Keyword Search Research Papers - Academia.edu (original) (raw)

Ontologies can represent large, multidimensional spaces: classi- cal music, research in computer science in the UK, health care for breast cancer are examples of rich domains. There have been no easy ways to represent meaningful slices... more

Ontologies can represent large, multidimensional spaces: classi- cal music, research in computer science in the UK, health care for breast cancer are examples of rich domains. There have been no easy ways to represent meaningful slices through these multi- dimensional spaces to privilege the parts of the domain that are of interest to a given user. mSpace, an interaction model

Mitochondrial single nucleotide polymorphisms (mtSNPs) constitute important data when trying to shed some light on human diseases and cancers. Unfortunately, providing relevant mtSNP genotyping information in mtDNA databases in a neatly... more

Mitochondrial single nucleotide polymorphisms (mtSNPs) constitute important data when trying to shed some light on human diseases and cancers. Unfortunately, providing relevant mtSNP genotyping information in mtDNA databases in a neatly organized and transparent visual manner still remains a challenge. Amongst the many methods reported for SNP genotyping, determining the restriction fragment length polymorphisms (RFLPs) is still one of the most convenient and cost-saving methods. In this study, we prepared the visualization of the mtDNA genome in a way, which integrates the RFLP genotyping information with mitochondria related cancers and diseases in a user-friendly, intuitive and interactive manner. The inherent problem associated with mtDNA sequences in BLAST of the NCBI database was also solved. V-MitoSNP provides complete mtSNP information for four different kinds of inputs: (1) color-coded visual input by selecting genes of interest on the genome graph, (2) keyword search by lo...

Online advertising market is becoming a popular area of academic research. Among other types of advertising, search engine advertising is leading the growth in terms of revenue. In general, there are two types of search engine... more

Online advertising market is becoming a popular area of academic research. Among other types of advertising, search engine advertising is leading the growth in terms of revenue. In general, there are two types of search engine advertising: paid placement ...

Online advertising market is becoming a popular area of academic research. Among other types of advertising, search engine advertising is leading the growth in terms of revenue. In general, there are two types of search engine... more

Online advertising market is becoming a popular area of academic research. Among other types of advertising, search engine advertising is leading the growth in terms of revenue. In general, there are two types of search engine advertising: paid placement ...

Online information repositories commonly provide keyword search facilities through textual query languages based on Boolean logic. However, there is evidence to suggest that the syntactic demands of such languages can lead to user errors... more

Online information repositories commonly provide keyword search facilities through textual query languages based on Boolean logic. However, there is evidence to suggest that the syntactic demands of such languages can lead to user errors and adversely affect the time that it takes users to form queries. Users also face difficulties because of the conflict in semantics between AND and OR when used in Boolean logic and English language. Analysis of usage logs for the New Zealand Digital Library (NZDL) show that few Boolean queries contain more than three terms, use of the intersection operator dominates and that query refinement is common. We suggest that graphical query languages, in particular Venn-like diagrams, can alleviate the problems that users experience when forming Boolean expressions with textual languages. A study of the utility of Venn diagrams for query specification indicates that with little or no training users can interpret and form Venn-like diagrams in a consistent manner which accurately correspond to Boolean expressions. We describe VQuery, a Venn-diagram based user interface to the New Zealand Digital Library (NZDL). In a study which compared VQuery with a standard textual Boolean interface, users took significantly longer to form queries and produced more erroneous queries when using VQuery. We discuss the implications of these results and suggest directions for future work.

A key to the Web's success is the power of search. The elegant way in which search results are returned is usually remarkably effective. However, for exploratory search in which users need to learn, discover, and understand novel or... more

A key to the Web's success is the power of search. The elegant way in which search results are returned is usually remarkably effective. However, for exploratory search in which users need to learn, discover, and understand novel or complex topics, there is substantial room for improvement. Human computer interaction researchers and web browser designers have developed novel strategies to

Keyness analysis is perhaps the most widely used technique within corpus approaches to (critical) discourse studies. As an automated keyness analysis usually returns a much larger number of key items than is feasible to examine manually... more

