Text information in scanned documents becomes accessible only when extracted and interpreted by a text recognizer. For a recognizer to work successfully, it must have detailed location information about the regions of the document images that it is asked to analyse. It will need focus on page regions with text skipping non-text regions that include illustrations or photographs. However, text recognizers

In our study, we examine the impact of citation network structures on the ability to discern valuable research topics in Computer Science literature. We use the bibliographic information available in the DBLP database to extract candidate phrases from scientific paper abstracts. Following that, we construct citation networks based on direct citation, co-citation and bibliographic coupling relationships

The Semantic Web provides guidelines for the representation of information about real-world objects (entities) and their relations (properties). This is helpful for the dissemination and consumption of information by people and applications. However, the information is mainly contained within natural language sentences, which do not have a structure or linguistic descriptions ready to be directly processed

Open research data (ORD) have been considered a driver of scientific transparency. However, data friction, as the phenomenon of data underutilisation for several causes, has also been pointed out. A factor often called into question for ORD low usage is the quality of the ORD and associated metadata. This work aims to illustrate the use of ORD, published by the Figshare scientific repository, concerning

Popularity prediction of online contents is always a tool of emergency management, business decision-making, and public opinion monitoring. Most previous work has made efforts to predict the volumes or levels of popularity, but patterns of popularity evolution are remaining largely unexplored. Actually, topic popularity patterns can offer more detailed information for event detection and early warning

To help students learn how to programme, we have to give them a clear knowledge map and sufficient materials. Question-based websites, such as stackoverflow, are excellent information sources for this goal. However, for beginners, the process can be a little tricky since they may not know how to ask correct questions if they do not have sufficient background knowledge, and a knowledge tree is usually

The scholarly publication landscape continues to grow in complexity, presenting researchers with ever-increasing dilemmas regarding journal choice. However, research into the decision-making processes associated with journal choice is limited. This article contributes by reporting on an international survey of researchers in various disciplines and with varying levels of experience. The study examines

COVID-19 has changed the lifestyle of many people due to its rapid human-to-human transmission. The spread started at the end of January 2020, and different countries used different approaches in terms of testing, sanitization, lock down and quarantine centres to control the spread of the virus. People are getting back to working and routine life activities with new normal standards of testing, sanitization

One of the key problems in image retrieval systems is the presence of irrelevant and noisy image content. Such content can cause significant confusion for the system. Therefore, there is a need to represent images with only informative features in order to improve the retrieval performance of the system or any subsequent process. In this article, we propose a method to identify the informative features

The unprecedented outbreak of COVID-19 is one of the most serious global threats to public health in this century. During this crisis, specialists in information science could play key roles to support the efforts of scientists in the health and medical community for combatting COVID-19. In this article, we demonstrate that information specialists can support health and medical community by applying

Previous research on information dissemination in emergencies focus on prediction of the volume via abundant models. However, most of these models did not specify different stages of emergencies, and hence making it difficult for public relations (PR) practitioner to make decisions based on needs of each stage in today’s rapid changing media environments. In this study, we introduce the idea of system

Taxonomy merging is an important work to provide a uniform schema for several heterogeneous taxonomies. Previous studies primarily focus on merging two taxonomies in a specific domain, while the merging of multiple taxonomies has been neglected. This article proposes a taxonomy merging approach to automatically merge multiple source taxonomies into a target taxonomy in an asymmetric manner. The approach

Using big data has been a prevailing research trend in various academic fields. However, no studies have explored the scope and structure of big data across disciplines. In this article, we applied topic modeling and word co-occurrence analysis methods to identify key topics from more than 36,000 big data publications across all academic disciplines between 2012 and 2017. The results revealed several

For some decades now, Galleries, Libraries, Archives and Museums (GLAM) institutions have published and provided access to information resources in digital format. Recently, innovative approaches have appeared such as the concept of Labs within GLAM institutions that facilitates the adoption of innovative and creative tools for content delivery and user engagement. In addition, new methods have been

This article defines and explores the direct citations between citing publications (DCCPs) of a publication. We construct an ego-centred citation network for each paper that contains all of its citing papers and itself, as well as the citation relationships among them. By utilising a large-scale scholarly dataset from the computer science field in the Microsoft Academic Graph (MAG-CS) dataset, we find

