Survey on Information Retrieval Accuracy in Biomedical Systems

The Web has become a large repository of documents (or pages) written in many different languages. In this context, traditional information retrieval (IR) techniques cannot be used whenever the user query and the documents being retrieved are in different languages. To address this problem, new cross‐language information retrieval (CLIR) techniques have been proposed. In this work, we describe a method for cross‐language retrieval of medical information. This method combines query terms and related medical concepts obtained automatically through a categorization procedure. The medical concepts are used to create a linguistic abstraction that allows retrieval of information in a language‐independent way, minimizing linguistic problems such as polysemy. To evaluate our method, we carried out experiments using the OHSUMED test collection, whose documents are written in English, with queries expressed in Portuguese, Spanish, and French. The results indicate that our cross‐language retrieval method is as effective as a standard vector space model algorithm operating on queries and documents in the same language. Further, our results are better than previous results in the literature.

In recent years, information retrieval technology is widely used in the medical industry. How to effectively retrieve electronic medical record has become a hot issue in the field of information retrieval. Medical terms occupy an important position in the electronic medical record (EMR) retrieval, and they are usually used to limit the retrieval conditions, so they suggest the user’s search intention. Aiming at the importance of medical terms, a method of query reformulation based on medical term reweighting is proposed to improve the performance of EMR retrieval. Firstly, the method filtrates medical terms from the origin query. Then, each medical term is weighted by its own self-information which can be estimated from the document set. Finally, the new query is constructed by combining the weighted medical terms and origin query proportionally. Experimental results on the TREC dataset show that our method performed above the baseline in three performance metrics: MAP (+ 14.2%), bpref (+ 8.0%), and P@10 (+ 9.6%).

Medical Information Retrieval (IR) aims to extract relevant medical information from the web, patients' records, electronic books, research articles etc. However, users are generally unfamiliar with medical terms and find difficulties expressing their needs. One interesting solution is to integrate fuzzy ontologies in order to achieve semantic interoperability and offer a way to handle vague and imprecise information regarding the medical field. In this paper, our aim is to perform an effective IR by extending its understanding of ambiguous medical concepts. We propose a fuzzy ontology-based medical IR approach which is composed of three components: (1) personalized medical ontology building, (2) contextualized fuzzification of the personalized medical ontology and (3) a query reformulation process based on the resulting fuzzy ontology. A prototype has been implemented allowing experimental evaluation of the proposal. We have studied how the query reformulation has led to a quality results improvement.

A semantic framework for enhancing pseudo-relevance feedback with soft negative sampling and contrastive learning

The field of information retrieval has witnessed tangible progress over the past decades in response to the expanded usage of the internet and the dire need of users to search for massive amounts of digital information. Given the steady increase of Arabic e-content, excellent information retrieval systems must be devised to suit the nature and requirements of the Arabic language. This paper sheds light on the current progress in the field of Arabic information retrieval, identifies the challenges that hinder the progress of this science, and proposes suggestions for further research. This paper uses the descriptive analytical method to examine the reality of Arabic studies in the field of information retrieval and to study the problems that are being faced in this area. Specifically, the previous literature on information retrieval is reviewed by searching the related databases and websites.

The Web enables to have access to silo-ed information describing news articles, often offering a multitude of viewpoints that, once combined, can provide a broader picture of the story being reported on the news. In this paper, we propose an approach that automatically extracts representative features of a news item, namely named entities, from textual content attached to a video item (subtitles) and from a set of documents from the Web collected using entity expansion techniques. Approaches relying on entity expansion generally try to collect and process the important facts behinds a particular news item, but they are often too dependent on frequency-based functions and information retrieval techniques thus neglecting the multi-dimensional relationships that are established among the entities. We propose a concentric-based approach that enables to represent the context of a news item, by harmonizing into a single model the representative entities, which can be extracted using information retrieval and natural language processing techniques (Core), and other entities that get prominent according to different dimensions such as informativeness, semantic connectivity, or popularity (Crust). We compare our approach with a baseline by analyzing the compactness of the generated summary on an existing gold standard available on the Web. Results of the experiments show that our approach converges faster to the ideal compact news snapshot with an improvement of 36.9% over the baseline.

The need for a convergence between semi-structured data management and Information Retrieval techniques is manifest to the scientific community. In order to fulfil this growing request, W3C has recently proposed XQuery Full Text, an IR-oriented extension of XQuery. However, the issue of query optimization requires the study of important properties like query equivalence and containment; to this aim, a formal representation of document and queries is needed. The goal of this thesis is to establish such formal background. We define a data model for XML documents and propose an algebra able to represent most of XQuery Full-Text expressions. We show how an XQuery Full-Text expression can be translated into an algebraic expression and how an algebraic expression can be optimized.

