Fine-Grained Ontology Reconstruction for Crisis Knowledge Based on Integrated Analysis of Temporal-Spatial Factors

2021 ◽  
Vol 48 (1) ◽  
pp. 24-41
Author(s):  
Xiaoyue Ma ◽  
Xue Pengzhen ◽  
Nada Matta ◽  
Qiang Chen
Keyword(s):  
Emergency Response ◽  
Temporal Relationship ◽  
Spatial Relationship ◽  
Integrated Analysis ◽  
Semantic Relationship ◽  
Real Situation ◽  
Fine Grained ◽  
Knowledge Based ◽  
Spatial Features ◽  
Dynamic Factors

Previous studies on crisis know­ledge organization mostly focused on the categorization of crisis know­ledge without regarding its dynamic trend and temporal-spatial features. In order to emphasize the dynamic factors of crisis collaboration, a fine-grained crisis know­ledge model is proposed by integrating temporal-spatial analysis based on ontology, which is one of the commonly used methods for know­ledge organization. The reconstruction of ontology-based crisis know­ledge will be implemented through three steps: analyzing temporal-spatial features of crisis know­ledge, reconstructing crisis know­ledge ontology, and verifying the temporal-spatial ontology. In the process of ontology reconstruction, the main classes and properties of the domain will be identified by investigating the crisis information resources. Meanwhile the fine-grained crisis ontology will be achieved at the level of characteristic representation of crisis know­ledge including temporal relationship, spatial relationship, and semantic relationship. Finally, we conducted case addition and system implementation to verify our crisis know­ledge model. This ontology-based know­ledge organization method theoretically optimizes the static organizational structure of crisis know­ledge, improving the flexibility of know­ledge organization and efficiency of emergency response. In practice, the proposed fine-grained ontology is supposed to be more in line with the real situation of emergency collaboration and management. Moreover, it will also provide the know­ledge base for decision-making during rescue process.

The Organization of Expert Activism: Shadow Mobilization in Two Social Movements*

2015 ◽  
Vol 20 (3) ◽  
pp. 305-323 ◽  
Author(s):  
Scott Frickel ◽  
Rebekah Torcasso ◽  
Annika Anderson
Keyword(s):  
Social Movements ◽  
Comparative Analysis ◽  
Environmental Justice ◽  
Methodological Approach ◽  
Cumulative Risk ◽  
Alternative Agriculture ◽  
Contemporary Society ◽  
Systematic Data ◽  
Fine Grained ◽  
Knowledge Based

The organization of expert activism is a problem of increasing importance for social movement organizers and scholars alike. Yet the relative invisibility of expert activists within social movements makes them difficult to systematically identify and study. This article offers two related ways forward. First, we advance a theory of “shadow mobilization” to explain the organization of expert activism in the broader context of proliferating risk and intensifying knowledge-based conflict. Second, we introduce a new methodological approach for collecting systematic data on members of this difficult-to-reach population. Findings from comparative analysis of expert activists in the environmental justice movement in Louisiana and the alternative agriculture movement in Washington reveal both important commonalities and fine-grained differences, suggesting that shadow mobilizations are strategic collective responses to cumulative risk in contemporary society.

An Efficient Content-Based Retrieval System for Large Image Database

2011 ◽  
pp. 249-276
Author(s):  
Ching-Sheng Wang ◽  
Timothy K. Shih
Keyword(s):  
Spatial Relationship ◽  
Image Database ◽  
Spatial Relations ◽  
Image Databases ◽  
Color Sensation ◽  
Spatial Features ◽  
Feedback Learning ◽  
Color Clustering ◽  
Object Similarity ◽  
Visual Interface