The hα index was recently proposed to measure the degree of scientific leadership. While the concept is useful and interesting, namely, as a complement to the traditional performance analysis, the metric suffers from important shortcomings. We argue that scientific leadership should be evaluated: (1) taking into account information of the moment the paper is produced/published, and (2) in the specific

Organisations use data in different formats: Word documents, Excel spreadsheets, databases, HTML pages and so on. It is not easy to make decisions with such data due to the lack of integration between the different sources and built-in decision-making rules. Decisions can be reached with knowledge bases, which, unlike databases, make it possible to store not only objects, facts and attributes but also

Transition forms a dynamic concept that has been underexplored within information literacy research and practice. This article uses the grounded theory of mitigating risk, which was produced through doctoral research into the information literacy practices of language-learners, as a lens for a more detailed examination of transition and its role within information literacy. This framing demonstrates

To explore to what extent the mobility of scientists disrupts the stability of their research collaboration, we designed a measure − Collaboration Stability After Moving (CSAM) − for scientists, retrieved 4343 US-related scientists’ curricula vitae (CVs) from the Open Researcher and Contributor ID (ORCID) website and publication records in the Web of Science database and applied a linear regression

Cultural heritage institutions have recently started to share their metadata as Linked Open Data (LOD) in order to disseminate and enrich them. The publication of large bibliographic data sets as LOD is a challenge that requires the design and implementation of custom methods for the transformation, management, querying and enrichment of the data. In this report, the methodology defined by previous

To retrieve a specific news article from a vast archive containing multilingual news articles against a user query or based on similarity among news articles is a challenging task. The task becomes even further complicated when the archive contains articles from a low resourced and morphologically complex language like Urdu, along with English new articles. The article proposes a content-based (lexical)

In the research on interdisciplinarity (RID), measures for evaluating the interdisciplinarity of scientific entities (e.g., papers, authors, journals or research areas) have been proposed for a long time. The author interdisciplinarity is very different from the other types of interdisciplinarity because of the complex interpersonal relationships between the connected authors. However, previous work

Academic data management has become an increasingly challenging task as research evolves over time. Essential tasks such as information retrieval and research networking have turned into extremely difficult operations due to an ever-growing number of researchers and scientific articles. Numerous initiatives have emerged in the IT environments to address this issue, especially focused on web technologies

Online social media (OSM) has become a hotbed for the rapid dissemination of disinformation or faked news. In order to track and limit the spread of faked news, we study stance identification of comments posted on OSM, where the stance can denote the comment’s semantics. In this article, we propose a framework for identification of rumour stances, combining network topology and OSM comments. We construct

Researchers give credit to peer-reviewed, and thus, credible publications through citations. Despite a rigorous reviewing process, certain articles undergo retraction due to disclosure of their ethical or scientific deficiencies. It is, therefore, important to understand how society and academia react to the erroneous or deceitful claims and purge the science of their unreliable results. Applying a

The process of tracking the evolution of a scientific field is arduous. It allows researchers to understand trends in areas of science and predict how they may evolve. Nowadays, most of the automated mechanisms developed to assist researchers in this process do not consider the content of articles to identify changes in its structure, only the articles metadata. These methods are not suited to easily

As part of moves towards open knowledge practices, making peer review open is cited as a way to enable fuller scrutiny and transparency of assessments around research. There are now many flavours of open peer review in use across scholarly publishing, including where reviews are fully attributable and the reviewer is named. This study examines whether there is any evidence of bias in two areas of common

The purpose of this research is to map and analyse the conceptual and thematic structure of library and information science (LIS) research from the perspective of the co-word analysis. The bibliographical records consist of all the research papers published in the LIS core journals between 1990 and 2016 and indexed in Web of Science. ‘CiteSpace’ was used to visualise the co-word network of LIS studies

In order to achieve real-time updating of the domain knowledge graph and improve the relationship extraction ability in the construction process, a domain knowledge graph construction method is proposed. Based on the structured knowledge in Wikipedia’s classification system, we acquire concepts and instances contained in subject areas. A relationship extraction algorithm based on co-word analysis is

Information seeking research has provided models of users in the search for information across many different contexts and situations. Digital content curation has emerged as a means for managing information and facilitating user learning by adding ‘value’ to digital content in different ways, enhancing the user experience. Using digital video and K–12 education as the context, this study examined

