Timofey Ermilov, Diego Moussallem, Ricardo Usbeck, A. N. Ngomo
The availability of billions of facts represented in RDF on the Web provides novel opportunities for data discovery and access. In particular, keyword search and question answering approaches enable even lay people to access this data. However, the interpretation of the results of these systems, as well as the navigation through these results, remains challenging. In this paper, we present Genesis, a generic RDF data access interface. Genesis can be deployed on top of any knowledge base and search engine with minimal effort and allows for the representation of RDF data in a layperson-friendly way. This is facilitated by the modular architecture for reusable components underlying our framework. Currently, these include a generic search back-end, together with corresponding interactive user interface components based on a service for similar and related entities as well as verbalization services to bridge between RDF and natural language.
{"title":"GENESIS: a generic RDF data access interface","authors":"Timofey Ermilov, Diego Moussallem, Ricardo Usbeck, A. N. Ngomo","doi":"10.1145/3106426.3106514","DOIUrl":"https://doi.org/10.1145/3106426.3106514","url":null,"abstract":"The availability of billions of facts represented in RDF on the Web provides novel opportunities for data discovery and access. In particular, keyword search and question answering approaches enable even lay people to access this data. However, the interpretation of the results of these systems, as well as the navigation through these results, remains challenging. In this paper, we present Genesis, a generic RDF data access interface. Genesis can be deployed on top of any knowledge base and search engine with minimal effort and allows for the representation of RDF data in a layperson-friendly way. This is facilitated by the modular architecture for reusable components underlying our framework. Currently, these include a generic search back-end, together with corresponding interactive user interface components based on a service for similar and related entities as well as verbalization services to bridge between RDF and natural language.","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":"89868675","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}
When looking for recently published scientific papers, a researcher usually focuses on the topics related to her/his scientific interests. The task of a recommender system is to provide a list of unseen papers that match these topics. The core idea of this paper is to leverage the latent topics of interest in the publications of the researchers, and to take advantage of the social structure of the researchers (relations among researchers in the same field) as reliable sources of knowledge to improve the recommendation effectiveness. In particular, we introduce a hybrid approach to the task of scientific papers recommendation, which combines content analysis based on probabilistic topic modeling and ideas from collaborative filtering based on a relevance-based language model. We conducted an experimental study on DBLP, which demonstrates that our approach is promising.
{"title":"A graph based approach to scientific paper recommendation","authors":"M. Amami, R. Faiz, Fabio Stella, G. Pasi","doi":"10.1145/3106426.3106479","DOIUrl":"https://doi.org/10.1145/3106426.3106479","url":null,"abstract":"When looking for recently published scientific papers, a researcher usually focuses on the topics related to her/his scientific interests. The task of a recommender system is to provide a list of unseen papers that match these topics. The core idea of this paper is to leverage the latent topics of interest in the publications of the researchers, and to take advantage of the social structure of the researchers (relations among researchers in the same field) as reliable sources of knowledge to improve the recommendation effectiveness. In particular, we introduce a hybrid approach to the task of scientific papers recommendation, which combines content analysis based on probabilistic topic modeling and ideas from collaborative filtering based on a relevance-based language model. We conducted an experimental study on DBLP, which demonstrates that our approach is promising.","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":"89608690","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}
Government 2.0 activities have become very attractive and popular these days. Using platforms to support the activities, anyone can anytime report issues or complaints in a city with their photographs and geographical information on the Web, and share them with other people. Since a variety of reports are posted, officials in the city management section have to check the importance of each report and sort out their priorities to the reports. However, it is not easy task to judge the importance of the reports. When several officials work on the task, the agreement rate of their judgments is not always high. Even if the task is done by only one official, his/her judgment sometimes varies on a similar report. To remedy this low agreement rate problem of human judgments, we propose a method of detecting signs of danger or unsafe problems described in citizens' reports. The proposed method uses a machine learning technique with word feature selection. Experimental results clearly explain the low agreement rate of human judgments, and illustrate that the proposed machine learning method has much higher performance than human judgments.
