scholarly journals Semantics Discovery via Human Computation Games

Semantic Web ◽  
2013 ◽  
pp. 286-308 ◽  
Author(s):  
Jakub Šimko ◽  
Michal Tvarožek ◽  
Mária Bieliková
Keyword(s):  
Computer Games ◽  
Web Search ◽  
Human Computation ◽  
Search Term ◽  
Search Game ◽  
Search Terms ◽  
Qualitative Feedback ◽  
Novel Approach ◽  
Series Of Experiments ◽  
Relationship Network

The effective acquisition of (semantic) metadata is crucial for many present day applications. Games with a purpose address this issue by transforming computational problems into computer games. The authors present a novel approach to metadata acquisition via Little Search Game (LSG) – a competitive web search game, whose purpose is the creation of a term relationship network. From a player perspective, the goal is to reduce the number of search results returned for a given search term by adding negative search terms to a query. The authors describe specific aspects of the game’s design, including player motivation and anti-cheating issues. The authors have performed a series of experiments with Little Search Game, acquired real-world player input, gathered qualitative feedback from the players, constructed and evaluated term relationship network from the game logs and examined the types of created relationships.

scholarly journals Semantics Discovery via Human Computation Games

2011 ◽  
Vol 7 (3) ◽  
pp. 23-45 ◽  
Author(s):  
Jakub Šimko ◽  
Michal Tvarožek ◽  
Mária Bieliková
Keyword(s):  
Computer Games ◽  
Web Search ◽  
Human Computation ◽  
Search Term ◽  
Search Game ◽  
Search Terms ◽  
Qualitative Feedback ◽  
Novel Approach ◽  
Series Of Experiments ◽  
Relationship Network

The effective acquisition of (semantic) metadata is crucial for many present day applications. Games with a purpose address this issue by transforming computational problems into computer games. The authors present a novel approach to metadata acquisition via Little Search Game (LSG) – a competitive web search game, whose purpose is the creation of a term relationship network. From a player perspective, the goal is to reduce the number of search results returned for a given search term by adding negative search terms to a query. The authors describe specific aspects of the game’s design, including player motivation and anti-cheating issues. The authors have performed a series of experiments with Little Search Game, acquired real-world player input, gathered qualitative feedback from the players, constructed and evaluated term relationship network from the game logs and examined the types of created relationships.

scholarly journals Using NLP in openEHR archetypes retrieval to promote interoperability: a feasibility study in China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bo Sun ◽  
Fei Zhang ◽  
Jing Li ◽  
Yicheng Yang ◽  
Xiaolin Diao ◽  
...  
Keyword(s):  
Information Sharing ◽  
Language Processing ◽  
Medical Information ◽  
Medical Professional ◽  
Semantic Interoperability ◽  
Medical Information System ◽  
Search Term ◽  
Search Terms ◽  
Novel Approach ◽  
Test Sets

Abstract Background With the development and application of medical information system, semantic interoperability is essential for accurate and advanced health-related computing and electronic health record (EHR) information sharing. The openEHR approach can improve semantic interoperability. One key improvement of openEHR is that it allows for the use of existing archetypes. The crucial problem is how to improve the precision and resolve ambiguity in the archetype retrieval. Method Based on the query expansion technology and Word2Vec model in Nature Language Processing (NLP), we propose to find synonyms as substitutes for original search terms in archetype retrieval. Test sets in different medical professional level are used to verify the feasibility. Result Applying the approach to each original search term (n = 120) in test sets, a total of 69,348 substitutes were constructed. Precision at 5 (P@5) was improved by 0.767, on average. For the best result, the P@5 was up to 0.975. Conclusions We introduce a novel approach that using NLP technology and corpus to find synonyms as substitutes for original search terms. Compared to simply mapping the element contained in openEHR to an external dictionary, this approach could greatly improve precision and resolve ambiguity in retrieval tasks. This is helpful to promote the application of openEHR and advance EHR information sharing.

Senses of “Selfie” Around the World From Web Search Patterns Over Extended Time

2018 ◽  
pp. 249-295
Author(s):  
Shalin Hai-Jew
Keyword(s):  
Web Search ◽  
Search Term ◽  
Extended Time ◽  
Social Phenomena ◽  
Search Terms ◽  
Time Patterns ◽  
Search Patterns ◽  
The World ◽  
Technological Affordances ◽  
Google Search

When social phenomena and practices go viral, like “selfies,” they instantiate in different locations around the world in different ways based on cultural differences, technological affordances, and other factors. When people go to search for “selfie” on Google Search, they are thinking different things as well. On Google Correlate, it is possible to identify the top correlating search terms that pattern-match the time patterns for the seeding search term. Based in this big data, these search term correlates (associated over extended time) provide a sense of the “group mind” around a particular topic.

