Real time summarization in microblog aims at providing new relevant and non redundant information about an event as soon as it occurs. In this paper, we introduce a new tweet summarization approach where the decision of selecting an incoming tweet is made immediately when a tweet is vailable. Unlike existing approaches where thresholds are redefined, the proposed method estimates thresholds for decision taking in real time as soon as the new tweet arrives. Tweet selection is based upon three criterion namely informativeness, novelty and relevance with regards of the user's interest which are combined as conjunctive condition. Only tweets having an informativeness and novelty scores above a parametric-free threshold are added to the summary. The evaluation of our approach was carried out on the TREC MB RTF 2015 data set and it was compared with well known baselines. The results have revealed that our approach produces the most precise summaries in comparison to all baselines and official runs of the TREC MB RTF 2015 task.
{"title":"Multi-criterion Real Time Tweet Summarization Based upon Adaptive Threshold","authors":"Abdelhamid Chellal, M. Boughanem, B. Dousset","doi":"10.1109/WI.2016.0045","DOIUrl":"https://doi.org/10.1109/WI.2016.0045","url":null,"abstract":"Real time summarization in microblog aims at providing new relevant and non redundant information about an event as soon as it occurs. In this paper, we introduce a new tweet summarization approach where the decision of selecting an incoming tweet is made immediately when a tweet is vailable. Unlike existing approaches where thresholds are redefined, the proposed method estimates thresholds for decision taking in real time as soon as the new tweet arrives. Tweet selection is based upon three criterion namely informativeness, novelty and relevance with regards of the user's interest which are combined as conjunctive condition. Only tweets having an informativeness and novelty scores above a parametric-free threshold are added to the summary. The evaluation of our approach was carried out on the TREC MB RTF 2015 data set and it was compared with well known baselines. The results have revealed that our approach produces the most precise summaries in comparison to all baselines and official runs of the TREC MB RTF 2015 task.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"23 1","pages":"264-271"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80119860","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}
Yulong Gu, Jiaxing Song, Weidong Liu, Y. Yao, Lixin Zou
Enormous efforts of human volunteers have made Wikipedia become a treasure of textual knowledge. Relation extraction that aims at extracting structured knowledge in the unstructured texts in Wikipedia is an appealing but quite challenging problem because it's hard for machines to understand plain texts. Existing methods are not effective enough because they understand relation types in textual level without exploiting knowledge behind plain texts. In this paper, we propose a novel framework called Athena 2.0 leveraging Semantic Patterns which are patterns that can understand relation types in semantic level to solve this problem. Extensive experiments show that Athena 2.0 significantly outperforms existing methods.
{"title":"Towards Accurate Relation Extraction from Wikipedia","authors":"Yulong Gu, Jiaxing Song, Weidong Liu, Y. Yao, Lixin Zou","doi":"10.1109/WI.2016.0023","DOIUrl":"https://doi.org/10.1109/WI.2016.0023","url":null,"abstract":"Enormous efforts of human volunteers have made Wikipedia become a treasure of textual knowledge. Relation extraction that aims at extracting structured knowledge in the unstructured texts in Wikipedia is an appealing but quite challenging problem because it's hard for machines to understand plain texts. Existing methods are not effective enough because they understand relation types in textual level without exploiting knowledge behind plain texts. In this paper, we propose a novel framework called Athena 2.0 leveraging Semantic Patterns which are patterns that can understand relation types in semantic level to solve this problem. Extensive experiments show that Athena 2.0 significantly outperforms existing methods.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"66 1","pages":"89-96"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79618997","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}
Qinpei Zhao, Zhenyu A. Liao, Jiangfeng Li, Yang Shi, Qirong Tang
The development of location-based applications raises a new challenge to manage and visualize large amounts of geo-tags presented on a web map. The visualization of the geo-tags often leads to a clutter problem, especially in web-mapping systems. We present a new clustering method to reduce the amount of visual clutter. A split smart swap strategy, which has the advantage that it can be applied to a certain data only once at all map scales, is employed in the method. We compare the proposed method to several other methods. Taking the advantage of the one-time running offline, the proposed method is more applicable for the clutter problem.
