Ryan Hartman, Josemar Faustino, Diego Pinheiro, R. Menezes
In the last two decades, we have witnessed the widespread use of structural analysis of data. The area, generally called Network Science, concentrates on understanding complex phenomena by looking for properties that emerge from the relationships between the pieces of data instead of the traditional mining of the data itself. A commonly used structural analysis in networks consists of finding subgraphs whose density of connections within the subgraph surpasses that of outside connections; called Community Detection. Many techniques have been proposed to find communities as well as benchmarks to evaluate the algorithms ability to find these substructures. Until recently, the literature has mostly neglected the fact that these communities often represent common characteristic of the elements in the community. For instance, in a social network, communities could represent: people who follow the same particular sport, people from the same classroom, authors working in the same field of study, to name a few. The problem here is one of community detection selection as a function of the ground truth provided by available meta-data. In this work, we propose the use of rank stability (entropy of ranks) to assess communities identified using different techniques from the perspective of meta-data. We validate our approach using a large-scale data set of on-line social interactions across multiple community detection techniques.
{"title":"Assessing the suitability of network community detection to available meta-data using rank stability","authors":"Ryan Hartman, Josemar Faustino, Diego Pinheiro, R. Menezes","doi":"10.1145/3106426.3106493","DOIUrl":"https://doi.org/10.1145/3106426.3106493","url":null,"abstract":"In the last two decades, we have witnessed the widespread use of structural analysis of data. The area, generally called Network Science, concentrates on understanding complex phenomena by looking for properties that emerge from the relationships between the pieces of data instead of the traditional mining of the data itself. A commonly used structural analysis in networks consists of finding subgraphs whose density of connections within the subgraph surpasses that of outside connections; called Community Detection. Many techniques have been proposed to find communities as well as benchmarks to evaluate the algorithms ability to find these substructures. Until recently, the literature has mostly neglected the fact that these communities often represent common characteristic of the elements in the community. For instance, in a social network, communities could represent: people who follow the same particular sport, people from the same classroom, authors working in the same field of study, to name a few. The problem here is one of community detection selection as a function of the ground truth provided by available meta-data. In this work, we propose the use of rank stability (entropy of ranks) to assess communities identified using different techniques from the perspective of meta-data. We validate our approach using a large-scale data set of on-line social interactions across multiple community detection techniques.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83209923","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}
In this paper the task of Information Foraging (IF) is considered as a useful paradigm to address Exploratory Search. In the context of IF, a Web navigation strategy is introduced and formalized, and a multi-agent based model is proposed to exploit a collaborative approach to Information Foraging. A system based on this model has been developed, and its evaluations on the ACM and DBLP repositories are reported. Two datasets with different sizes were considered to show the effectiveness and the efficiency of the developed system. Furthermore, comparative evaluations were conducted in order to compare our approach with classical information access approaches. The results are promising and show the ability of the proposed Web Information Foraging system to find relevant Web pages in a very short time.
{"title":"A collaborative approach to web information foraging based on multi-agent systems","authors":"Y. Drias, G. Pasi","doi":"10.1145/3106426.3106533","DOIUrl":"https://doi.org/10.1145/3106426.3106533","url":null,"abstract":"In this paper the task of Information Foraging (IF) is considered as a useful paradigm to address Exploratory Search. In the context of IF, a Web navigation strategy is introduced and formalized, and a multi-agent based model is proposed to exploit a collaborative approach to Information Foraging. A system based on this model has been developed, and its evaluations on the ACM and DBLP repositories are reported. Two datasets with different sizes were considered to show the effectiveness and the efficiency of the developed system. Furthermore, comparative evaluations were conducted in order to compare our approach with classical information access approaches. The results are promising and show the ability of the proposed Web Information Foraging system to find relevant Web pages in a very short time.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84402102","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}
Marios Belk, Andreas Pamboris, C. Fidas, C. Katsini, N. Avouris, G. Samaras
This paper investigates the trade-off between security and usability in recognition-based graphical authentication mechanisms. Through a user study (N=103) based on a real usage scenario, it draws insights about the security strength and memorability of a chosen password with respect to the amount of images presented to users during sign-up. In particular, it reveals the users' predisposition in following predictable patterns when selecting graphical passwords, and its effect on practical security strength. It also demonstrates that a "sweet-spot" exists between security and usability in graphical authentication approaches on the basis of adjusting accordingly the image grid size presented to users when creating passwords. The results of the study can be leveraged by researchers and practitioners engaged in designing intelligent graphical authentication user interfaces for striking an appropriate balance between security and usability.