Content-based image retrieval has become more desirable for developing large image databases. This chapter presents an efficient method of retrieving images from an image database. This system combines color, shape and spatial features to index and measure the similarity of images. Several color spaces that are widely used in computer graphics are discussed and compared for color clustering. In addition, this chapter proposes a new automatic indexing scheme of image databases according to our clustering method and color sensation, which could be used to retrieve images efficiently. As a technical contribution, a Seed-Filling like algorithm that could extract the shape and spatial relationship feature of an image is proposed. Due to the difficulty of determining how far objects are separated, this system uses qualitative spatial relations to analyze object similarity. Also, the system is incorporated with a visual interface and a set of tools, which allows the users to express the query by specifying or sketching the images conveniently. The feedback learning mechanism enhances the precision of retrieval. The experience shows that the system is able to retrieve image information efficiently by the proposed approaches.

scholarly journals Study protocol: building an evidence base for epidemiology emergency response, a mixed-methods study

BMJ Open ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. e037326
Author(s):  
Amy Elizabeth Parry ◽  
Martyn D Kirk ◽  
David N Durrheim ◽  
Babatunde Olowokure ◽  
Tambri Housen
Keyword(s):  
Mixed Methods ◽  
Emergency Response ◽  
Evidence Base ◽  
Mixed Methods Study ◽  
Local Context ◽  
Integrated Analysis ◽  
Displaced Populations ◽  
National University ◽  
New Thinking ◽  
The Right

IntroductionDeterminants and drivers for emergencies, such as political instability, weak health systems, climate change and forcibly displaced populations, are increasing the severity, complexity and frequency of public health emergencies. As emergencies become more complex, it is increasingly important that the required skillset of the emergency response workforce is clearly defined. To enable essential epidemiological activities to be implemented and managed during an emergency, a workforce is required with the right mix of skills, knowledge, experience and local context awareness. This study aims to provide local and international responders with an opportunity to actively contribute to the development of new thinking around emergency response roles and required competencies. In this study, we will develop recommendations using a broad range of evidence to address identified lessons and challenges so that future major emergency responses are culturally and contextually appropriate, and less reliant on long-term international deployments.Method and analysisWe will conduct a mixed-methods study using an exploratory sequential study design. The integration of four data sources, including key informant interviews, a scoping literature review, survey and semistructured interviews will allow the research questions to be examined in a flexible, semistructured way, from a range of perspectives. The study is unequally weighted, with a qualitative emphasis. We will analyse all activities as individual components, and then together in an integrated analysis. Thematic analysis will be conducted in NVivo V.11 and quantitative analysis will be conducted in Stata V.15.Ethics and disseminationAll activities have been approved by the Science and Medical Delegated Ethics Review Committee at the Australian National University (protocol numbers 2018–521, 2018–641, 2019–068). Findings will be disseminated through international and local deployment partners, peer-reviewed publication, presentation at international conferences and through social media such as Twitter and Facebook.

scholarly journals OpenKG Chain: A Blockchain Infrastructure for Open Knowledge Graphs

2021 ◽  
pp. 1-18
Author(s):  
Huajun Chen ◽  
Ning Hu ◽  
Guilin Qi ◽  
Haofen Wang ◽  
Zhen Bi ◽  
...  
Keyword(s):  
Open Data ◽  
Knowledge Bases ◽  
Knowledge Graph ◽  
Cumulative Number ◽  
Trust Network ◽  
Fine Grained ◽  
Knowledge Based ◽  
Granularity Level ◽  
Knowledge Graphs ◽  
Structured Graphs

Abstract The early concept of knowledge graph originates from the idea of the Semantic Web, which aims at using structured graphs to model the knowledge of the world and record the relationships that exist between things. Currently publishing knowledge bases as open data on the Web has gained significant attention. In China, CIPS(Chinese Information Processing Society) launched the OpenKG in 2015 to foster the development of Chinese Open Knowledge Graphs. Unlike existing open knowledge-based programs, OpenKG chain is envisioned as a blockchain-based open knowledge infrastructure. This article introduces the first attempt at the implementation of sharing knowledge graphs on OpenKG chain, a blockchain-based trust network. We have completed the test of the underlying blockchain platform, as well as the on-chain test of OpenKG's dataset and toolset sharing as well as fine-grained knowledge crowdsourcing at the triple level. We have also proposed novel definitions: K-Point and OpenKG Token, which can be considered as a measurement of knowledge value and user value. 1033 knowledge contributors have been involved in two months of testing on the blockchain, and the cumulative number of on-chain recordings triggered by real knowledge consumers has reached 550,000 with an average daily peak value of more than 10,000. For the first time, We have tested and realized on-chain sharing of knowledge at entity/triple granularity level. At present, all operations on the datasets and toolset in OpenKG.CN, as well as the triplets in OpenBase, are recorded on the chain, and corresponding value will also be generated and assigned in a trusted mode. Via this effort, OpenKG chain looks to provide a more credible and traceable knowledge-sharing platform for the knowledge graph community.