{"title":"A Split Smart Swap Clustering for Clutter Problem in Web Mapping System","authors":"Qinpei Zhao, Zhenyu A. Liao, Jiangfeng Li, Yang Shi, Qirong Tang","doi":"10.1109/WI.2016.0070","DOIUrl":"https://doi.org/10.1109/WI.2016.0070","url":null,"abstract":"The development of location-based applications raises a new challenge to manage and visualize large amounts of geo-tags presented on a web map. The visualization of the geo-tags often leads to a clutter problem, especially in web-mapping systems. We present a new clustering method to reduce the amount of visual clutter. A split smart swap strategy, which has the advantage that it can be applied to a certain data only once at all map scales, is employed in the method. We compare the proposed method to several other methods. Taking the advantage of the one-time running offline, the proposed method is more applicable for the clutter problem.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"14 1","pages":"439-443"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83063220","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 day-to-day behavior of the individuals reveal their personality traits. With the emergence of the social media platforms, some aspects of this behavior are being recorded in their online profiles. This provides necessary input to develop algorithms that can predict personality traits of individuals. However, these algorithms need to exploit the semantics of the data in order to reveal the personality traits. Current studies on this topic mainly exploited the syntactic features of the language used by individuals to predict their personality traits. In this work we demonstrate the value of exploiting semantics of the messages conveyed in social media posts for predicting personality traits. In other words, we present a study that attempts to simulate the cognitive ability of the human brain, which allows to identify the important implicit information in social media posts for understanding the personality traits of an individual. Our approach shows the value of publicly available knowledge bases in eliciting implicit information in the user generated content and their impact on predicting the personality traits of an individual. We evaluated our approach using well-known 'myPersonality' dataset and showed that it outperforms the state-of-the-art algorithms that mainly depend on syntactic features.
{"title":"Knowledge-Driven Approach to Predict Personality Traits by Leveraging Social Media Data","authors":"M. Thilakaratne, R. Weerasinghe, Sujan Perera","doi":"10.1109/WI.2016.0048","DOIUrl":"https://doi.org/10.1109/WI.2016.0048","url":null,"abstract":"The day-to-day behavior of the individuals reveal their personality traits. With the emergence of the social media platforms, some aspects of this behavior are being recorded in their online profiles. This provides necessary input to develop algorithms that can predict personality traits of individuals. However, these algorithms need to exploit the semantics of the data in order to reveal the personality traits. Current studies on this topic mainly exploited the syntactic features of the language used by individuals to predict their personality traits. In this work we demonstrate the value of exploiting semantics of the messages conveyed in social media posts for predicting personality traits. In other words, we present a study that attempts to simulate the cognitive ability of the human brain, which allows to identify the important implicit information in social media posts for understanding the personality traits of an individual. Our approach shows the value of publicly available knowledge bases in eliciting implicit information in the user generated content and their impact on predicting the personality traits of an individual. We evaluated our approach using well-known 'myPersonality' dataset and showed that it outperforms the state-of-the-art algorithms that mainly depend on syntactic features.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"33 1","pages":"288-295"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89303884","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}
Coalition formation, a key factor in multi-agent cooperation, can be solved optimally for at most a few dozen agents. This paper proposes a general approach to find suboptimal solutions for a large-scale coalition formation problem containing thousands of agents using multi-agent simulation. We model coalition formation as an iterative process in which agents join and leave coalitions, and we propose several valuation functions that assign values to the coalitions. We propose several coalition selection strategies that agents may use to decide whether or not to leave their current coalition and which coalition to join. We also show how these valuation functions and coalition selection strategies represent specific coalition formation applications. Finally, we show almost-optimal performance of our algorithms in small-scale scenarios by comparing our solutions with an optimal solution, and we show stable performance in a large-scale setting in which searching for the optimal solution is not feasible.
{"title":"Multi-agent Simulation Framework for Large-Scale Coalition Formation","authors":"Pavel Janovsky, S. DeLoach","doi":"10.1109/WI.2016.0055","DOIUrl":"https://doi.org/10.1109/WI.2016.0055","url":null,"abstract":"Coalition formation, a key factor in multi-agent cooperation, can be solved optimally for at most a few dozen agents. This paper proposes a general approach to find suboptimal solutions for a large-scale coalition formation problem containing thousands of agents using multi-agent simulation. We model coalition formation as an iterative process in which agents join and leave coalitions, and we propose several valuation functions that assign values to the coalitions. We propose several coalition selection strategies that agents may use to decide whether or not to leave their current coalition and which coalition to join. We also show how these valuation functions and coalition selection strategies represent specific coalition formation applications. Finally, we show almost-optimal performance of our algorithms in small-scale scenarios by comparing our solutions with an optimal solution, and we show stable performance in a large-scale setting in which searching for the optimal solution is not feasible.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"70 1","pages":"343-350"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83909435","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 key constraint of REST API is that all the resources must be reachable by some hyperlink paths from an entry point. However, to apply this constraint without prudence can result in excessive hyperlinks that do not provide new services but increase the dependence between the resources. Excessive hyperlinks are difficult to identify because: 1) a REST API can have dynamic and unbounded paths, and 2) the hyperlinks used to navigate a path are not observable and can be ambiguous. To tackle the first challenge, we propose a REST API model and a random walk algorithm to reduce the paths of a REST API to a small set. To address the second challenge, we develop a client model and a connection minimization algorithm to identify excessive hyperlinks based on given paths. By combining the random walk and the connection minimization algorithms, our method can minimize the connections of a REST API in polynomial time without involving the actual clients. A prototype system has been implemented and the tests show that the method is correct and can converge 90.6% to 99.9% faster than the baseline approach.
