10,803 research outputs found
Processing Social Media Messages in Mass Emergency: A Survey
Social media platforms provide active communication channels during mass
convergence and emergency events such as disasters caused by natural hazards.
As a result, first responders, decision makers, and the public can use this
information to gain insight into the situation as it unfolds. In particular,
many social media messages communicated during emergencies convey timely,
actionable information. Processing social media messages to obtain such
information, however, involves solving multiple challenges including: handling
information overload, filtering credible information, and prioritizing
different classes of messages. These challenges can be mapped to classical
information processing operations such as filtering, classifying, ranking,
aggregating, extracting, and summarizing. We survey the state of the art
regarding computational methods to process social media messages, focusing on
their application in emergency response scenarios. We examine the
particularities of this setting, and then methodically examine a series of key
sub-problems ranging from the detection of events to the creation of actionable
and useful summaries
Analyzing Self-Driving Cars on Twitter
This paper studies users' perception regarding a controversial product,
namely self-driving (autonomous) cars. To find people's opinion regarding this
new technology, we used an annotated Twitter dataset, and extracted the topics
in positive and negative tweets using an unsupervised, probabilistic model
known as topic modeling. We later used the topics, as well as linguist and
Twitter specific features to classify the sentiment of the tweets. Regarding
the opinions, the result of our analysis shows that people are optimistic and
excited about the future technology, but at the same time they find it
dangerous and not reliable. For the classification task, we found Twitter
specific features, such as hashtags as well as linguistic features such as
emphatic words among top attributes in classifying the sentiment of the tweets
Mapping e-Scienceâs Path in the Collaboration Space: Ontological Approach to Monitoring Infrastructure Development
In an undertaking such as the U.S. Cyberinfrastructure Initiative, or the UK e-science program, which span many years and comprise a great many projects funded by multiple agencies, it can be very difficult to keep tabs on what everyone is doing. But, it is not impossible. In this paper, we propose the construction of ontologies as a means of monitoring a research programâs portfolio of projects. In particular, we introduce the âvirtual laboratory ontologyâ (VLO) and show how its application to e-Science yields a mapping of the distribution of projects in several dimensions of the âcollaboration space.â In this paper, we sketch out a method to induce a project mapping from project descriptions and present the resulting map for the UK e-science program. This paper shows the proposed mapping approach to be informative as well as feasible, and we expect that its further development can prove to be substantively useful for future work in cyber-infrastructure-building.e-Science, virtual laboratory ontology, collaboration space, project mapping, cyber-infrastructure building
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
From Affective Science to Psychiatric Disorder: Ontology as Semantic Bridge
Advances in emotion and affective science have yet to translate routinely into psychiatric research and practice. This is unfortunate since emotion and affect are fundamental components of many psychiatric conditions. Rectifying this lack of interdisciplinary integration could thus be a potential avenue for improving psychiatric diagnosis and treatment. In this contribution, we propose and discuss an ontological framework for explicitly capturing the complex interrelations between affective entities and psychiatric disorders, in order to facilitate mapping and integration between affective science and psychiatric diagnostics. We build on and enhance the categorisation of emotion, affect and mood within the previously developed Emotion Ontology, and that of psychiatric disorders in the Mental Disease Ontology. This effort further draws on developments in formal ontology regarding the distinction between normal and abnormal in order to formalize the interconnections. This operational semantic framework is relevant for applications including clarifying psychiatric diagnostic categories, clinical information systems, and the integration and translation of research results across disciplines
The state-of-the-art in web-scale semantic information processing for cloud computing
Based on integrated infrastructure of resource sharing and computing in
distributed environment, cloud computing involves the provision of dynamically
scalable and provides virtualized resources as services over the Internet.
