scholarly journals AI-Driven Cross-Domain Knowledge Discoveryand Hypotheses Generation for Enhanced Earth System Predictability

2021 ◽  
Author(s):  
Svitlana Volkova ◽  
Nathan Hodas ◽  
Timothy Scheibe
Keyword(s):  
Domain Knowledge ◽  
Earth System ◽  
Cross Domain ◽  
Hypotheses Generation

scholarly journals Development of Knowledge Graph for Data Management Related to Flooding Disasters Using Open Data

2021 ◽  
Vol 13 (5) ◽  
pp. 124
Author(s):  
Jiseong Son ◽  
Chul-Su Lim ◽  
Hyoung-Seop Shim ◽  
Ji-Sun Kang
Keyword(s):  
Artificial Intelligence ◽  
Domain Knowledge ◽  
Open Data ◽  
Heterogeneous Data ◽  
Big Data Analysis ◽  
Knowledge Graph ◽  
Cross Domain ◽  
Disaster Data ◽  
Knowledge Graphs ◽  
Open Datasets

Despite the development of various technologies and systems using artificial intelligence (AI) to solve problems related to disasters, difficult challenges are still being encountered. Data are the foundation to solving diverse disaster problems using AI, big data analysis, and so on. Therefore, we must focus on these various data. Disaster data depend on the domain by disaster type and include heterogeneous data and lack interoperability. In particular, in the case of open data related to disasters, there are several issues, where the source and format of data are different because various data are collected by different organizations. Moreover, the vocabularies used for each domain are inconsistent. This study proposes a knowledge graph to resolve the heterogeneity among various disaster data and provide interoperability among domains. Among disaster domains, we describe the knowledge graph for flooding disasters using Korean open datasets and cross-domain knowledge graphs. Furthermore, the proposed knowledge graph is used to assist, solve, and manage disaster problems.

Documentation of climate change data supporting cross-domain data reuse

2020 ◽  
Author(s):  
Martina Stockhause ◽  
Mark Greenslade ◽  
David Hassell ◽  
Charlotte Pascoe
Keyword(s):  
Climate Change ◽  
Data Dissemination ◽  
Coupled Model ◽  
Data Reuse ◽  
Projection Data ◽  
Earth System ◽  
Policy Makers ◽  
Data Infrastructure ◽  
External Resources ◽  
Cross Domain

<p>Climate change data and information is among those of the highest interest for cross-domain researchers, policy makers and the general public. Serving climate projection data to these diverse users requires detailed and accessible documentation.</p><p>Thus, the CMIP6 (Coupled Model Intercomparison Project Phase 6) data infrastructure consists not only of the ESGF (Earth System Grid Federation) as the data dissemination component but additionally of ES-DOC (Earth System Documentation) and the Citation Service for describing the provenance of the data. These services provide further information on the data creation process (experiments, models, …) and data reuse (data references and licenses) and connect the data to other external resources like research papers.</p><p>The contribution will present documentation of the climate change workflow around the furtherInfoURL page serving as an entry point. The challenges are to collect quality-controlled information from the international research community in different infrastructure components and to display them seamlessly alongside on the furtherInfoURL page.</p><p> </p><p><strong><em>References / Links:</em></strong></p><ul><li>CMIP6: https://pcmdi.llnl.gov/CMIP6/</li> <li>ES-DOC: https://es-doc.org/</li> <li>Citation Service: http://cmip6cite.wdc-climate.de</li> </ul>

scholarly journals Reducing the Search Space in Literature-Based Discovery by Exploring Outlier Documents: a Case Study in Finding Links Between Gut Microbiome and Alzheimer’s Disease

2017 ◽  
Vol 3 (3) ◽  
pp. 58 ◽  
Author(s):  
Bojan Cestnik ◽  
Elsa Fabbretti ◽  
Donatella Gubiani ◽  
Tanja Urbančič ◽  
Nada Lavrač
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Gut Microbiome ◽  
Domain Knowledge ◽  
Search Space ◽  
Easy Access ◽  
Literature Mining ◽  
Cross Domain ◽  
Scientific Papers ◽  
Literature Based Discovery