Companies often develop and maintain a collection of product variants that share some common features but also support different, customer-specific features. To reengineering such legacy product variants for systematic reuse, one must identify features and their implementing code units (e.g. functions, files) in different product variants. Information retrieval (IR) techniques may be applied for that purpose. In this paper, we discuss problems that hinder direct application of IR techniques to a collection of product variants. To counter these problems, we present an approach to support effective feature location in product variants. The novelty of our approach is that we exploit commonalities and differences of product variants by software differencing and FCA techniques so that IR technique can achieve satisfactory results for feature location in product variants. We have implemented our approach and conducted evaluation with a collection of nine Linux kernel product variants. Our evaluation shows that our approach always significantly outperforms a direct application of IR technique in the subject product variants.

The document-length normalization problem has been widely studied in the field of information retrieval. The cosine normalization (Baeza-Yates and Ribeiro-Neto, 1999), the maximum if normalization (Allan et al., 1997) and the byte length normalization (Robertson et al., 1992) are the most commonly used normalization techniques. In (Singhal et al., 1996), authors studied the retrieval probability of documents w.r.t. their size, using different similarity measures. They have shown that none of existing measures retrieve the documents of different lengths with the same probability. We first show here that the document and query sizes are indeed very influent on the similarity score expectation. Therefore, we propose to realize a statistical regression of the similarity scores distribution w. r. t. document and query sizes in order to normalize them. Experimental results appear to indicate that our approach, as well in the field of classical Information Retrieval as when applied to a document clustering process, allows to judge similarities really more fairly.

This chapter provides a perspective on a problem-based learning (PBL) approach to teaching information retrieval (IR) theory, techniques, and practice at under- and postgraduate levels. The educational philosophy of PBL is outlined, and its pertinence to addressing the challenges of teaching IR in terms of both the diverse nature of the subject and also of the students is discussed. It is argued that teaching IR as a solution to a problem (access to information) and as a problem (how can we organize information in IR systems so it can be accessed) provides an engaging and challenging way to teach it to a range of students. It also draws connections between IR theory and techniques and relates IR to other disciplines through showing how similar problems are faced in other subjects. Some specific examples of teaching exercises based on PBL are provided with a discussion and reflection on their effectiveness. Finally assessment and feedback from students are discussed.

This article discusses the opportunities and challenges of applying modern information retrieval techniques to the cultural heritage domain. Although the field of information retrieval is closely associated with computer science, it originally emerged from library science — also one of the main disciplines concerned with access to cultural heritage material. Hence we are, in a sense, exploring what happens if we bring these strands of research back together again. The article consists of three parts. In the first part, we explain the field of information retrieval and its multidisciplinary nature. In the second part, we discuss how and why the problem of providing access to cultural heritage can be cast naturally as an information retrieval problem. In the third and main part, we present a detailed case study of applying the modern information retrieval approach in practice within a museum.

Being such a vast resource of information, World Wide Web has become irreplaceable but the outburst of information available over the internet has made web search a time consuming and a very complex process. Now days if someone need to retrieve any information using internet they come across huge number of web pages because of large amount of data on web. It becomes difficult task to find information so in order to retrieve meaningful and intelligent information we have many information retrieval (IR) techniques. To gather the significant information from such a vast available resource Information Retrieval (IR); a method of retrieving such information resources which are relevant to an information need is applied. This paper would review few of these methods for intelligent information retrieval systems on web based on the Ontology information retrieval techniques.

Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports

Research articles in biomedicine domain have increased exponentially, which makes it more and more difficult for biologists to manually capture all the information they need. Information retrieval technologies can help to obtain the users' needed information automatically. However, it is a great challenge to apply these technologies to biomedicine domain directly because of some domain specific characteristics, such as the abundance of terminologies. To enhance the effectiveness of the biomedical information retrieval, we propose a novel framework based on the state-of-the-art information retrieval methods, called learning to rank, which has been proved effective to rank documents based on their relevance degree. In the framework, we attempt to tackle the problem of the abundance of terminologies by constructing ranking models, which focus on not only retrieving the most relevant documents but also diversifying the searching results to increase the completeness of the resulting list for a given query. In the model training, we propose two novel document labeling strategies, and combine several traditional retrieval models as learning features. Besides, we also investigate the usefulness of different learning to rank approaches in our framework. Experimental results on TREC Genomics datasets demonstrate our proposed framework is effective in improving the performance of biomedical information retrieval.

SAGE Open Medical Case Reports is an open access journal that publishes short case reports providing key insights into real medical cases that are essential for physicians, and that ultimately help to improve patient outcomes. It publishes online only and does not limit content due to page budgets or thematic significance.