scholarly journals Knowledge-based classification of fine-grained immune cell types in single-cell RNA-Seq data with ImmClassifier

2020 ◽  
Author(s):  
Xuan Liu ◽  
Sara J.C. Gosline ◽  
Lance T. Pflieger ◽  
Pierre Wallet ◽  
Archana Iyer ◽  
...  
Keyword(s):  
T Cells ◽  
Single Cell ◽  
Immune Cells ◽  
Immune Cell ◽  
Cell Types ◽  
Effector Memory ◽  
Immune Cell Population ◽  
Fine Grained ◽  
Knowledge Based ◽  
Comparable Performance

AbstractSingle-cell RNA sequencing is an emerging strategy for characterizing the immune cell population in diverse environments including blood, tumor or healthy tissues. While this has traditionally been done with flow or mass cytometry targeting protein expression, scRNA-Seq has several established and potential advantages in that it can profile immune cells and non-immune cells (e.g. cancer cells) in the same sample, identify cell types that lack precise markers for flow cytometry, or identify a potentially larger number of immune cell types and activation states than is achievable in a single flow assay. However, scRNA-Seq is currently limited due to the need to identify the types of each immune cell from its transcriptional profile, which is not only time-consuming but also requires a significant knowledge of immunology. While recently developed algorithms accurately annotate coarse cell types (e.g. T cells vs macrophages), making fine distinctions has turned out to be a difficult challenge. To address this, we developed a machine learning classifier called ImmClassifier that leverages a hierarchical ontology of cell type. We demonstrate that ImmClassifier outperforms other tools (+20% recall, +14% precision) in distinguishing fine-grained cell types (e.g. CD8+ effector memory T cells) with comparable performance on coarse ones. Thus, ImmClassifier can be used to explore more deeply the heterogeneity of the immune system in scRNA-Seq experiments.

scholarly journals From Word To Sense Embeddings: A Survey on Vector Representations of Meaning

2018 ◽  
Vol 63 ◽  
pp. 743-788 ◽  
Author(s):  
Jose Camacho-Collados ◽  
Mohammad Taher Pilehvar
Keyword(s):  
Theoretical Background ◽  
Comprehensive Overview ◽  
Lexical Meaning ◽  
Fine Grained ◽  
Knowledge Based ◽  
Evaluation Procedures ◽  
Word Level ◽  
Wide Range ◽  
Sense Representation ◽  
Vector Representations

Over the past years, distributed semantic representations have proved to be effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey focuses on the representation of meaning. We start from the theoretical background behind word vector space models and highlight one of their major limitations: the meaning conflation deficiency, which arises from representing a word with all its possible meanings as a single vector. Then, we explain how this deficiency can be addressed through a transition from the word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and applications for this type of representation, and provides an analysis of four of its important aspects: interpretability, sense granularity, adaptability to different domains and compositionality.

scholarly journals Deep Multi-Modal Metric Learning with Multi-Scale Correlation for Image-Text Retrieval

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 466 ◽  
Author(s):  
Yan Hua ◽  
Yingyun Yang ◽  
Jianhe Du
Keyword(s):  
State Of The Art ◽  
Metric Learning ◽  
Heterogeneous Data ◽  
Semantic Relationship ◽  
Modal Data ◽  
Fine Grained ◽  
Semantic Correlation ◽  
Multi Scale ◽  
Deep Model ◽  
Relationship Of