{"title":"Machine learning is better than human to satisfy decision by majority","authors":"S. Hirokawa, Takahiko Suzuki, Tsunenori Mine","doi":"10.1145/3106426.3106520","DOIUrl":"https://doi.org/10.1145/3106426.3106520","url":null,"abstract":"Government 2.0 activities have become very attractive and popular these days. Using platforms to support the activities, anyone can anytime report issues or complaints in a city with their photographs and geographical information on the Web, and share them with other people. Since a variety of reports are posted, officials in the city management section have to check the importance of each report and sort out their priorities to the reports. However, it is not easy task to judge the importance of the reports. When several officials work on the task, the agreement rate of their judgments is not always high. Even if the task is done by only one official, his/her judgment sometimes varies on a similar report. To remedy this low agreement rate problem of human judgments, we propose a method of detecting signs of danger or unsafe problems described in citizens' reports. The proposed method uses a machine learning technique with word feature selection. Experimental results clearly explain the low agreement rate of human judgments, and illustrate that the proposed machine learning method has much higher performance than human judgments.","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":"78082652","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}
Nowadays, User Generated Content is the main source of real time news and opinions on the world happenings. Social Media, which serves as an environment for the creation and spreading of User Generated Content, is, therefore, representative of our culture and constitutes a potential treasury of knowledge. In this paper we propose a fully automatic approach for modeling and tracking the information evolution in Social Media. In particular, we propose to model a Social Media stream as a text graph. A graph degeneracy technique is used to identify the temporal sequence of the core units of information streams represented by graphs. Furthermore, as the major novelty of this work, we propose a set of measures to track and evaluate the evolution of information in time. An experimental evaluation on the crawled datasets from one of the most popular Social Media platforms proves the validity and applicability of the proposed approach.
{"title":"Information evolution modeling and tracking in social media","authors":"E. Shabunina, G. Pasi","doi":"10.1145/3106426.3106443","DOIUrl":"https://doi.org/10.1145/3106426.3106443","url":null,"abstract":"Nowadays, User Generated Content is the main source of real time news and opinions on the world happenings. Social Media, which serves as an environment for the creation and spreading of User Generated Content, is, therefore, representative of our culture and constitutes a potential treasury of knowledge. In this paper we propose a fully automatic approach for modeling and tracking the information evolution in Social Media. In particular, we propose to model a Social Media stream as a text graph. A graph degeneracy technique is used to identify the temporal sequence of the core units of information streams represented by graphs. Furthermore, as the major novelty of this work, we propose a set of measures to track and evaluate the evolution of information in time. An experimental evaluation on the crawled datasets from one of the most popular Social Media platforms proves the validity and applicability of the proposed approach.","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":"75170778","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}
David Ben Yosef, L. Dery, S. Obraztsova, Zinovi Rabinovich, M. Bannikova
Voting is a common way to reach a group decision. When possible, voters will attempt to vote strategically, in order to optimize their satisfaction from the outcome. Previous research has modelled how rational voter agents (bots) vote to maximize their personal utility in an iterative voting process that has a deadline (a timeout). However, it remains an open question whether human beings behave rationally when faced with the same settings. The focus of this paper is therefore to examine how the deadline factor affects manipulative behavior in real-world scenarios were humans are required to reach a decision before a deadline. An On-line platform was built to enable voting games by all types of users: agents (bots), humans, and mixed games with both humans and agents. We compare the results of human behavior and bot behavior and conclude that it might be wise to allow bots to make (certain) decisions on our behalf.
{"title":"Haste makes waste: a case to favour voting bots","authors":"David Ben Yosef, L. Dery, S. Obraztsova, Zinovi Rabinovich, M. Bannikova","doi":"10.1145/3106426.3106532","DOIUrl":"https://doi.org/10.1145/3106426.3106532","url":null,"abstract":"Voting is a common way to reach a group decision. When possible, voters will attempt to vote strategically, in order to optimize their satisfaction from the outcome. Previous research has modelled how rational voter agents (bots) vote to maximize their personal utility in an iterative voting process that has a deadline (a timeout). However, it remains an open question whether human beings behave rationally when faced with the same settings. The focus of this paper is therefore to examine how the deadline factor affects manipulative behavior in real-world scenarios were humans are required to reach a decision before a deadline. An On-line platform was built to enable voting games by all types of users: agents (bots), humans, and mixed games with both humans and agents. We compare the results of human behavior and bot behavior and conclude that it might be wise to allow bots to make (certain) decisions on our behalf.","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":"72972294","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}
A. Najjar, Yazan Mualla, O. Boissier, Gauthier Picard
As more interactive and multimedia-rich applications are migrating to the cloud, end-user satisfaction and her Quality of Experience (QoE) will become a determinant factor to secure success for any Software as a Service (SaaS) provider. Yet, in order to survive in this competitive market, SaaS providers also need to maximize their Quality of Business (QoBiz) and minimize costs paid to cloud providers. However, most of the existing works in the literature adopt a provider-centric approach where the end-user preferences are overlooked. In this article, we propose the AQUAMan mechanism that gives the provider a fine-grained QoE-driven control over the service acceptability rate while taking into account both end-users' satisfaction and provider's QoBiz. The proposed solution is implemented using a multi-agent simulation environment. The results show that the SaaS provider is capable of attaining the predefined acceptability rate while respecting the imposed average cost per user. Furthermore, the results help the SaaS provider identify the limits of the adaptation mechanism and estimate the best average cost to be invested per user.