Keyness analysis is perhaps the most widely used technique within corpus approaches to (critical) discourse studies. As an automated keyness analysis usually returns a much larger number of key items than is feasible to examine manually within an appropriate co-text, the approach to selection of key items is of paramount importance, as it will determine the results and conclusions (Gabrielatos & Marchi, 2011). Currently, studies tend to adopt a methodologically naïve approach to selecting key items for manual analysis: they remove items from consideration before the automated analysis by using frequency thresholds or stoplists and/or or select a small sub-set of items returned by the automated analysis (e.g. the top-N key items and/or key items that they deem relevant to the focus of the study) (see Pojanapunya & Watson Todd, 2016). However, the above approaches lack a principled rationale, and adopting them can remove important key items from consideration and lead to cherry-picking – consequently rendering results and conclusions questionable. Also, keyness studies predominantly focus on differences between the compared corpora, and there are very few studies using keyness analysis to examine similarities (Taylor, 2013). This paper will discuss a new approach to selecting key items in a principled fashion, and will demonstrate the relevant procedures via a case study. The approach utilises cluster analysis, and caters for a focus on both difference and similarity. However, in order to contextualise the proposed procedure, the paper will need to preface its main focus with addressing a number of relevant misconceptions regarding the nature of keyness, the selection of the corpora to be compared (usually referred to as the study and reference corpus), and appropriate metrics for establishing keyness.

Manuscript type: Research paper Research aims: This paper aims to identify Malaysian companies that had adopted Enterprise Risk Management (ERM) and to determine the intensity of risk disclosure practised before and after the... more

Manuscript type: Research paper Research aims: This paper aims to identify Malaysian companies that had adopted Enterprise Risk Management (ERM) and to determine the intensity of risk disclosure practised before and after the implementation of the 2013 Bursa Malaysia Guidelines on Risk Management and Internal Control. Design/ Methodology/ Approach: This study used a dual approach of content analysis followed by an online survey. In the first phase, content analysis was performed on the annual reports of 754 Malaysian public listed companies by using the common terms used in ERM. In the second phase, an online survey was circulated among 330 ERM adopters which were identified from the content analysis approach. Research findings: Findings from the content analysis show that the overall level of risk disclosure before and after the current guidelines had increased by five (5) per cent. Findings from the online survey further suggest that 53 per cent of respondents confirmed that ERM is indeed an integral part of their organisation. Theoretical contributions/ Originality: This study seeks to broaden current literature on risk disclosure by investigating the regulatory impact on disclosure practices. The second contribution lies in the use of dual approaches to data collection: content analysis and online survey, both of which enhance the accuracy of findings without adversely impacting on its generalisability and the costs of conducting this research. Practitioner/ Policy implications: The findings of the current study reflect on the true ERM adoption rate in this part of the region which is useful to practitioners who are still skeptical of ERM. Knowing that more than half of the public listed companies have implemented ERM may be the motivation for the non-adopters to implement ERM. Moreover, findings will encourage policy makers to introduce voluntary guidelines to regulate ERM implementation and disclosure practices in Malaysia. Research limitations/ Implications: The use of keyword search to identify ERM adopters bears the conflict of substance over form, particularly when the common terms in the disclosure do not reflect the actual practices. Future research may need to address the conflicts by using a score method that can help to improve the scientific aspects of the methodology. A framework for the analysis of risk communication and an index to measure the quality of risk disclosure can further enhance the instrument.

The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access quality. The privacy of health data can only be preserved by keeping it in an... more

The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access quality. The privacy of health data can only be preserved by keeping it in an encrypted form, but it affects usability and flexibility in terms of effective search. Attribute-based searchable encryption (ABSE) has proven its worth by providing fine-grained searching capabilities in the shared cloud storage. However, it is not practical to apply this scheme to the devices with limited resources and storage capacity because a typical ABSE involves serious computations. In a healthcare cloud-based cyber-physical system (CCPS), the data is often collected by resource-constraint devices; therefore, here also, we cannot directly apply ABSE schemes. In the proposed work, the inherent computational cost of the ABSE scheme is managed by executing the computationally intensive tasks of a typical ABSE scheme on the blockchain network. Thus, it makes the proposed scheme suitable for online storage and retrieval of personal health data in a typical CCPS. With the assistance of blockchain technology, the proposed scheme offers two main benefits. First, it is free from a trusted authority, which makes it genuinely decentralized and free from a single point of failure. Second, it is computationally efficient because the computational load is now distributed among the consensus nodes in the blockchain network. Specifically, the task of initializing the system, which is considered the most computationally intensive, and the task of partial search token generation, which is considered as the most frequent operation, is now the responsibility of the consensus nodes. This eliminates the need of the trusted authority and reduces the burden of data users, respectively. Further, in comparison to existing decentralized fine-grained searchable encryption schemes, the proposed scheme has achieved a significant reduction in storage and computational cost for the secret key associated with users. It has been verified both theoretically and practically in the performance analysis section.