Literature-based discovery tools have been often used to overcome the problem of fragmentation of science and to assist researchers in their process of cross-domain knowledge discovery. In this paper we propose a methodology for cross-domain literature-based discovery that focuses on outlier documents to reduce the search space of potential cross-domain links and to improve search efficiency. In a previous study, literature mining tools OntoGen for document clustering and CrossBee for cross-domain bridging term exploration were combined to search for hidden relations in scientific papers from two different domains of interest, where the utility of the approach was demonstrated in a study involving PubMed papers about Alzheimer’s disease and gut microbiome. This paper extends the approach by proposing a methodology, implemented as a repeatable workflow in a web-based text mining platform TextFlows, which enables easy access and execution of the methodology for the interested researcher.

scholarly journals Assessing Large-Scale, Cross-Domain Knowledge Bases for Semantic Search

2020 ◽  
Vol 39 (3) ◽  
pp. 595-602
Author(s):  
Aatif Ahmad Khan ◽  
Sanjay Kumar Malik
Keyword(s):  
Domain Knowledge ◽  
Large Scale ◽  
Open Data ◽  
Knowledge Bases ◽  
Semantic Search ◽  
Semantic Web Technologies ◽  
Data Format ◽  
Web Technologies ◽  
Cross Domain ◽  
Description Framework

Semantic Search refers to set of approaches dealing with usage of Semantic Web technologies for information retrieval in order to make the process machine understandable and fetch precise results. Knowledge Bases (KB) act as the backbone for semantic search approaches to provide machine interpretable information for query processing and retrieval of results. These KB include Resource Description Framework (RDF) datasets and populated ontologies. In this paper, an assessment of the largest cross-domain KB is presented that are exploited in large scale semantic search and are freely available on Linked Open Data Cloud. Analysis of these datasets is a prerequisite for modeling effective semantic search approaches because of their suitability for particular applications. Only the large scale, cross-domain datasets are considered, which are having sizes more than 10 million RDF triples. Survey of sizes of the datasets in triples count has been depicted along with triples data format(s) supported by them, which is quite significant to develop effective semantic search models.

scholarly journals Taxonomy Construction of Unseen Domains via Graph-based Cross-Domain Knowledge Transfer

2020 ◽  
Author(s):  
Chao Shang ◽  
Sarthak Dash ◽  
Md. Faisal Mahbub Chowdhury ◽  
Nandana Mihindukulasooriya ◽  
Alfio Gliozzo
Keyword(s):  
Knowledge Transfer ◽  
Domain Knowledge ◽  
Cross Domain

A Bisociative Design Framework for Knowledge Discovery Across Seemingly Unrelated Product Domains

2012 ◽  
Author(s):  
Conrad S. Tucker ◽  
Sung Woo Kang
Keyword(s):  
Domain Knowledge ◽  
Current Data ◽  
Data Driven ◽  
Design Framework ◽  
Specific Domain ◽  
Cross Domain ◽  
Product Design Process ◽  
Product Domains ◽  
Multiple Domains ◽  
Novel Design

The Bisociative Design framework proposed in this work aims to quantify hidden, previously unknown design synergies/insights across seemingly unrelated product domains. Despite the overabundance of data characterizing the digital age, designers still face tremendous challenges in transforming data into knowledge throughout the design processes. Data driven methodologies play a significant role in the product design process ranging from customer preference modeling to detailed engineering design. Existing data driven methodologies employed in the design community generate mathematical models based on data relating to a specific domain and are therefore constrained in their ability to discover novel design insights beyond the domain itself (I.e., cross domain knowledge). The Bisociative Design framework proposed in this work overcomes the limitations of current data driven design methodologies by decomposing design artifacts into form patterns, function patterns and behavior patterns and then evaluating potential cross-domain design insights through a proposed multidimensional Bisociative Design metric. A hybrid marine model involving multiple domains (capable of flight and marine navigation) is used as a case study to demonstrate the proposed Bisociative Design framework and explain how associations and novel design models can be generated through the discovery of hidden, previously unknown patterns across multiple, unrelated domains.

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