These applications also bring a large scale heterogeneous and distributed
information which pose a great challenge in terms of the semantic ambiguity. It
is critical for application services in cloud computing environment to provide
users intelligent service and precise information. Semantic information
processing can help users deal with semantic ambiguity and information overload
efficiently through appropriate semantic models and semantic information
processing technology. The semantic information processing have been
successfully employed in many fields such as the knowledge representation,
natural language understanding, intelligent web search, etc. The purpose of
this report is to give an overview of existing technologies for semantic
information processing in cloud computing environment, to propose a research
direction for addressing distributed semantic reasoning and parallel semantic
computing by exploiting semantic information newly available in cloud computing
environment.Comment: 20 page
A Disease Diagnosis and Treatment Recommendation System Based on Big Data Mining and Cloud Computing
It is crucial to provide compatible treatment schemes for a disease according
to various symptoms at different stages. However, most classification methods
might be ineffective in accurately classifying a disease that holds the
characteristics of multiple treatment stages, various symptoms, and
multi-pathogenesis. Moreover, there are limited exchanges and cooperative
actions in disease diagnoses and treatments between different departments and
hospitals. Thus, when new diseases occur with atypical symptoms, inexperienced
doctors might have difficulty in identifying them promptly and accurately.
Therefore, to maximize the utilization of the advanced medical technology of
developed hospitals and the rich medical knowledge of experienced doctors, a
Disease Diagnosis and Treatment Recommendation System (DDTRS) is proposed in
this paper. First, to effectively identify disease symptoms more accurately, a
Density-Peaked Clustering Analysis (DPCA) algorithm is introduced for
disease-symptom clustering. In addition, association analyses on
Disease-Diagnosis (D-D) rules and Disease-Treatment (D-T) rules are conducted
by the Apriori algorithm separately. The appropriate diagnosis and treatment
schemes are recommended for patients and inexperienced doctors, even if they
are in a limited therapeutic environment. Moreover, to reach the goals of high
performance and low latency response, we implement a parallel solution for
DDTRS using the Apache Spark cloud platform. Extensive experimental results
demonstrate that the proposed DDTRS realizes disease-symptom clustering
effectively and derives disease treatment recommendations intelligently and
accurately
Exploring The Value Of Folksonomies For Creating Semantic Metadata
Finding good keywords to describe resources is an on-going problem: typically we select such words manually from a thesaurus of terms, or they are created using automatic keyword extraction techniques. Folksonomies are an increasingly well populated source of unstructured tags describing web resources. This paper explores the value of the folksonomy tags as potential source of keyword metadata by examining the relationship between folksonomies, community produced annotations, and keywords extracted by machines. The experiment has been carried-out in two ways: subjectively, by asking two human indexers to evaluate the quality of the generated keywords from both systems; and automatically, by measuring the percentage of overlap between the folksonomy set and machine generated keywords set. The results of this experiment show that the folksonomy tags agree more closely with the human generated keywords than those automatically generated. The results also showed that the trained indexers preferred the semantics of folksonomy tags compared to keywords extracted automatically. These results can be considered as evidence for the strong relationship of folksonomies to the human indexerâs mindset, demonstrating that folksonomies used in the del.icio.us bookmarking service are a potential source for generating semantic metadata to annotate web resources
Knowledge society arguments revisited in the semantic technologies era
In the light of high profile governmental and international efforts to realise the knowledge society, I review the arguments made for and against it from a technology standpoint. I focus on advanced knowledge technologies with applications on a large scale and in open- ended environments like the World Wide Web and its ambitious extension, the Semantic Web. I argue for a greater role of social networks in a knowledge society and I explore the recent developments in mechanised trust, knowledge certification, and speculate on their blending with traditional societal institutions. These form the basis of a sketched roadmap for enabling technologies for a knowledge society
Semantics-based services for a low carbon society: An application on emissions trading system data and scenarios management
A low carbon society aims at fighting global warming by stimulating synergic
efforts from governments, industry and scientific communities. Decision support
systems should be adopted to provide policy makers with possible scenarios,
options for prompt countermeasures in case of side effects on environment,
economy and society due to low carbon society policies, and also options for
information management. A necessary precondition to fulfill this agenda is to
face the complexity of this multi-disciplinary domain and to reach a common
understanding on it as a formal specification. Ontologies are widely accepted
means to share knowledge. Together with semantic rules, they enable advanced
semantic services to manage knowledge in a smarter way. Here we address the
European Emissions Trading System (EU-ETS) and we present a knowledge base
consisting of the EREON ontology and a catalogue of rules. Then we describe two
innovative semantic services to manage ETS data and information on ETS
scenarios
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