{"title":"Sweet-spotting security and usability for intelligent graphical authentication mechanisms","authors":"Marios Belk, Andreas Pamboris, C. Fidas, C. Katsini, N. Avouris, G. Samaras","doi":"10.1145/3106426.3106488","DOIUrl":"https://doi.org/10.1145/3106426.3106488","url":null,"abstract":"This paper investigates the trade-off between security and usability in recognition-based graphical authentication mechanisms. Through a user study (N=103) based on a real usage scenario, it draws insights about the security strength and memorability of a chosen password with respect to the amount of images presented to users during sign-up. In particular, it reveals the users' predisposition in following predictable patterns when selecting graphical passwords, and its effect on practical security strength. It also demonstrates that a \"sweet-spot\" exists between security and usability in graphical authentication approaches on the basis of adjusting accordingly the image grid size presented to users when creating passwords. The results of the study can be leveraged by researchers and practitioners engaged in designing intelligent graphical authentication user interfaces for striking an appropriate balance between security and usability.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81827696","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}
With the deluge of data published on the web, it becomes even more difficult for a user to get access to the relevant information based on his preferences. In order to accurately predict the preference a user would give to an item, recommender systems should use an effective information filtering engine. This task can be achieved using content based filtering (CBF) or collaborative filtering or a hybrid approach. This work describes an approach to CBF that aims to deal with the issues of unstructured data and new user on which existing approaches perform poorly. The basic feature of the proposed approach is to incorporate linked data cloud into the information filtering process using a semantic space vector model. FOAF vocabulary is used to define a new distance measure between users based on their FOAF profiles. Unstructured items representations are enhanced by additional attributes extracted from Linked data cloud which alleviates the burden to analyze the content of these items and therefore reduces the computational cost. We report on some promising experiments of the proposed approach performed on MovieLens data sets.
{"title":"Improving content based recommender systems using linked data cloud and FOAF vocabulary","authors":"Hanane Zitouni, S. Meshoul, Kamel Taouche","doi":"10.1145/3106426.3120963","DOIUrl":"https://doi.org/10.1145/3106426.3120963","url":null,"abstract":"With the deluge of data published on the web, it becomes even more difficult for a user to get access to the relevant information based on his preferences. In order to accurately predict the preference a user would give to an item, recommender systems should use an effective information filtering engine. This task can be achieved using content based filtering (CBF) or collaborative filtering or a hybrid approach. This work describes an approach to CBF that aims to deal with the issues of unstructured data and new user on which existing approaches perform poorly. The basic feature of the proposed approach is to incorporate linked data cloud into the information filtering process using a semantic space vector model. FOAF vocabulary is used to define a new distance measure between users based on their FOAF profiles. Unstructured items representations are enhanced by additional attributes extracted from Linked data cloud which alleviates the burden to analyze the content of these items and therefore reduces the computational cost. We report on some promising experiments of the proposed approach performed on MovieLens data sets.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80267645","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}
Beibei Jin, Jianing Yang, Xiangsheng Huang, D. Khan
The performance of Deep Reinforcement Learning (DRL) algorithms is usually constrained by instability and variability. In this work, we present an extension of Deep Q-Network (DQN) called Deep Deformable Q-Network which is based on deformable convolution mechanisms. The new algorithm can readily be built on existing models and can be easily trained end-to-end by standard back-propagation. Extensive experiments on the Atari games validate the feasibility and effectiveness of the proposed Deep Deformable Q-Network.