{"title":"AQUAMan: QoE-driven cost-aware mechanism for SaaS acceptability rate adaptation","authors":"A. Najjar, Yazan Mualla, O. Boissier, Gauthier Picard","doi":"10.1145/3106426.3106485","DOIUrl":"https://doi.org/10.1145/3106426.3106485","url":null,"abstract":"As more interactive and multimedia-rich applications are migrating to the cloud, end-user satisfaction and her Quality of Experience (QoE) will become a determinant factor to secure success for any Software as a Service (SaaS) provider. Yet, in order to survive in this competitive market, SaaS providers also need to maximize their Quality of Business (QoBiz) and minimize costs paid to cloud providers. However, most of the existing works in the literature adopt a provider-centric approach where the end-user preferences are overlooked. In this article, we propose the AQUAMan mechanism that gives the provider a fine-grained QoE-driven control over the service acceptability rate while taking into account both end-users' satisfaction and provider's QoBiz. The proposed solution is implemented using a multi-agent simulation environment. The results show that the SaaS provider is capable of attaining the predefined acceptability rate while respecting the imposed average cost per user. Furthermore, the results help the SaaS provider identify the limits of the adaptation mechanism and estimate the best average cost to be invested per user.","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":"81680932","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}
Although there have been tremendous gains in network communication reliability, many real world applications of distributed systems still face message loss, limitations, delay, and corruption. Yet despite this fact, most Distributed Constraint Satisfaction (DCSP) protocols assume that communication is perfect (messages that are sent will be received) although not ideal (not in a timely manner). As a result, many protocols are designed to exploit this assumption and are severely impacted when applied to real world conditions. This study compares the performance of several leading DCSP protocols including the Distributed Stochastic Algorithm (DSA), Distributed Breakout Algorithm (DBA), Max-Gain Message (MGM) and Distributed Probabilistic Protocol (DPP) to analyse their behaviour in communication degraded environments. The analysis begins by comparing the performance of all of the protocols in a perfect communication environment. We then use a simulated communication degraded environment where messages are probabilistically lost. Finally, we compare their performance by limiting the communication rate, which introduces delay. We show that DBA, once modified with a message timeout, is quite resistant to high message loss while DPP and DSA converge slower onto worse solutions. Our results also show that the setting of timeout value for DBA and MGM is an important factor in the convergence of these algorithms. Under conditions of message delay, DPP and DSA are less affected than DBA and MGM. Overall, DPP and DSA cause considerably less network load.
{"title":"Solving DCSP problems in highly degraded communication environments","authors":"Saeid Samadidana, R. Mailler","doi":"10.1145/3106426.3106445","DOIUrl":"https://doi.org/10.1145/3106426.3106445","url":null,"abstract":"Although there have been tremendous gains in network communication reliability, many real world applications of distributed systems still face message loss, limitations, delay, and corruption. Yet despite this fact, most Distributed Constraint Satisfaction (DCSP) protocols assume that communication is perfect (messages that are sent will be received) although not ideal (not in a timely manner). As a result, many protocols are designed to exploit this assumption and are severely impacted when applied to real world conditions. This study compares the performance of several leading DCSP protocols including the Distributed Stochastic Algorithm (DSA), Distributed Breakout Algorithm (DBA), Max-Gain Message (MGM) and Distributed Probabilistic Protocol (DPP) to analyse their behaviour in communication degraded environments. The analysis begins by comparing the performance of all of the protocols in a perfect communication environment. We then use a simulated communication degraded environment where messages are probabilistically lost. Finally, we compare their performance by limiting the communication rate, which introduces delay. We show that DBA, once modified with a message timeout, is quite resistant to high message loss while DPP and DSA converge slower onto worse solutions. Our results also show that the setting of timeout value for DBA and MGM is an important factor in the convergence of these algorithms. Under conditions of message delay, DPP and DSA are less affected than DBA and MGM. Overall, DPP and DSA cause considerably less network load.","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":"79800771","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}
The current study presents a two-stage question retrieval approach which, in the first phase, retrieves similar questions for a given query using a deep learning based approach and in the second phase, re-ranks initially retrieved questions on the basis of inter-question similarities. The suggested deep learning based approach is trained using several surface features of texts and the associated weights are pre-trained using a deep generative model for better initialization. The proposed retrieval model outperforms standard baseline question retrieval approaches. The proposed re-ranking approach performs inference over a similarity graph constructed with the initially retrieved questions and re-ranks the questions based on their similarity with other relevant questions. Suggested re-ranking approach significantly improves the precision for the retrieval task.