With the tremendous growth of scientific literature in recent years, methods of detecting and analysing research topics have become more and more important. This study proposes a topic analysis method combining latent Dirichlet allocation (LDA) and a three-dimensional strategic diagram. This study constructs the three-dimensional strategic diagram by three dimensions of centrality, density and novelty

Mobile devices are the technological basis of computational intelligent systems, yet traditional mobile application interfaces tend to rely only on the touch modality. That said, such interfaces could improve human–computer interaction by combining diverse interaction modalities, such as visual, auditory and touch. Also, a lot of information on the Web is published under the Linked Data principles

Increasing videos available in educational content repositories makes searching difficult, and recommendation systems have been used to help students and teachers receive a content of interest. Speech is an important carrier of information in video lectures and is used by content-based video recommendation systems. Although automatic speech recognition (ASR) transcripts have been used in modern video

Text classification (TC) is very important and critical task in the 21th century as there exist high volume of electronic data on the Internet. In TC, textual data are characterised by a huge number of highly sparse features/terms. A typical TC consists of many steps and one of the most important steps is undoubtedly feature selection (FS). In this study, we have comprehensively investigated the effects

The purpose of the study is to (a) contribute to annotating an Altmetrics dataset across five disciplines, (b) undertake sentiment analysis using various machine learning and natural language processing–based algorithms, (c) identify the best-performing model and (d) provide a Python library for sentiment analysis of an Altmetrics dataset. First, the researchers gave a set of guidelines to two human

Social media are powerful networking platforms that provide users with significant information opportunities. Despite this, little is known about their impact on job search behaviour. Here, interview (participants = 7), focus group (participants = 6) and survey (n = 558) data supplied by young jobseekers in Scotland were analysed to investigate the role of social media in job search. The findings show

Bringing together considerations from three research trends (honest signals of collaboration, socio-semantic networks and homophily theory), we hypothesise that word use similarity and having similar social network positions are linked with the level of employees’ digital interaction. To verify our hypothesis, we analyse the communication of close to 1600 employees, interacting on the intranet communication

Information systems have been improving in helping users find information. However, they have been less attended to regarding helping searchers in using located information. This research attempts to address the issue of information use by investigating what information systems and features searchers think are helpful in using located information to accomplish information tasks. In all, 32 college

A universal knowledge base can be defined as a domain-independent ontology containing instances. Ontologies define the concepts and relations among these concepts and are used to represent a domain of interest. These universal knowledge bases are the elementary units for automated reasoning on the Semantic Web. The Semantic Web is an extension of the World Wide Web which facilitates software agents

Publishing mobile applications on the official stores is becoming a big business. Many developers are charmed by the billion-dollar success of breakout applications. Thus, in order to ensure success, mobile applications need to sustain top ranking. Previous work on the predictability of mobile applications success aimed to extract from app stores relevant features that influence high rating. In this

Large quantities of textual posts are increasingly generated in course discussion forums, and the accumulation of these data greatly increases the cognitive loads on online participants. It is imperative for them to automatically identify the potential semantic information derived from these textual discourse interactions. Moreover, existing topic models can discover the latent topics or sentimental

Nowadays, cyber hate speech is increasingly growing, which forms a serious problem worldwide by threatening the cohesion of civil societies. Hate speech relates to using expressions or phrases that are violent, offensive or insulting for a person or a minority of people. In particular, in the Arab region, the number of Arab social media users is growing rapidly, which is accompanied with high increasing

Special libraries are essential information and documentation centres for university teachers and researchers due to the quality and richness of their collections. In Spain, it is estimated that there are 2456 special libraries, although many are unknown either generally or among information professionals. These include museum libraries, which are important centres with valuable collections of bibliographic

The concept of big data has been extensively considered as a technological modernisation in organisations and educational institutes. Thus, the purpose of this study is to determine whether the modified technology acceptance model (MTAM) is viable for evaluating the performance of librarians in the use of big data analytics in academic libraries. This study used an empirical research method for collecting

The relational database (RDB) to resource description framework (RDF) transformation is a major semantic information extraction method because most web data are managed by RDBs. Existing automatic RDB-to-RDF transformation methods generate RDF data without losing the semantics of original relational data. However, two major problems have been observed during the mapping of multi-column key constraints:

For those who are not experts in a particular scientific field, it is difficult to understand scientific research trends. Although studies on the extraction of research trends have been conducted, most focus on extracting global trends from large-scale data, and the methods are often complicated. The purpose of this study is to develop a method of obtaining overviews of a scientific field for non-experts

Dividing papers based on their numbers of citations into several groups constitutes one of the most common research practices in bibliometrics and beyond. However, existing dividing methods are both arbitrary and subject to bias. This article proposes a novel approach to partition highly, medium and lowly cited publications based on their citation distribution. We utilise the whole Web of Science (WoS)

What are the landmark papers in scientific disciplines? Which papers are indispensable for scientific progress? These are typical questions which are of interest not only for researchers (who frequently know the answers – or guess to know them) but also for the interested general public. Citation counts can be used to identify very useful papers since they reflect the wisdom of the crowd – in this

Text segmentation (TS) is the process of dividing multi-topic text collections into cohesive segments using topic boundaries. Similarly, text clustering has been renowned as a major concern when it comes to multi-topic text collections, as they are distinguished by sub-topic structure and their contents are not associated with each other. Existing clustering approaches follow the TS method which relies

This article studies cross-lingual text similarity using neural machine translation models. A straightforward approach based on machine translation is to use translated text so as to make the problem monolingual. Another possible approach is to use intermediate states of machine translation models as recently proposed in the related work, which could avoid propagation of translation errors. We aim

Sentiment analysis plays an important role in understanding individual opinions expressed in websites such as social media and product review sites. The common approaches to sentiment analysis use the sentiments carried by words that express opinions and are based on either supervised or unsupervised learning techniques. The unsupervised learning approach builds a word-sentiment dictionary, but it

News media websites are important online resources that have drawn great attention of text mining researchers. The main aim of this study is to propose a framework for ranking online news websites from different viewpoints. The ranking of news websites provides useful information, which can benefit many news-related tasks such as news retrieval and news recommendation. In the proposed framework, the

By applying different clustering algorithms, the author strived to construct the best visual representation of scientific domains and disciplines in Poland. Journals and their disciplinary categories constituted a data set. A comparative analysis of maps was based on both qualitative and quantitative approaches. Complex patterns of eight maps were evaluated taking into account both the local proximity

Social media plays an increasingly important role in travel information seeking and decision-making. However, there is limited understanding of how a group of tourists use social media to plan trips collaboratively and the different practices between countries. In this study, we investigated the collaborative information seeking (CIS) and sharing behaviours of mobile social media users from Australia

This article presents analysis of data from scientific articles and patents to identify the evolving trends and underlying topics in research on humanoid robots. We used topic modelling based on latent Dirichlet allocation analysis to identify underlying topics in sub-areas in the field. We also used social network analysis to measure the centrality indices of publication keywords to detect important

Wikipedia is important in higher education because students and scholars often use it. Nevertheless, the issue of Wikipedia’s quality is an obstacle for its use at the higher education level. In order to contribute to this discussion, we have proposed ‘Verifiability by respected sources’ as an indicator for assessing the quality of Wikipedia articles at the higher education level and conducted an analysis

The use of online information sources in most professions is widespread, and well researched. Less understood is how the use of these sources vary across the strata within a single profession, and how question context affects search behaviour. Using the dental profession as a case of a highly stratified discipline, we examine search preferences for sources by professional strata among dentists in a

Recently, link prediction has attracted more attention from various disciplines such as computer science, bioinformatics and economics. In link prediction, numerous information such as network topology, profile information and user-generated contents are considered to discover missing links between nodes. Whereas numerous previous researches had focused on the structural features of the networks for

In the current Semantic Web Community, as the size and complexity of ontologies increase, ontology summarisation is becoming more important. There are many studies in the literature that use different approaches and metrics. However, many of these studies are not effective in terms of performance or have integration issues with current technologies. In this study, the popular ontology summarisation

Semantic interoperability issues of international e-Government data exchanges have not been solved up until now. In the case of social security institutions, the data exchange operations have some particularities that make that the non-ambiguous definition of core concepts used in the institutions has a key impact on the success and quality of system interconnections. In this article, we present the