{"title":"Deep deformable Q-Network: an extension of deep Q-Network","authors":"Beibei Jin, Jianing Yang, Xiangsheng Huang, D. Khan","doi":"10.1145/3106426.3109426","DOIUrl":"https://doi.org/10.1145/3106426.3109426","url":null,"abstract":"The performance of Deep Reinforcement Learning (DRL) algorithms is usually constrained by instability and variability. In this work, we present an extension of Deep Q-Network (DQN) called Deep Deformable Q-Network which is based on deformable convolution mechanisms. The new algorithm can readily be built on existing models and can be easily trained end-to-end by standard back-propagation. Extensive experiments on the Atari games validate the feasibility and effectiveness of the proposed Deep Deformable Q-Network.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85684453","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}
Tatsuya Nakamura, T. Tominaga, Miki Watanabe, Nattapong Thammasan, K. Urai, Yutaka Nakamura, Kazufumi Hosoda, T. Hara, Y. Hijikata
In this paper, we present results of investigation on the dynamics of group decision making - how people discuss and make a decision-with collaborative web search. Prior works proposed systems that support group decision making with web search but have not examined the influence of discussion behaviors especially on the satisfaction levels with the final conclusion. In this study, we conducted a set of experiments to observe discussion behaviors and the consequent satisfaction with the conclusion using our experimental system and a set of questionnaires. The task for each participant was to make a decision on a restaurant. Our primary results revealed (1) the similar activities across all groups at the beginning and the end of the group discussion, (2) a lack of correspondence between the satisfaction with the conclusion and the time spent to reach the conclusion, and (3) the presumption that a member who actively engaged in the activities that were visible for the other members was likely to be voted as a leader in the group discussion beyond the discussion. Finally, we discussed how to implement intelligent systems that aid group decision making.
{"title":"Investigation on dynamics of group decision making with collaborative web search","authors":"Tatsuya Nakamura, T. Tominaga, Miki Watanabe, Nattapong Thammasan, K. Urai, Yutaka Nakamura, Kazufumi Hosoda, T. Hara, Y. Hijikata","doi":"10.1145/3106426.3106505","DOIUrl":"https://doi.org/10.1145/3106426.3106505","url":null,"abstract":"In this paper, we present results of investigation on the dynamics of group decision making - how people discuss and make a decision-with collaborative web search. Prior works proposed systems that support group decision making with web search but have not examined the influence of discussion behaviors especially on the satisfaction levels with the final conclusion. In this study, we conducted a set of experiments to observe discussion behaviors and the consequent satisfaction with the conclusion using our experimental system and a set of questionnaires. The task for each participant was to make a decision on a restaurant. Our primary results revealed (1) the similar activities across all groups at the beginning and the end of the group discussion, (2) a lack of correspondence between the satisfaction with the conclusion and the time spent to reach the conclusion, and (3) the presumption that a member who actively engaged in the activities that were visible for the other members was likely to be voted as a leader in the group discussion beyond the discussion. Finally, we discussed how to implement intelligent systems that aid group decision making.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87941989","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}
With the rapid development of computer technology, the barriers of communication among different systems caused by system heterogeneity or data structure have been broken down. However, the demands for personalized content for accuracy in resource exchange and delivery are becoming increasingly high. The structures of existing literature resources like papers, patents and books, with different formats and structures, leading to lots of problems in content delivery and inheritance. Thus, based on the XML technology, we design and develop the data exchange system. This system supports the mapping and integration of different structures of literature resource, and parsing resources at the same time, so that users can upload and verify the XML schema files according to their individual demands for data exchange.
