scholarly journals Emergence: Documents in Crisis

2021 ◽  
Vol 8 (2) ◽  
Author(s):  
Wayne deFremery
Keyword(s):  
Semantic Associations

This essay suggests the etymologies of emergence, emergency, and crisis create a useful framework for theorizing documents. Indeed, the overlapping semantic associations of the words allow for the idea that documents emerge in crisis. The semantic overlap also allows a means for theorizing how documents descend into crisis. Theorizing documents in crisis, the essay argues, usefully complements documentalist theories of documentary representation suggested by thinkers like Paul Otlet and Suzanne Briet, as well newer conceptualizations of documentality as conceived by Michael Buckland and Maurizio Ferraris and documentarity as described by Ronald Day.

scholarly journals Bibliometric Analysis of SARS, MERS, and COVID-19 Studies from India and Connection to Sustainable Development Goals

2021 ◽  
Vol 13 (14) ◽  
pp. 7555
Author(s):  
Raghu Raman ◽  
Ricardo Vinuesa ◽  
Prema Nedungadi
Keyword(s):  
United States ◽  
Sustainable Development ◽  
Bibliometric Analysis ◽  
Data Access ◽  
Well Being ◽  
Good Health ◽  
The United States ◽  
Social Sciences And Humanities ◽  
Semantic Associations ◽  
Number Of Publications

India is ranked fifth in the world in terms of COVID-19 publications accounting for 6.7% of the total. About 60% of the COVID-19 publications in the year 2020 are from United States, China, UK, Italy, and India. We present a bibliometric analysis of the publication trends and citation structure along with the identification of major research clusters. By performing network analysis of authors, citations, institutions, keywords, and countries, we explore semantic associations by applying visualization techniques. Our study shows lead taken by the United States, China, UK, Italy, India in COVID-19 research may be attributed to the high prevalence of COVID-19 cases in those countries witnessing the first outbreak and also due to having access to COVID-19 data, access to labs for experimental trials, immediate funding, and overall support from the govt. agencies. A large number of publications and citations from India are due to co-authored publications with countries like the United States, UK, China, and Saudi Arabia. Findings show health sciences have the highest number of publications and citations, while physical sciences and social sciences and humanities counts were low. A large proportion of publications fall into the open-access category. With India as the focus, by comparing three major pandemics—SARS, MERS, COVID-19—from a bibliometrics perspective, we observe much broader involvement of authors from multiple countries for COVID-19 studies when compared to SARS and MERS. Finally, by applying bibliometric indicators, we see an increasing number of sustainable development-related studies from the COVID-19 domain, particularly concerning the topic of good health and well-being. This study allows for a deeper understanding of how the scholarly community from a populous country like India pursued research in the midst of a major pandemic which resulted in the closure of scientific institutions for an extended time.

scholarly journals Greater cross-viewer similarity of semantic associations for representational than for abstract artworks

2015 ◽  
Vol 15 (14) ◽  
pp. 12 ◽  
Author(s):  
Astrid Schepman ◽  
Paul Rodway ◽  
Sarah J. Pullen
Keyword(s):  
Semantic Associations

Multi-Target Search on Semantic Associations in Linked Data

2018 ◽  
Vol 14 (1) ◽  
pp. 71-97 ◽  
Author(s):  
Xiang Zhang ◽  
Erjing Lin ◽  
Yulian Lv
Keyword(s):  
Linked Data ◽  
Large Scale ◽  
Search Process ◽  
Search Model ◽  
Linguistic Features ◽  
Target Search ◽  
Search Results ◽  
Semantic Associations ◽  
Effectiveness And Efficiency ◽  
Virtual Document

In this article, the authors propose a novel search model: Multi-Target Search (MT search in brief). MT search is a keyword-based search model on Semantic Associations in Linked Data. Each search contains multiple sub-queries, in which each sub-query represents a certain user need for a certain object in a group relationship. They first formularize the problem of association search, and then introduce their approach to discover Semantic Associations in large-scale Linked Data. Next, they elaborate their novel search model, the notion of Virtual Document they use to extract linguistic features, and the details of search process. The authors then discuss the way search results are organized and summarized. Quantitative experiments are conducted on DBpedia to validate the effectiveness and efficiency of their approach.

Approaches for Semantic Association Mining and Hidden Entities Extraction in Knowledge Base

2010 ◽  
pp. 119-141 ◽  
Author(s):  
Thabet Slimani ◽  
Boutheina Ben Yaghlane ◽  
Khaled Mellouli
Keyword(s):  
Semantic Web ◽  
Knowledge Base ◽  
Domain Knowledge ◽  
Information And Communications Technology ◽  
Association Mining ◽  
Semantic Association ◽  
Real World Data ◽  
Semantic Associations ◽  
Hyperclique Pattern ◽  
Flow Of Information

