Pub Date : 2019-09-17DOI: 10.5220/0008353003250332
Conor McCrossan, O. Molloy
In order to measure the effectiveness of Ocean Literacy (OL) tools we can measure people’s knowledge of, and attitude and behaviour towards, specific ocean-related topics, both before and after their use of the tool. The research described in this paper aims at development of more accurate, focused survey tools. In particular we are interested in ensuring that we can accurately assess knowledge on specific topics, rather than assessing broad ocean literacy levels. Surveys were created to measure the levels of knowledge, attitude, and behaviour of university students. The topics which the surveys focused on were micro-plastics, coastal tourism, and sustainable fisheries. The knowledge, attitude, and behaviour questions in the surveys are based on work carried out as part of the H2020 ResponSEAble project on Ocean Literacy. The results show that while the students have a high level of pro-ocean-environmental attitude, their existing behaviour is low to medium, and their future intended behaviour is at a higher level than their existing behaviour. The findings provide useful pointers on how to improve both the ocean literacy tools (no statistically significant correlation between knowledge and either attitude or behaviour) as well as the design of the survey and questions themselves.
{"title":"Measuring Individuals' Knowledge, Attitude and Behaviour on Specific Ocean Related Topics","authors":"Conor McCrossan, O. Molloy","doi":"10.5220/0008353003250332","DOIUrl":"https://doi.org/10.5220/0008353003250332","url":null,"abstract":"In order to measure the effectiveness of Ocean Literacy (OL) tools we can measure people’s knowledge of, and attitude and behaviour towards, specific ocean-related topics, both before and after their use of the tool. The research described in this paper aims at development of more accurate, focused survey tools. In particular we are interested in ensuring that we can accurately assess knowledge on specific topics, rather than assessing broad ocean literacy levels. Surveys were created to measure the levels of knowledge, attitude, and behaviour of university students. The topics which the surveys focused on were micro-plastics, coastal tourism, and sustainable fisheries. The knowledge, attitude, and behaviour questions in the surveys are based on work carried out as part of the H2020 ResponSEAble project on Ocean Literacy. The results show that while the students have a high level of pro-ocean-environmental attitude, their existing behaviour is low to medium, and their future intended behaviour is at a higher level than their existing behaviour. The findings provide useful pointers on how to improve both the ocean literacy tools (no statistically significant correlation between knowledge and either attitude or behaviour) as well as the design of the survey and questions themselves.","PeriodicalId":133533,"journal":{"name":"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129484342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-17DOI: 10.5220/0007946501590166
Harri Ruoslahti, Ilkka Tikanmäki
European authorities collaborate as a community toward a coherent approach of situational understanding and open trust base information sharing. Innovation in multi-stakeholder collaboration networks involve complex collaboration between user community members, providing cross-sector, cross-border and cross-authority interaction and information sharing for collaborative situation awareness, and cooperation to increase safety and security. This study analyses data consisting of elements of use cases, collected from EU funded innovation projects. These were placed in a table based on similarity, difference and relevance to produce a classification. The results of this study indicate that use cases and scenarios engage end-users to co-create very practical descriptions providing input communication for innovation projects; also multi-actor projects are complex networks thus, this study contributes to the network approach of innovation. The implications of this study are that reaching faster innovation can be facilitated by leading and organising projects well, providing appropriate feedback to ensure project plans and results stay connected with project goals, fostering project continuums, and having e.g. higher education institutions bring problems as project ideas. The results, innovations, and feedback from research and innovation projects can benefit the European society.
{"title":"Complex Authority Network Interactions in the Common Information Sharing Environment","authors":"Harri Ruoslahti, Ilkka Tikanmäki","doi":"10.5220/0007946501590166","DOIUrl":"https://doi.org/10.5220/0007946501590166","url":null,"abstract":"European authorities collaborate as a community toward a coherent approach of situational understanding and open trust base information sharing. Innovation in multi-stakeholder collaboration networks involve complex collaboration between user community members, providing cross-sector, cross-border and cross-authority interaction and information sharing for collaborative situation awareness, and cooperation to increase safety and security. This study analyses data consisting of elements of use cases, collected from EU funded innovation projects. These were placed in a table based on similarity, difference and relevance to produce a classification. The results of this study indicate that use cases and scenarios engage end-users to co-create very practical descriptions providing input communication for innovation projects; also multi-actor projects are complex networks thus, this study contributes to the network approach of innovation. The implications of this study are that reaching faster innovation can be facilitated by leading and organising projects well, providing appropriate feedback to ensure project plans and results stay connected with project goals, fostering project continuums, and having e.g. higher education institutions bring problems as project ideas. The results, innovations, and feedback from research and innovation projects can benefit the European society.","PeriodicalId":133533,"journal":{"name":"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129603037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-03-07DOI: 10.5220/0009980100290039
Yiming Xu, Dnyanesh G. Rajpathak, Ian Gibbs, D. Klabjan
Ontology learning is a critical task in industry, dealing with identifying and extracting concepts captured in text data such that these concepts can be used in different tasks, e.g. information retrieval. Ontology learning is non-trivial due to several reasons with limited amount of prior research work that automatically learns a domain specific ontology from data. In our work, we propose a two-stage classification system to automatically learn an ontology from unstructured text data. We first collect candidate concepts, which are classified into concepts and irrelevant collocates by our first classifier. The concepts from the first classifier are further classified by the second classifier into different concept types. The proposed system is deployed as a prototype at a company and its performance is validated by using complaint and repair verbatim data collected in automotive industry from different data sources.