{"title":"Using re-ranking to boost deep learning based community question retrieval","authors":"K. Ghosh, Plaban Kumar Bhowmick, Pawan Goyal","doi":"10.1145/3106426.3106442","DOIUrl":"https://doi.org/10.1145/3106426.3106442","url":null,"abstract":"The current study presents a two-stage question retrieval approach which, in the first phase, retrieves similar questions for a given query using a deep learning based approach and in the second phase, re-ranks initially retrieved questions on the basis of inter-question similarities. The suggested deep learning based approach is trained using several surface features of texts and the associated weights are pre-trained using a deep generative model for better initialization. The proposed retrieval model outperforms standard baseline question retrieval approaches. The proposed re-ranking approach performs inference over a similarity graph constructed with the initially retrieved questions and re-ranks the questions based on their similarity with other relevant questions. Suggested re-ranking approach significantly improves the precision for the retrieval task.","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":"81779464","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}
Vinicius Woloszyn, H. D. Dos Santos, Leandro Krug Wives, Karin Becker
The automatic detection of relevant reviews plays a major role in tasks such as opinion summarization, opinion-based recommendation, and opinion retrieval. Supervised approaches for ranking reviews by relevance rely on the existence of a significant, domain-dependent training data set. In this work, we propose MRR (Most Relevant Reviews), a new unsupervised algorithm that identifies relevant revisions based on the concept of graph centrality. The intuition behind MRR is that central reviews highlight aspects of a product that many other reviews frequently mention, with similar opinions, as expressed in terms of ratings. MRR constructs a graph where nodes represent reviews, which are connected by edges when a minimum similarity between a pair of reviews is observed, and then employs PageRank to compute the centrality. The minimum similarity is graph-specific, and takes into account how reviews are written in specific domains. The similarity function does not require extensive pre-processing, thus reducing the computational cost. Using reviews from books and electronics products, our approach has outperformed the two unsupervised baselines and shown a comparable performance with two supervised regression models in a specific setting. MRR has also achieved a significantly superior run-time performance in a comparison with the unsupervised baselines.
{"title":"MRR","authors":"Vinicius Woloszyn, H. D. Dos Santos, Leandro Krug Wives, Karin Becker","doi":"10.1145/3106426.3106444","DOIUrl":"https://doi.org/10.1145/3106426.3106444","url":null,"abstract":"The automatic detection of relevant reviews plays a major role in tasks such as opinion summarization, opinion-based recommendation, and opinion retrieval. Supervised approaches for ranking reviews by relevance rely on the existence of a significant, domain-dependent training data set. In this work, we propose MRR (Most Relevant Reviews), a new unsupervised algorithm that identifies relevant revisions based on the concept of graph centrality. The intuition behind MRR is that central reviews highlight aspects of a product that many other reviews frequently mention, with similar opinions, as expressed in terms of ratings. MRR constructs a graph where nodes represent reviews, which are connected by edges when a minimum similarity between a pair of reviews is observed, and then employs PageRank to compute the centrality. The minimum similarity is graph-specific, and takes into account how reviews are written in specific domains. The similarity function does not require extensive pre-processing, thus reducing the computational cost. Using reviews from books and electronics products, our approach has outperformed the two unsupervised baselines and shown a comparable performance with two supervised regression models in a specific setting. MRR has also achieved a significantly superior run-time performance in a comparison with the unsupervised baselines.","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":"81794341","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. Gulla, Lemei Zhang, Peng Liu, Özlem Özgöbek, Xiaomeng Su
Datasets for recommender systems are few and often inadequate for the contextualized nature of news recommendation. News recommender systems are both time- and location-dependent, make use of implicit signals, and often include both collaborative and content-based components. In this paper we introduce the Adressa compact news dataset, which supports all these aspects of news recommendation. The dataset comes in two versions, the large 20M dataset of 10 weeks' traffic on Adresseavisen's news portal, and the small 2M dataset of only one week's traffic. We explain the structure of the dataset and discuss how it can be used in advanced news recommender systems.
{"title":"The Adressa dataset for news recommendation","authors":"J. Gulla, Lemei Zhang, Peng Liu, Özlem Özgöbek, Xiaomeng Su","doi":"10.1145/3106426.3109436","DOIUrl":"https://doi.org/10.1145/3106426.3109436","url":null,"abstract":"Datasets for recommender systems are few and often inadequate for the contextualized nature of news recommendation. News recommender systems are both time- and location-dependent, make use of implicit signals, and often include both collaborative and content-based components. In this paper we introduce the Adressa compact news dataset, which supports all these aspects of news recommendation. The dataset comes in two versions, the large 20M dataset of 10 weeks' traffic on Adresseavisen's news portal, and the small 2M dataset of only one week's traffic. We explain the structure of the dataset and discuss how it can be used in advanced news recommender systems.","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":"81362279","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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