Exploring Semantic Characteristics of Socially Constructed Knowledge Repository to Optimize Web Search

2013 ◽  
pp. 250-271
Author(s):  
Dengya Zhu ◽  
Heinz Dreher
Keyword(s):  
Web Search ◽  
Experimental Results ◽  
Training Data ◽  
Search Term ◽  
Short Term ◽  
User Perspective ◽  
Knowledge Repository ◽  
Search Results ◽  
Socially Constructed ◽  
Search Terms

Short-term queries preferred by most users often result in a list of Web search results with low precision from a user perspective. The purpose of this research is to improve the relevance of Web search results via search-term disambiguation and ontological filtering of search results based on socially constructed search concepts. A Special Search Browser (SSB) is developed where semantic characteristics of the socially constructed knowledge repository are extracted to form a category-document set. kNN is employed with the extracted category-documents as training data to classify Web results. Users’ selected categories are employed to present the search results. Experimental results based on five experts’ judgments over 250 hits from Yahoo! API demonstrate that utilizing the socially constructed search concepts to categorize and filter search results can improve precision by 23.5%, from Yahoo’s 41.7% to 65.2% of SSB based on the results of five selected ambiguous search-terms.

scholarly journals TVA in the wild: Applying the theory of visual attention to game-like and less controlled experiments

2021 ◽  
Vol 3 (1) ◽  
pp. 1-46
Author(s):  
Alexander Krüger ◽  
Jan Tünnermann ◽  
Lukas Stratmann ◽  
Lucas Briese ◽  
Falko Dressler ◽  
...  
Keyword(s):  
Visual Attention ◽  
Computer Games ◽  
Driving Simulator ◽  
Formal Theory ◽  
Partial Report ◽  
Visual Encoding ◽  
In The Wild ◽  
Series Of Experiments ◽  
Attentional Weight ◽  
Sufficient Precision

Abstract As a formal theory, Bundesen’s theory of visual attention (TVA) enables the estimation of several theoretically meaningful parameters involved in attentional selection and visual encoding. As of yet, TVA has almost exclusively been used in restricted empirical scenarios such as whole and partial report and with strictly controlled stimulus material. We present a series of experiments in which we test whether the advantages of TVA can be exploited in more realistic scenarios with varying degree of stimulus control. This includes brief experimental sessions conducted on different mobile devices, computer games, and a driving simulator. Overall, six experiments demonstrate that the TVA parameters for processing capacity and attentional weight can be measured with sufficient precision in less controlled scenarios and that the results do not deviate strongly from typical laboratory results, although some systematic differences were found.

scholarly journals Infodemiology of systemic lupus erythematous using Google Trends

Lupus ◽  
2017 ◽  
Vol 26 (8) ◽  
pp. 886-889 ◽  
Author(s):  
M Radin ◽  
S Sciascia
Keyword(s):  
Big Data ◽  
Northern Hemisphere ◽  
Lupus Erythematosus ◽  
Kendall Test ◽  
Google Trends ◽  
Data Monitoring ◽  
Search Term ◽  
Systemic Lupus ◽  
Search Terms ◽  
Google Search

Objective People affected by chronic rheumatic conditions, such as systemic lupus erythematosus (SLE), frequently rely on the Internet and search engines to look for terms related to their disease and its possible causes, symptoms and treatments. ‘Infodemiology’ and ‘infoveillance’ are two recent terms created to describe a new developing approach for public health, based on Big Data monitoring and data mining. In this study, we aim to investigate trends of Internet research linked to SLE and symptoms associated with the disease, applying a Big Data monitoring approach. Methods We analysed the large amount of data generated by Google Trends, considering ‘lupus’, ‘relapse’ and ‘fatigue’ in a 10-year web-based research. Google Trends automatically normalized data for the overall number of searches, and presented them as relative search volumes, in order to compare variations of different search terms across regions and periods. The Menn–Kendall test was used to evaluate the overall seasonal trend of each search term and possible correlation between search terms. Results We observed a seasonality for Google search volumes for lupus-related terms. In the Northern hemisphere, relative search volumes for ‘lupus’ were correlated with ‘relapse’ (τ = 0.85; p = 0.019) and with fatigue (τ = 0.82; p = 0.003), whereas in the Southern hemisphere we observed a significant correlation between ‘fatigue’ and ‘relapse’ (τ = 0.85; p = 0.018). Similarly, a significant correlation between ‘fatigue’ and ‘relapse’ (τ = 0.70; p < 0.001) was seen also in the Northern hemisphere. Conclusion Despite the intrinsic limitations of this approach, Internet-acquired data might represent a real-time surveillance tool and an alert for healthcare systems in order to plan the most appropriate resources in specific moments with higher disease burden.