{"title":"Research on design and implementation of data exchange system","authors":"Wenwen Zhu, Zhijun Guo, Yonghong Cheng","doi":"10.1145/3106426.3109048","DOIUrl":"https://doi.org/10.1145/3106426.3109048","url":null,"abstract":"With the rapid development of computer technology, the barriers of communication among different systems caused by system heterogeneity or data structure have been broken down. However, the demands for personalized content for accuracy in resource exchange and delivery are becoming increasingly high. The structures of existing literature resources like papers, patents and books, with different formats and structures, leading to lots of problems in content delivery and inheritance. Thus, based on the XML technology, we design and develop the data exchange system. This system supports the mapping and integration of different structures of literature resource, and parsing resources at the same time, so that users can upload and verify the XML schema files according to their individual demands for data exchange.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86558925","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}
Víctor D. Cortés, J. D. Velásquez, Carlos F. Ibáñez
Today online social networks seem to be good tools to quickly monitor what is going on with the population, since they provide environments where users can freely share large amounts of information related to their own lives. Due to well known limitations of surveys, this novel kind of data can be used to get additional real time insights from people to understand their actual behavior related to drug use. The aim of this work is to make use of text messages (tweets) and relationships between Chilean Twitter users to predict marijuana use among them. To do this we collected Twitter accounts using a location-based criteria, and built a set of features based on tweets they made and ego centric network metrics. To get tweet-based features, tweets were filtered using marijuana-related keywords and a set of 1000 tweets were manually labeled to train algorithms capable of predicting marijuana use in tweets. In addition, a sentiment classifier of tweets was developed using the TASS corpus. Then, we made a survey to get real marijuana use labels related to accounts and these labels were used to train supervised machine learning algorithms. The marijuana use per user classifier had precision, recall and F-measure results close to 0.7, implying significant predictive power of the selected variables. We obtained a model capable of predicting marijuana use of Twitter users and estimating their opinion about marijuana. This information can be used as an efficient (fast and low cost) tool for marijuana surveillance, and support decision making about drug policies.
{"title":"Twitter for marijuana infodemiology","authors":"Víctor D. Cortés, J. D. Velásquez, Carlos F. Ibáñez","doi":"10.1145/3106426.3106541","DOIUrl":"https://doi.org/10.1145/3106426.3106541","url":null,"abstract":"Today online social networks seem to be good tools to quickly monitor what is going on with the population, since they provide environments where users can freely share large amounts of information related to their own lives. Due to well known limitations of surveys, this novel kind of data can be used to get additional real time insights from people to understand their actual behavior related to drug use. The aim of this work is to make use of text messages (tweets) and relationships between Chilean Twitter users to predict marijuana use among them. To do this we collected Twitter accounts using a location-based criteria, and built a set of features based on tweets they made and ego centric network metrics. To get tweet-based features, tweets were filtered using marijuana-related keywords and a set of 1000 tweets were manually labeled to train algorithms capable of predicting marijuana use in tweets. In addition, a sentiment classifier of tweets was developed using the TASS corpus. Then, we made a survey to get real marijuana use labels related to accounts and these labels were used to train supervised machine learning algorithms. The marijuana use per user classifier had precision, recall and F-measure results close to 0.7, implying significant predictive power of the selected variables. We obtained a model capable of predicting marijuana use of Twitter users and estimating their opinion about marijuana. This information can be used as an efficient (fast and low cost) tool for marijuana surveillance, and support decision making about drug policies.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86607113","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}
J. C. Duarte, M. C. Cavalcanti, Igor de Souza Costa, Diego Esteves
Nowadays, despite the fact that Machine Learning (ML) experiments can be easily built using several ML frameworks, as the demand for practical solutions for several kinds of scientific problems is always increasing, organizing its results and the different algorithms' setups used, in order to be able to reproduce them, is a long known problem without an easy solution. Motivated by the need of a high level of interoperability and data provenance with respect to ML experiments, this work presents a generic solution using a web-service application that interacts with the MEX vocabulary, a lightweight solution for archiving and querying ML experiments. By using this solution, researchers can share their setups and results, in a interoperable format that describes all the steps needed to reproduce their research. Although the solution presented in this work could be implemented in any programming language, we chose Java to build the web-service and also we chose to present experiments with Python's Scikit-learn ML Framework, using Decorators and Code Reflection, that demonstrates the simplicity of incorporating data provenance in such a high level, simplifying the experiment logging process.
