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Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis. A wide variety of SciBERT‐based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work. Semantic main path network was built by merging the top‐K main paths extracted from various time slices of semantic citation network. In addition, a three‐way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics‐agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas. [ABSTRACT FROM AUTHOR]

Purpose - The purpose of this paper is to explore the application of post-structuralist theory to understanding hypertext and the World Wide Web, and the challenge posed by digital information technology to the practices of the information profession. Design/methodology/approach - The method adopted is that of a critical study. Findings - The paper argues for the importance of post-structuralism for an understanding of the implications of digital information for the information management profession. Originality/value - Focuses on an epistemological gap between the traditional practices of the information profession, and the structure of the World Wide Web. [ABSTRACT FROM AUTHOR]

Citation analysis was used to measure the impact of GESAMP, the Joint Group of Experts on the Scientific Aspects of Marine Environmental Protection, which since 1969 has published reports for the United Nations and seven of its agencies. Web of Science was used to search for citations to 114 publications, of which 15 are journal articles or books. Citations to grey literature can be difficult to locate and interpret, but two-thirds of the 1436 citations, in 1178 citing papers, are to grey literature items. The distribution of citations and self-citation are examined. Journal versions were cited more than corresponding reports. Core journals for GESAMP citations include seven environmental science journals and a social science journal. This paper confirms that citation searching can successfully measure the impact of organizations producing grey literature. Such publications can be very influential, diffusing widely from their source. [ABSTRACT FROM AUTHOR]

Over the last three decades, research in Information Retrieval (IR) shows performance improvement when many sources of evidence are combined to produce a ranking of documents. Most current approaches assess document relevance by computing a single score which aggregates values of some attributes or criteria. They use analytic aggregation operators which either lead to a loss of valuable information, e.g., the min or lexicographic operators, or allow very bad scores on some criteria to be compensated with good ones, e.g., the weighted sum operator. Moreover, all these approaches do not handle imprecision of criterion scores. In this paper, we propose a multiple criteria framework using a new aggregation mechanism based on decision rules identifying positive and negative reasons for judging whether a document should get a better ranking than another. The resulting procedure also handles imprecision in criteria design. Experimental results are reported showing that the suggested method performs better than standard aggregation operators. [ABSTRACT FROM AUTHOR]

In this paper we presented a framework for query refinement which is driven by user's information needs. Based on the analyses of the real web IR case studies we model the query refinement process as the process of decreasing query ambiguity with respect to the user's need. In the refinement process a user is provided with the query's neighborhood and the guidelines for changing the ambiguities of the query. The approach assumes the existence of a controlled vocabulary, which is used for describing the content of information resources. We model the queries (query-answering pairs), ambiguities and refinements formally. The partial ordering of the query-answering pairs in the searching space represents the mathematical lattice structure, so that formal concept analysis is used as the mathematical backbone of the query refinement process. We present a case study related to the searching in MEDLINE, which shows the benefits of using our approach. [ABSTRACT FROM AUTHOR]

Recent development of the network such as the Internet makes it easy to retrieve documents over the World Wide Web (WWW). Document retrieval with search engines is convenient, especially to retrieve documents for a domain with which a user is not so familiar, since the retrieved documents can be used to get a gross overview of the domain as a preliminary investigation. However, when inappropriate query terms are used, it often results in retrieving too many documents and the retrieved documents have to be scanned by the user him/herself to find out truly valuable ones. This paper proposes a document retrieval support system to reduce such "dual" retrieval by suggesting terms based on the relationship of relevant terms to the specified query terms. Semantic relations of terms are dealt with using a thesaurus and relevant terms for the terms are organized into a tree structure based on the relations defined in the thesaurus. Since the value of retrieved documents is intrinsically user-dependent, even when the same query terms are used, appropriate terms to facilitate document retrieval can vary depending on users. To realize personalization in term suggestion, the relationship of terms for the user is learned from a user-supplied case so that personalized terms can be suggested. A prototype system has been implemented and it is evaluated through experiments. The results of experiments are encouraging and show that it is worth following this path, especially for supporting users who are not yet familiar with the target domain. [ABSTRACT FROM AUTHOR]

In this paper we describe the practical aspects of extracting and using a glossary for a selected technical domain. We first describe the existing glossary extraction process, as applied to general corpora, and examine its shortcomings in the technical support domain. Then we propose a number of enhancements to it, including focusing the glossary on a selected domain context, providing support for multidomain glossaries, and importing domain-specific dictionaries. We apply our focused-glossary approach to the IBM Technical Support corpus and incorporate resulting glossaries within the information search and delivery system used by IBM Technical Support. We demonstrate the effectiveness of our approach by evaluating the quality of keywords and terms extracted from sample documents with the help of these glossaries. [ABSTRACT FROM AUTHOR]

This is the first in the series of the papers on an application of the systems analytic approach to evaluation of information retrieval (IR). At the beginning, the importance of consistent terminology in information retrieval is discussed, and different definitions of “information retrieval” and “IR system” are presented. The importance of identifying boundaries of the IR system for clarifying terminological differences is discussed. The terminological problems in IR are summarized, and some of the existing approaches to their solution are identified. A historical overview of IR research is presented in order to explain the existing terminological differences and to create a common basis for a discussion of evaluation approaches to IR. [ABSTRACT FROM AUTHOR]

The article describes the type of paper submissions that will be accepted by the program committee of the American Society for Information Science and Technology. There are four types of paper submissions: 1) contributed papers; 2) contributed posters and short papers; 3) symposia and panels; and, 4) pre-conference sessions. For instance, contributed papers present original, recent research and design projects, and theoretical developments or innovative practical applications providing more general insight into an area of practice.

The article discusses the content of the articles within the issue. The first four articles in this issue focus on the psychology of information and information systems. One article analyzes the notion of information privacy. In the paper of Gail Rein, she explores the conceptualization and encoding of reputation. Another paper, combines psychology, linguistics and information architecture to explore how the precise wording used in menus and hyperlinks affects end user behavior on website. Finally, the author builds on this new media theme, recognizing that the information world is shifting towards streaming sources where information is fed continuously to many devices.