{"title":"Automatic Ontology Learning from Domain-Specific Short Unstructured Text Data","authors":"Yiming Xu, Dnyanesh G. Rajpathak, Ian Gibbs, D. Klabjan","doi":"10.5220/0009980100290039","DOIUrl":"https://doi.org/10.5220/0009980100290039","url":null,"abstract":"Ontology learning is a critical task in industry, dealing with identifying and extracting concepts captured in text data such that these concepts can be used in different tasks, e.g. information retrieval. Ontology learning is non-trivial due to several reasons with limited amount of prior research work that automatically learns a domain specific ontology from data. In our work, we propose a two-stage classification system to automatically learn an ontology from unstructured text data. We first collect candidate concepts, which are classified into concepts and irrelevant collocates by our first classifier. The concepts from the first classifier are further classified by the second classifier into different concept types. The proposed system is deployed as a prototype at a company and its performance is validated by using complaint and repair verbatim data collected in automotive industry from different data sources.","PeriodicalId":133533,"journal":{"name":"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116303299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-18DOI: 10.1007/978-3-030-49559-6_12
Nelson N. Tenório, Danieli Pinto, Mariana Oliveira, Flávio Bortolozzi, N. Matta
{"title":"Investigating Knowledge Management Within Small and Medium-Sized Companies: The Proof of Concept Results of a Survey Addressed to Software Development Industry","authors":"Nelson N. Tenório, Danieli Pinto, Mariana Oliveira, Flávio Bortolozzi, N. Matta","doi":"10.1007/978-3-030-49559-6_12","DOIUrl":"https://doi.org/10.1007/978-3-030-49559-6_12","url":null,"abstract":"","PeriodicalId":133533,"journal":{"name":"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114262427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-18DOI: 10.1007/978-3-030-49559-6_3
N. Guimarães, Á. Figueira, L. Torgo
{"title":"Analysis and Detection of Unreliable Users in Twitter: Two Case Studies","authors":"N. Guimarães, Á. Figueira, L. Torgo","doi":"10.1007/978-3-030-49559-6_3","DOIUrl":"https://doi.org/10.1007/978-3-030-49559-6_3","url":null,"abstract":"","PeriodicalId":133533,"journal":{"name":"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115199844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-18DOI: 10.1007/978-3-030-49559-6_5
Wei Chen, L. Kloul
{"title":"An Advanced Driver Assistance Test Cases Generation Methodology Based on Highway Traffic Situation Description Ontologies","authors":"Wei Chen, L. Kloul","doi":"10.1007/978-3-030-49559-6_5","DOIUrl":"https://doi.org/10.1007/978-3-030-49559-6_5","url":null,"abstract":"","PeriodicalId":133533,"journal":{"name":"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116919847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-18DOI: 10.1007/978-3-030-49559-6_9
Ziwei Xu, M. Harzallah, F. Guillet, R. Ichise
{"title":"Towards a Term Clustering Framework for Modular Ontology Learning","authors":"Ziwei Xu, M. Harzallah, F. Guillet, R. Ichise","doi":"10.1007/978-3-030-49559-6_9","DOIUrl":"https://doi.org/10.1007/978-3-030-49559-6_9","url":null,"abstract":"","PeriodicalId":133533,"journal":{"name":"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114417677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-18DOI: 10.1007/978-3-030-49559-6_11
Ahmad Issa Alaa Aldine, M. Harzallah, G. Berio, Nicolas Béchet, Ahmad Faour
{"title":"DHPs: Dependency Hearst's Patterns for Hypernym Relation Extraction","authors":"Ahmad Issa Alaa Aldine, M. Harzallah, G. Berio, Nicolas Béchet, Ahmad Faour","doi":"10.1007/978-3-030-49559-6_11","DOIUrl":"https://doi.org/10.1007/978-3-030-49559-6_11","url":null,"abstract":"","PeriodicalId":133533,"journal":{"name":"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","volume":"213 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131507241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-18DOI: 10.1007/978-3-030-49559-6_2
Rabaa Alabdulrahman, H. Viktor, E. Paquet
{"title":"HCC-Learn Framework for Hybrid Learning in Recommender Systems","authors":"Rabaa Alabdulrahman, H. Viktor, E. Paquet","doi":"10.1007/978-3-030-49559-6_2","DOIUrl":"https://doi.org/10.1007/978-3-030-49559-6_2","url":null,"abstract":"","PeriodicalId":133533,"journal":{"name":"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128275549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-09-18DOI: 10.1007/978-3-030-49559-6_1
Nawal Almutairi, Frans Coenen, Keith Dures
{"title":"Secure Outsourced kNN Data Classification over Encrypted Data Using Secure Chain Distance Matrices","authors":"Nawal Almutairi, Frans Coenen, Keith Dures","doi":"10.1007/978-3-030-49559-6_1","DOIUrl":"https://doi.org/10.1007/978-3-030-49559-6_1","url":null,"abstract":"","PeriodicalId":133533,"journal":{"name":"International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114521712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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