Crowdsourcing for information retrieval: introduction to the special issue

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 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.

It is our great pleasure to welcome you to New Brunswick, New Jersey and the third ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR 2018). CHIIR, as you may know, has roots in two previously successful communities: The Information Interaction in Context (IIiX) conference and the Human Computer Information Retrieval (HCIR) symposium, which ran from 2006 and 2007 respectively. These meetings were focused on issues that involved Human Information Behavior (HIB), Interactive Information Retrieval (IIR) and Human-Computer Interaction (HCI). Those roots, as well as decades of research and development on information seeking and other forms of information interaction, human-centered design, and information retrieval are the backbone of CHIIR. At the same time, this vibrant new community has picked up more scholars and themes as it continues to attract new students and professionals, as well as emerging research topics that are evident in this year's program. In fact, CHIIR is one of the few forums that explicitly supports presentation of bold new ideas through its Perspective Papers category. In addition, we have picked up Demos as a new category for presentations this year. All of these wonderful things could not be possible without a large number of people making significant contributions. We are quite fortunate to have Katriina Byström, Falk Scholer, and Jeff Huang as our Program Chairs this year. These three brave souls took upon a tremendous effort of working with dozens of submissions, reviewers, and meta-reviewers and constructing a high-quality program while coordinating from three different continents. Soo Young Rieh and Preben Hansen, on the other hand dealt with higher than expected load for short papers and still managed to get them reviewed and assessed on time. Even our Doctoral Consortium (DC) this year ended up being quite challenging as we received twice as many submissions as before, but our fine DC chairs Jaceck Gwizdka and Vivek Singh found world-class mentors for these doctoral students. Thanks to our Workshops and Tutorials Chair Xiaojun Yuan, we have a fantastic lineup of tutorials on the first day and workshops on the last day. When the whole program was ready for the conference, it was our Proceedings Chair Roberto Gonzalez-Ibanez who managed to put together these proceedings that you are holding in your hands, or more likely viewing in the ACM Digital Library! We are thankful for having an energetic and resourceful local arrangement committee with Michael Cole as the Treasurer, Fernando Diaz as the Sponsorship Chair, Kaitlin Costello as the Local Organizing Chair, and Sunyoung Kim and Matt Mitsui as the Student Volunteers Chairs. And it was our Publicity Chair Anita Crescenzi, who helped us with not only the outreach efforts, but also with the registration system. Finally, none of these would have happened without the authors and reviewers from more than 30 countries whose contributions are reflected in every aspect of this conference. So, thank you all!

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.

Graph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of natural language processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information processing tasks. Readers will come away with a firm understanding of the major methods and applications in natural language processing and information retrieval that rely on graph-based representations and algorithms.

Document analysis is one of the emerging area of research in the field of Information Retrieval. Many attempts have been made for retrieving information from a document using various machine learning algorithms. A concept of context vector is frequently used in information retrieval from document/s. Context Vector is an vector, which is used for various feature selection from documents, automatic classification of text documents, Subject Verb Agreement, etc. This paper discusses, the attempts made in the field of Information Retrieval (IR) from document using context vector. It also discuss about pros and cons of each attempt. This paper propose a system which can give “context vector” of the document set using Latent Semantic Analysis which is the most trending method in document analysis. The system is tested on BBC news dataset and proves to be successful.

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%).

Retrieval of relevant documents from a collection is a tedious task. As genetic algorithms (GA) are robust and efficient search and optimization techniques, they can be used to search the huge document search space. In this paper, a general frame work of information retrieval system is discussed. The applicability of genetic algorithms in the field of information retrieval is also discussed. A review on how GA is applied to different problem domains in information retrieval is presented. A study on the similarity measures in information retrieval is also discussed.

Research on information retrieval model under scarcity theory and user cognition

This paper presents a survey of evolutionary approach for Clustering and how Genetic Algorithm is applied to different problem domains in the field of Information Retrieval. The paper is original in two main aspects first it gives an overview of evolutionary approach for clustering and information retrieval and second it suggests a novel evolutionary approach which serves both the areas of Clustering and Information Retrieval by developing a single Genetic Algorithm. Conventional clustering algorithms are sensitive to the choice of cluster center. Such a partition is insufficient to represent many real situations. To improve the performance of clustering algorithm, Genetic Algorithms is applied to clustering algorithms. Genetic Algorithms are robust in searching a multidimensional space to find optimal solution. This paper tries to reflect the research profile of this area by focusing more on how Evolutionary Algorithms can effectively work in forming clusters and improving retrieval performance.

The document retrieval is one of the fast growing and complex research area in the field of information retrieval. Concept-based information retrieval is more beneficial than keyword-based access. The most important advantage is the ability to make use of semantic relationships among concepts in finding relevant documents. Removal of irrelevant documents by identifying conceptual mismatches is another benefit. The linguistic representation of concepts is words and phrases. These concepts are mental structures. The inherent conciseness of natural language can make words represent multiple concepts. Different words can represent the same or similar concepts. We present a novel method of concept mining methodology, which provides an efficient way of document retrieval. The performance of this proposed retrieval system was analyzed by the existing methods for different medical documents. The proposed retrieval system shows that the documents are retrieved effectively based on concepts and the system performs with higher accuracy value.