Multi-modal retrieval is a challenge due to heterogeneous gap and a complex semantic relationship between different modal data. Typical research map different modalities into a common subspace with a one-to-one correspondence or similarity/dissimilarity relationship of inter-modal data, in which the distances of heterogeneous data can be compared directly; thus, inter-modal retrieval can be achieved by the nearest neighboring search. However, most of them ignore intra-modal relations and complicated semantics between multi-modal data. In this paper, we propose a deep multi-modal metric learning method with multi-scale semantic correlation to deal with the retrieval tasks between image and text modalities. A deep model with two branches is designed to nonlinearly map raw heterogeneous data into comparable representations. In contrast to binary similarity, we formulate semantic relationship with multi-scale similarity to learn fine-grained multi-modal distances. Inter-modal and intra-modal correlations constructed on multi-scale semantic similarity are incorporated to train the deep model in an end-to-end way. Experiments validate the effectiveness of our proposed method on multi-modal retrieval tasks, and our method outperforms state-of-the-art methods on NUS-WIDE, MIR Flickr, and Wikipedia datasets.

Spatial Pattern Mining for Soil Erosion Characterization

2012 ◽  
pp. 190-210
Author(s):  
Nazha Selmaoui-Folcher ◽  
Frédéric Flouvat ◽  
Dominique Gay ◽  
Isabelle Rouet
Keyword(s):  
Soil Erosion ◽  
Pattern Mining ◽  
New Caledonia ◽  
Regional Scale ◽  
Strong Impact ◽  
Interestingness Measure ◽  
Knowledge Based ◽  
Spatial Features ◽  
Spatial Approach ◽  
Complexity Assessment

The protection and the maintenance of the exceptional environment of New Caledonia are major goals for this territory. Among environmental problems, erosion has a strong impact on terrestrial and coastal ecosystems. However, due to the volume of data and its complexity, assessment of hazard at a regional scale is time-consuming, costly and rarely updated. Therefore, understanding and predicting environmental phenomenons need advanced techniques of analysis and modelization. In order to improve the understanding of the erosion phenomenon, this paper proposes a spatial approach based on co-location mining and GIS. Considering a set of Boolean spatial features, the goal of co-location mining is to find subsets of features often located together. This system provides useful and interpretable knowledge based on a new interestingness measure for co-locations and a new visualization of the discovered knowledge. The interestingness measure better reflects the importance of a co-location for the experts, and is completely integrated in the mining process. The visualization approach is a simple, concise and intuitive representation of the co-locations that takes into consideration the spatial nature of the underlying objects and the experts practice.

scholarly journals A Survey on Aspect-Based Sentiment Classification

2021 ◽  
Author(s):  
Gianni Brauwers ◽  
Flavius Frasincar
Keyword(s):  
State Of The Art ◽  
Knowledge Bases ◽  
Sentiment Classification ◽  
Automatic Extraction ◽  
Learning Models ◽  
Text Documents ◽  
Fine Grained ◽  
Knowledge Based ◽  
Transformer Model ◽  
The Web

With the constantly growing number of reviews and other sentiment-bearing texts on the Web, the demand for automatic sentiment analysis algorithms continues to expand. Aspect-based sentiment classification (ABSC) allows for the automatic extraction of highly fine-grained sentiment information from text documents or sentences. In this survey, the rapidly evolving state of the research on ABSC is reviewed. A novel taxonomy is proposed that categorizes the ABSC models into three major categories: knowledge-based, machine learning, and hybrid models. This taxonomy is accompanied with summarizing overviews of the reported model performances, and both technical and intuitive explanations of the various ABSC models. State-of-the-art ABSC models are discussed, such as models based on the transformer model, and hybrid deep learning models that incorporate knowledge bases. Additionally, various techniques for representing the model inputs and evaluating the model outputs are reviewed. Furthermore, trends in the research on ABSC are identified and a discussion is provided on the ways in which the field of ABSC can be advanced in the future.

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