Due to the rapidly increasing use of information and communications technology, Semantic Web technology is being increasingly applied in a large spectrum of applications in which domain knowledge is represented by means of an ontology in order to support reasoning performed by a machine. A semantic association (SA) is a set of relationships between two entities in knowledge base represented as graph paths consisting of a sequence of links. Because the number of relationships between entities in a knowledge base might be much greater than the number of entities, it is recommended to develop tools and invent methods to discover new unexpected links and relevant semantic associations in the large store of the preliminary extracted semantic association. Semantic association mining is a rapidly growing field of research, which studies these issues in order to create efficient methods and tools to help us filter the overwhelming flow of information and extract the knowledge that reflect the user need. The authors present, in this work, an approach which allows the extraction of association rules (SWARM: Semantic Web Association Rule Mining) from a structured semantic association store. Then, present a new method which allows the discovery of relevant semantic associations between a preliminary extracted SA and predefined features, specified by user, with the use of Hyperclique Pattern (HP) approach. In addition, the authors present an approach which allows the extraction of hidden entities in knowledge base. The experimental results applied to synthetic and real world data show the benefit of the proposed methods and demonstrate their promising effectiveness.

Mining Organizations’ Networks

2011 ◽  
pp. 205-230 ◽  
Author(s):  
James A. Danowski
Keyword(s):  
Social Network ◽  
Oil Spill ◽  
Organizational Hierarchy ◽  
Art And Design ◽  
Bp Oil Spill ◽  
Network Mining ◽  
Social Network Mining ◽  
Corporate Innovation ◽  
Semantic Associations ◽  
Over Time

This chapter presents six examples of organization-related social network mining: 1) interorganizational and sentiment networks in the Deepwater BP Oil Spill events, 2) intraorganizational interdepartmental networks in the Savannah College of Art and Design (SCAD), 3) who-to-whom email networks across the organizational hierarchy the Ford Motor Company’s automotive engineering innovation: “Sync® w/ MyFord Touch”, 4) networks of selected individuals who left that organization, 5) semantic associations across email for a corporate innovation in that organization, and 6) assessment of sentiment across its email for innovations over time. These examples are discussed in terms of motivations, methods, implications, and applications.

The different representational frameworks underpinning abstract and concrete knowledge: Evidence from odd-one-out judgements

2009 ◽  
Vol 62 (7) ◽  
pp. 1377-1390 ◽  
Author(s):  
Sebastian J. Crutch ◽  
Sarah Connell ◽  
Elizabeth K. Warrington
Keyword(s):  
Concrete Word ◽  
Cognitive Tasks ◽  
Research Participants ◽  
Semantic Associations ◽  
Abstract Words ◽  
Concrete Concepts ◽  
Normal Cognition ◽  
Neuropsychological Investigations ◽  
Concrete Words

Recent evidence from neuropsychological investigations of individuals with global aphasia and deep or deep-phonological dyslexia suggests that abstract and concrete concepts are underpinned by qualitatively different representational frameworks. Abstract words are represented primarily by their association to other words, whilst concrete words are represented primarily by their taxonomic similarity to one another. In the current study, we present the first evidence for this association/similarity distinction to be gathered from healthy research participants. Using a semantic odd-one-out task, it is shown that normal participants identify associative connections more quickly than similarity-based connections when processing abstract words, but that the pattern is reversed for concrete words. It is also demonstrated that the typical concrete-word advantage observed in many cognitive tasks is abolished and even reversed when participants have to comprehend the semantic associations between words. The data provide converging evidence for the different representational frameworks hypothesis and suggest that claims based on information from previous neuropsychological investigations can be generalized to normal cognition.

scholarly journals Rule-Guided Compositional Representation Learning on Knowledge Graphs

2020 ◽  
Vol 34 (03) ◽  
pp. 2950-2958
Author(s):  
Guanglin Niu ◽  
Yongfei Zhang ◽  
Bo Li ◽  
Peng Cui ◽  
Si Liu ◽  
...  
Keyword(s):  
State Of The Art ◽  
Representation Learning ◽  
Vector Spaces ◽  
Semantic Structure ◽  
Completion Task ◽  
Joint Embedding ◽  
Semantic Associations ◽  
Structured Information ◽  
Low Dimensional ◽  
Embedding Methods

Representation learning on a knowledge graph (KG) is to embed entities and relations of a KG into low-dimensional continuous vector spaces. Early KG embedding methods only pay attention to structured information encoded in triples, which would cause limited performance due to the structure sparseness of KGs. Some recent attempts consider paths information to expand the structure of KGs but lack explainability in the process of obtaining the path representations. In this paper, we propose a novel Rule and Path-based Joint Embedding (RPJE) scheme, which takes full advantage of the explainability and accuracy of logic rules, the generalization of KG embedding as well as the supplementary semantic structure of paths. Specifically, logic rules of different lengths (the number of relations in rule body) in the form of Horn clauses are first mined from the KG and elaborately encoded for representation learning. Then, the rules of length 2 are applied to compose paths accurately while the rules of length 1 are explicitly employed to create semantic associations among relations and constrain relation embeddings. Moreover, the confidence level of each rule is also considered in optimization to guarantee the availability of applying the rule to representation learning. Extensive experimental results illustrate that RPJE outperforms other state-of-the-art baselines on KG completion task, which also demonstrate the superiority of utilizing logic rules as well as paths for improving the accuracy and explainability of representation learning.

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