scholarly journals Silica-encapsulated DNA tracers for measuring aerosol distribution dynamics in real-world settings

2021 ◽  
Author(s):  
Anne M Luescher ◽  
Julian Koch ◽  
Wendelin J Stark ◽  
Robert N Grass
Keyword(s):  
Real World ◽  
Complex Dynamics ◽  
Quantitative Polymerase Chain Reaction ◽  
Cost Effective ◽  
Confined Space ◽  
Aerosol Dynamics ◽  
Novel Approach ◽  
Aerosol Distribution ◽  
Wide Range ◽  
Series Of Experiments

Aerosolized particles play a significant role in human health and environmental risk management. The global importance of aerosol-related hazards, such as the circulation of pathogens and high levels of air pollutants, have led to a surging demand for suitable surrogate tracers to investigate the complex dynamics of airborne particles in real-world scenarios. In this study, we propose a novel approach using silica particles with encapsulated DNA (SPED) as a tracing agent for measuring aerosol distribution indoors. In a series of experiments with a portable setup, SPED were successfully aerosolized, re-captured and quantified using quantitative polymerase chain reaction (qPCR). Position-dependency and ventilation effects within a confined space could be shown in a quantitative fashion achieving detection limits below 0.1 ng particles per m3 of sampled air. In conclusion, SPED show promise for a flexible, cost-effective and low-impact characterization of aerosol dynamics in a wide range of settings.

Tweeting on dementia: A snapshot of the content and sentiment of tweets associated with dementia

First Monday ◽  
2021 ◽  
Author(s):  
David Robertshaw ◽  
Ivana Babicova
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Lewy Body Dementia ◽  
Future Research ◽  
Search Term ◽  
Word Count ◽  
Emotional Tone ◽  
Search Terms ◽  
Linguistic Inquiry ◽  
World List

This study aimed to record and characterise tweets related to dementia, to investigate their content and sentiment. Data were extracted from Twitter over a period of six weeks during February and March 2019 and then analysed using Linguistic Inquiry and Word Count (LIWC) and AntWordProfiler. Using five search terms related to dementia, this study collected 860,383 tweets (more than 27 million words). Results have shown that out of all the collected tweets, 48.63 percent of tweets related to the search term ‘dementia’, 49.95 percent to ‘Alzheimer’s disease’ and the remainder related to frontotemporal dementia, Lewy Body dementia and vascular dementia. People wrote more positively and personally about the term ‘dementia’ than the other terms, and more technically regarding the term ‘Alzheimer’s disease’. All search terms had a negative emotional tone overall. Dementia and related terms are commonly discussed on Twitter. The overall negative emotional tone associated with all dementia related search terms suggests that dementia is still largely stigmatised and talked about negatively. Recommendations for future research include the development of a health world list or a dementia world list, and to consider how the results of this research inform social change interventions going forwards.

Bibliometric Analysis of Lean, Agile, and Leagile Supply Chains in Automobile Industry (1990 - 2017)

2020 ◽  
pp. 1746-1770
Author(s):  
Venkatesh Iyengar ◽  
S. Vijayakumar Bharathi
Keyword(s):  
Supply Chains ◽  
Bibliometric Analysis ◽  
Automobile Industry ◽  
Search Term ◽  
Search Results ◽  
Search Terms ◽  
Efficient Performance ◽  
Academic Publications ◽  
Data Statistics ◽  
Legal Strategies

This article describes how organizations embrace various supply chain strategies aiming at effective and efficient performance outcomes for gaining competitive advantage. The authors conducted an extensive search for academic publications on lean, agile, and leagile (hybrid) supply chains in context with automobile industries, published since year 1990 in reliable repositories such as Google Scholar, Scopus and ResearchGate. None of these papers used bibliometric analysis on the topic. This paper systematically maps, publications on lean, agile, and legal strategies in automobile industry published during 1990–2017. A five-step process is followed, namely (i) defining appropriate search terms, (ii) initial search results, (iii) refinement of search results, (iv) initial data, statistics, and (v) data analysis; adopted for inclusion of relevant documents for publication and citation analysis. Selected documents include primary search term ‘automobile' along with associated secondary terms such as ‘lean', ‘agile', ‘lean and agile', and ‘leagile' as part of the title, abstract, or keywords. The analysis finds several documents on lean or agile strategies, but only one document exists on ‘leagile' paradigm. Maximum articles are contributed on engineering subjects followed by business, management and accounting and computer sciences. Large publication and high citation counts were observed for lean from United States and Chinese authors, whereas Indian authors contributed in agile studies. This article identifies areas of current research interests discussing crucial contributions by several authors' and provides potential directions for further research investigations in the field.

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