Post-traumatic stress disorder (PTSD) is a potentially serious psychiatric disorder that has traditionally been associated with traumatic stressors such as participation in combat, violent assault, and survival of natural disasters.... more

Post-traumatic stress disorder (PTSD) is a potentially serious psychiatric disorder that has traditionally been associated with traumatic stressors such as participation in combat, violent assault, and survival of natural disasters. Recently, investigators have reported that the experience of critical illness can also lead to PTSD, although details of the association between critical illness and PTSD remain unclear. We conducted keyword searches of MEDLINE and Psych Info and investigations of secondary references for all articles pertaining to PTSD in medical intensive care unit (ICU) survivors. From 78 screened papers, 16 studies (representing 15 cohorts) and approximately 920 medical ICU patients met inclusion criteria. A total of 10 investigations used brief PTSD screening tools exclusively as opposed to more comprehensive diagnostic methods. Reported PTSD prevalence rates varied from 5% to 63%, with the three highest prevalence estimates occurring in studies with fewer than 30 patients. Loss to follow-up rates ranged from 10% to 70%, with average loss to follow-up rates exceeding 30%. Exact PTSD prevalence rates cannot be determined due to methodological limitations such as selection bias, loss to follow-up, and the wide use of screening (as opposed to diagnostic) instruments. In general, the high prevalence rates reported in the literature are likely to be overestimates due to the limitations of the investigations conducted to date. Although PTSD may be a serious problem in some survivors of critical illness, data on the whole population are inconclusive. Because the magnitude of the problem posed by PTSD in survivors of critical illness is unknown, there remains a pressing need for larger and more methodologically rigorous investigations of PTSD in ICU survivors.

Digital libraries stand to benefit from technology insertions from the fields of information visualization, human-computer interaction, and cognitive psychology, among others. However, the current state of interaction between these fields... more

Digital libraries stand to benefit from technology insertions from the fields of information visualization, human-computer interaction, and cognitive psychology, among others. However, the current state of interaction between these fields is not well understood. We use our knowledge visualization tool, VxInsight , to provide several domain visualizations of the overlap between these fields. Relevant articles were extracted from the Science Citation Indexes (SCI and Social SCI) using keyword searches. An article map, a semantic (co-term) map, and a co-author network have been generated from the data. Analysis reveals that while there are overlaps between fields, they are not substantial. However, the most recent work suggests areas where future collaboration could have a great impact on digital libraries of the future.

Abstract—In this work, a template-based search approach is adopted for the Keyword Search (KWS) problem on two of the low-resource languages (Turkish and Swahili). In lowresource languages, the use of Large Vocabulary Continuous Speech... more

Abstract—In this work, a template-based search approach
is adopted for the Keyword Search (KWS) problem on two
of the low-resource languages (Turkish and Swahili). In lowresource
languages, the use of Large Vocabulary Continuous
Speech Recognition (LVCSR) systems in KWS tasks may perform
poorly especially on out-of-vocabulary words. In the proposed
method, the keywords are modeled to be in the same form of
the audio search document in an artificial manner and utilizing
template-based search methods, the performance of the baseline
system is improved.

Web search users complain of the inaccurate results produced by current search engines. Most of these inaccurate results are due to a failure to understand the user’s search goal. This paper proposes a method to extract users’ intentions... more

Web search users complain of the inaccurate results produced by current search engines. Most of these inaccurate results are due to a failure to understand the user’s search goal. This paper proposes a method to extract users’ intentions and to build an intention map representing these extracted intentions. The proposed method makes intention vectors from clicked pages from previous search logs obtained on a given query. The components of the intention vector are weights of the keywords in a document. It extracts user’s intentions by using clustering the intention vectors and extracting intention keywords from each cluster. The extracted the intentions on a query are represented in an intention map. For the efficiency analysis of intention map, we extracted user’s intentions using 2,600 search log data a current domestic commercial search engine. The experimental results with a search engine using the intention maps show statistically significant improvements in user satisfaction scores.