatly improves the extraction accuracy and recall rate of resume information data, but also compared with the traditional methods, there are obvious improvements in the three aspects of massive information retrieval methods, query usage efficiency, and the intelligent analysis of complex systems.

OP23 Smart Searches For Context-Sensitive Topics: Geographic Search Filters

IntroductionSome systematic review topics are context-sensitive and informed by evidence about a specific geographic setting. Smart information retrieval methods are required to identify such evidence in an efficient manner. This presentation will discuss how validated geographic search filters enable smart literature searching for context-sensitive reviews using the National Institute for Health and Care Excellence (NICE) United Kingdom (UK) filters for MEDLINE and Embase (OVID) as examples. The NICE UK filters were developed in 2016. The filters demonstrated high recall and high precision, however, further research was required to confirm these results.MethodsIn 2018, the filters’ recall of references from 100 UK-based multidisciplinary reviews was calculated. Reproducible search strategies were identified from twenty-six of the 100 reviews in MEDLINE and from nine reviews in Embase. From this, the precision and number-needed-to-read (NNR) were calculated.ResultsThe MEDLINE filter achieved 96 percent recall (1401 out of 1454 UK references), 2.1 percent precision and a NNR of forty-seven. The Embase filter achieved 97 percent recall (1520 out of 1560 UK references), 0.7 percent precision and a NNR of 146. Compared to not using a filter, the MEDLINE and Embase filters reduced the number of search results by an average of 87 percent and 80 percent, respectively.ConclusionsThe filters retrieve the majority of evidence for UK topics while reducing search result volumes and so enable smart literature searching for context-sensitive topics. Large literature search result volumes can increase development time-frames for systematic reviews. Using the filters can therefore save time for reviews with a UK focus. There are currently two other validated geographic search filters for Africa and Spain. It is hoped that the NICE UK filters’ successful retrieval performance will encourage the development of validated search filters for more geographic regions.

Adverse drug reactions of anticancer drugs derived from natural sources

Cancer, a life threatening disease adversely affects huge population worldwide. Naturally derived drug discovery has emerged as a potential pathway in search of anticancers. Natural products-based drugs are generally considered safe, compared to their synthetic counterparts. A systematic review on adverse drugs reactions (ADRs) of the anticancer natural products has not been performed till date. We reviewed anticancer drugs, derived from plants, microbes and marine sources with their mechanistic action and reported ADRs. PubMed, ScienceDirect and Scopus were searched through Boolean information retrieval method using keywords “natural products”, “cancer”, “herbal”, “marine drugs” and “adverse drug reaction”. We documented ADRs of natural products based anticancer agents, mechanisms of action and chemical structures. It was observed that majority of the natural products based anticancer drugs possess ample adverse effects, dominantly hematological toxicities, alopecia, neurotoxicity and cardiotoxicity. These findings deviate from the preconceived notion about safer nature of herbal drugs. We also came across some anti-cancer natural products with less/no reported adverse events like Cabazitaxel and Arglabin. Comprehensive pharmacovigilance studies are needed to report ADRs and thereby predicting safety of anti-cancer drugs, either originated from natural sources or chemically synthesized.

Experimenting with information retrieval methods in the recovery of feature-code SPL traces

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. This work evaluates the methods’ retrieval quality targeting the extraction of feature-code trace links in a set of variant-rich software development projects. 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. 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. 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.

Subjects and basis of legal responsibility in the pharmaceutical sphere of Ukraine

The purpose of the work is to determine the subjects of legal relations arising in the application of measures of legal liability for violation of pharmaceutical law and the grounds for its application. Materials and methods. The research material is a modern regulatory and legal framework of the pharmaceutical sector in the healthcare industry, as well as judicial practice in cases of bringing to justice the violators of pharmaceutical legislation. During the work, information retrieval, analysis, systematization and generalization methods were used. Results . On the basis of the analysis, a group of subjects of legal responsibility in the pharmaceutical field was suggested for further scientific discussion and discussion. Conclusions. The legal liability of subjects of pharmaceutical legal relations is an extremely important factor in preventing attacks on the legal rights of citizens to provide high-quality, safe and affordable drugs and medical products. The main groups of subjects of legal responsibility in the pharmaceutical sphere of offenses include pharmaceutical workers; pharmacies; business entities operating in the pharmaceutical and related fields; officials of the State Service of Ukraine on Drugs and Drug Control and other state regulatory and law enforcement agencies.

Innovation in information retrieval methods for evidence synthesis studies.

Innovation in information retrieval methods for evidence synthesis studies.

Integrating heterogeneous sources for predicting question temporal anchors across Yahoo! Answers

Modern Community Question Answering (CQA) web forums provide the possibility to browse their archives using question-like search queries as in Information Retrieval (IR) systems. Although these traditional IR methods have become very successful at fetching semantically related questions, they typically leave unconsidered their temporal relations. That is to say, a group of questions may be asked more often during specific recurring time lines despite being semantically unrelated. In fact, predicting temporal aspects would not only assist these platforms in widening the semantic diversity of their search results, but also in re-stating questions that need to refresh their answers and in producing more dynamic, especially temporally-anchored, displays.In this paper, we devised a new set of time-frame specific categories for CQA questions, which is obtained by fusing two distinct earlier taxonomies (i.e., [29] and [50]). These new categories are then utilized in a large crowd-sourcing based human annotation effort. Accordingly, we present a systematical analysis of its results in terms of complexity and degree of difficulty as it relates to the different question topics11The new annotated corpus will be made publicly available upon acceptance under https://doi.org/10.13140/RG.2.2.23360.38407.Incidentally, through a large number of experiments, we investigate the effectiveness of a wider variety of linguistic features compared to what has been done in previous works. We additionally mix evidence/features distilled directly and indirectly from questions by capitalizing on their related web search results. We finally investigate the impact and effectiveness of multi-view learning to boost a large variety of multi-class supervised learners by optimizing a latent layer build on top of two views: one composed of features harvested from questions, and the other from CQA meta data and evidence extracted from web resources (i.e., snippets and Internet archives).

DTexSL: A dynamic disaster textual storyline generating framework

Effectively capturing the status information and improving situational awareness is the most important task in disaster information management. Due to the rapid increase of online information, this task becomes very challenging. Existing information retrieval and text summarization methods can solve information overload problem to some extent, however, they suffer from some limitations: lacking theme structure, ignoring spatial information, and unable to update information on the real time events. In this paper, we propose a dynamic disaster storyline generation framework, which generates a global storyline describing the evolution of the disaster events in the high-level layer and provides condensed information about specific regions affected by the disaster in the local-level layer. The proposed framework considers both uniqueness and relevance for representative document selection, uses Maximal Marginal Relevance to generate summaries from each local document set, and utilizes dynamic Steiner tree to implement the information update. Comprehensive experiments on typhoons data sets demonstrate the effectiveness of the proposed methods in each level and the overall framework.