Since its inception in the late 1950s, the field of Information Retrieval (IR) has developed tools that help people find, organize, and analyze information. The key early influences on the field are well-known. Among them are H. P. Luhn's pioneering work, the development of the vector space retrieval model by Salton and his students, Cleverdon's development of the Cranfield experimental methodology, Spärck Jones' development of idf , and a series of probabilistic retrieval models by Robertson and Croft. Until the development of the WorldWideWeb (Web), IR was of greatest interest to professional information analysts such as librarians, intelligence analysts, the legal community, and the pharmaceutical industry.

NLP technology becomes dominant in the field of Information Retrieval (IR) because it facilitates the retrieval of relevant information from different resources in particular natural language. Traditional IR systems treat each document as a bag-of-words and do not exploit any language related information. It is obvious that considering such information can improve the performance of various IR tasks. One of the crucial prerequisites to deal with these tasks is the knowledge about the role of the various word classes, also known as part-of-speech (POS). For that purpose, the research presented in this paper has used word classes as additional information about document and query terms and have evaluated the impact of such information in the process of passage retrieval from documents written in Macedonian language. As a supplement, the effect of word forms has also been analyzed. The findings indicate that word class information is only important if it is combined with the term frequency and inverse document frequency. Including word forms further improves system's accuracy.

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

The publication of this special issue, “Linking metagenomics to aquatic microbial ecology,” in Limnology and Oceanography, is long overdue. But perhaps it could not have appeared in L&O any earlier. The term “metagenomics” was invented in the late 1990s to describe the retrieval of genetic information from microorganisms without preselection by PCR, which gets only the targeted gene, or by isolating and culturing the organisms in the lab, which gets only a few microbes. Before metagenomics, microbial ecologists were well on their way to identifying microorganisms by using approaches that focused on the genes for ribosomal RNA. But with few exceptions, the rRNA gene data did not say much about what the organisms do—their role in ecological and biogeochemical processes. Metagenomic data tell us about their potential roles, and the other “omic” approaches (metatranscriptomics, metaproteomics, and so on) get closer to their ongoing activity. Metagenomics and its relatives took over microbial ecology after 2000, as documented by Pep Gasol's analysis of publications in the field since 1980 (Microbial Ecology of the Oceans, third edition 2018). Pep found that metagenomics started to have an impact, as measured by citation numbers, at least 10 years ago. Until this special issue, the metagenomic wave has left L&O largely untouched. According to the Web of Science, out of the nearly 10,000 papers published in the journal since its beginning, only seven report metagenomic results. An additional two papers have metatranscriptomic data, while no L&O paper has metaproteomic or other omic data from organisms. By contrast, since its start in 2007, the ISME Journal has published 432 metagenomic papers. All seven of the L&O metagenomic papers appeared after 2009, which so happens is when I published a primer about metagenomics and other omics in the L&O Bulletin at the invitation of then editor, John Dolan. (In case you are wondering, the L&O Bulletin article is why Deputy Editor-in-Chief Maggie Xenopoulos thought of me for this piece.) My article did not exactly launch an avalanche of papers on the topic. While this special issue appears late in metagenomic history, arguably it is appearing in L&O at just about the right time. The field has evolved to the point where it can now more forcefully explore questions of interest to L&O readers, as discussed in the issue's introduction by Grossart et al. Where once even extracting DNA was challenging, metagenomic methods now can be used by many L&O readers, not just the specialists who developed the methods. These methods have also gotten cheaper. In particular, sequencing costs have declined dramatically while sequencing power has exploded beyond belief. Because of the huge decrease in costs, microbial ecologists can analyze more samples and explore ecological and biogeochemical questions that were not feasible until recently. Metagenomic-wielding investigators can now take more time points or sample more locations; replication is no longer a foreign word. In this regard, a metagenomic paper is starting to look more like a typical L&O paper. Finally, after being dominated by work in the oceans for many years, metagenomics is catching up to the range of aquatic habitats covered by L&O. Reflecting the recent growth in freshwater omics, this special issue has more freshwater papers than marine ones (16 vs. 6). I suppose purely oceanographic methods have appeared in L&O, but it seems appropriate that this freshwater-dominated special issue is coming out in a journal published by a society that started in 1936 as the Limnological Society of America. I hope this special issue will encourage more omic papers in L&O. The journal needs these papers if it is to see the latest advances in microbial ecology and microbial biogeochemistry. It would be an exaggeration to say that metagenomics needs L&O. But publication of more omic papers in L&O would signal a maturation of the field, that it is focusing less on methodology and more on understanding microbial communities and processes. Of course, metagenomics has already contributed many new insights into the ecology and biogeochemistry of microbes. This special issue shows how it can do even more.