{"title":"An interoperable service for the provenance of machine learning experiments","authors":"J. C. Duarte, M. C. Cavalcanti, Igor de Souza Costa, Diego Esteves","doi":"10.1145/3106426.3106496","DOIUrl":"https://doi.org/10.1145/3106426.3106496","url":null,"abstract":"Nowadays, despite the fact that Machine Learning (ML) experiments can be easily built using several ML frameworks, as the demand for practical solutions for several kinds of scientific problems is always increasing, organizing its results and the different algorithms' setups used, in order to be able to reproduce them, is a long known problem without an easy solution. Motivated by the need of a high level of interoperability and data provenance with respect to ML experiments, this work presents a generic solution using a web-service application that interacts with the MEX vocabulary, a lightweight solution for archiving and querying ML experiments. By using this solution, researchers can share their setups and results, in a interoperable format that describes all the steps needed to reproduce their research. Although the solution presented in this work could be implemented in any programming language, we chose Java to build the web-service and also we chose to present experiments with Python's Scikit-learn ML Framework, using Decorators and Code Reflection, that demonstrates the simplicity of incorporating data provenance in such a high level, simplifying the experiment logging process.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77395108","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}
Many Linked Open Data applications require fresh copies of RDF data at their local repositories. Since RDF documents constantly change and those changes are not automatically propagated to the LOD applications, it is important to regularly visit the RDF documents to refresh the local copies and keep them up-to-date. For this purpose, crawling strategies determine which RDF documents should be preferentially fetched. Traditional crawling strategies rely only on how an RDF document has been modified in the past. In contrast, we predict on the triple level whether a change will occur in the future. We use the weekly snapshots of the DyLDO dataset as well as the monthly snapshots of the Wikidata dataset. First, we conduct an in-depth analysis of the life span of triples in RDF documents. Through the analysis, we identify which triples are stable and which are ephemeral. We introduce different features based on the triples and apply a simple but effective linear regression model. Second, we propose a novel crawling strategy based on the linear regression model. We conduct two experimental setups where we vary the amount of available bandwidth as well as iteratively observe the quality of the local copies over time. The results demonstrate that the novel crawling strategy outperforms the state of the art in both setups.
{"title":"Keeping linked open data caches up-to-date by predicting the life-time of RDF triples","authors":"Chifumi Nishioka, A. Scherp","doi":"10.1145/3106426.3106463","DOIUrl":"https://doi.org/10.1145/3106426.3106463","url":null,"abstract":"Many Linked Open Data applications require fresh copies of RDF data at their local repositories. Since RDF documents constantly change and those changes are not automatically propagated to the LOD applications, it is important to regularly visit the RDF documents to refresh the local copies and keep them up-to-date. For this purpose, crawling strategies determine which RDF documents should be preferentially fetched. Traditional crawling strategies rely only on how an RDF document has been modified in the past. In contrast, we predict on the triple level whether a change will occur in the future. We use the weekly snapshots of the DyLDO dataset as well as the monthly snapshots of the Wikidata dataset. First, we conduct an in-depth analysis of the life span of triples in RDF documents. Through the analysis, we identify which triples are stable and which are ephemeral. We introduce different features based on the triples and apply a simple but effective linear regression model. Second, we propose a novel crawling strategy based on the linear regression model. We conduct two experimental setups where we vary the amount of available bandwidth as well as iteratively observe the quality of the local copies over time. The results demonstrate that the novel crawling strategy outperforms the state of the art in both setups.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76549299","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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