{"title":"Connection Minimization in REST API with Random Walks","authors":"Li Li, Min Luo","doi":"10.1109/WI.2016.0059","DOIUrl":"https://doi.org/10.1109/WI.2016.0059","url":null,"abstract":"A key constraint of REST API is that all the resources must be reachable by some hyperlink paths from an entry point. However, to apply this constraint without prudence can result in excessive hyperlinks that do not provide new services but increase the dependence between the resources. Excessive hyperlinks are difficult to identify because: 1) a REST API can have dynamic and unbounded paths, and 2) the hyperlinks used to navigate a path are not observable and can be ambiguous. To tackle the first challenge, we propose a REST API model and a random walk algorithm to reduce the paths of a REST API to a small set. To address the second challenge, we develop a client model and a connection minimization algorithm to identify excessive hyperlinks based on given paths. By combining the random walk and the connection minimization algorithms, our method can minimize the connections of a REST API in polynomial time without involving the actual clients. A prototype system has been implemented and the tests show that the method is correct and can converge 90.6% to 99.9% faster than the baseline approach.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"80 1","pages":"375-382"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83955885","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}
H. Drias, Samir Kechid, Sofia Adamou, Farouk Benyoucef
In the field of data science, we consider usually data independently from a problem to be solved. The originality of this paper consists in handling huge instances of combinatorial problems with datamining technologies in order to reduce the complexity of their treatment. Such task can be performed on Web combinatorial optimization such as internet data packet routing and web clustering. We focus in particular on the satisfiability of Boolean formulae but the proposed idea could be adopted for any other complex problem. The aim is to explore the satisfiability instance using datamining techniques in order to reduce its size, prior to solve it. An estimated solution for the obtained instance is then computed using a hybrid algorithm based on DPLL technique and a genetic algorithm. It is then compared to the solution of the initial instance in order to validate the method effectiveness. We performed experiments on the wellknown BMC datasets and show the benefits of using datamining techniques as a pretreatment, prior to solving the problem.
{"title":"Data Preprocessing for Web Combinatorial Problems","authors":"H. Drias, Samir Kechid, Sofia Adamou, Farouk Benyoucef","doi":"10.1109/WI.2016.0067","DOIUrl":"https://doi.org/10.1109/WI.2016.0067","url":null,"abstract":"In the field of data science, we consider usually data independently from a problem to be solved. The originality of this paper consists in handling huge instances of combinatorial problems with datamining technologies in order to reduce the complexity of their treatment. Such task can be performed on Web combinatorial optimization such as internet data packet routing and web clustering. We focus in particular on the satisfiability of Boolean formulae but the proposed idea could be adopted for any other complex problem. The aim is to explore the satisfiability instance using datamining techniques in order to reduce its size, prior to solve it. An estimated solution for the obtained instance is then computed using a hybrid algorithm based on DPLL technique and a genetic algorithm. It is then compared to the solution of the initial instance in order to validate the method effectiveness. We performed experiments on the wellknown BMC datasets and show the benefits of using datamining techniques as a pretreatment, prior to solving the problem.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"17 1","pages":"425-428"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83833311","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}
Visual analytics on frequent web usage patterns aims to help users to (i) analyze the data so as to discover implicit, previously unknown and potentially useful information in the form of collections of frequently visited web pages in a single session and to (ii) visually represent the discovered knowledge so as to gain insight about the data. In this paper, we propose an interactive visual analytics tool (iVAT) for frequent pattern mining. It uses an orientation free, circular layout to show frequent patterns. Moreover, we provide users with interactive feature to explicitly show connections between superset and subsets of sets of visited web pages. Experimental results show the effectiveness of our iVAT for visual analytics of frequent patterns about web data.
{"title":"An Interactive Circular Visual Analytic Tool for Visualization of Web Data","authors":"P. Dubois, Zhao Han, Fan Jiang, C. Leung","doi":"10.1109/WI.2016.0127","DOIUrl":"https://doi.org/10.1109/WI.2016.0127","url":null,"abstract":"Visual analytics on frequent web usage patterns aims to help users to (i) analyze the data so as to discover implicit, previously unknown and potentially useful information in the form of collections of frequently visited web pages in a single session and to (ii) visually represent the discovered knowledge so as to gain insight about the data. In this paper, we propose an interactive visual analytics tool (iVAT) for frequent pattern mining. It uses an orientation free, circular layout to show frequent patterns. Moreover, we provide users with interactive feature to explicitly show connections between superset and subsets of sets of visited web pages. Experimental results show the effectiveness of our iVAT for visual analytics of frequent patterns about web data.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"1 1","pages":"709-712"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90620252","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}
Typically, news articles mention not just one but multiple events. These events can be classified into subject or background events. The former are events that the article is written about, while the latter are additional events referred to in order to explain the background of the subject events (e.g., causal relations, circumstances or the consequences of the main event). Background events are considered to play an important role in helping to understand articles. In this paper, we first propose to classify content of news articles into subject or background event descriptions. In the second part of the paper, we demonstrate a novel solution for improving the news article search. Based on the subject and background relationship structure between events and articles, our method outputs news articles that help with understanding of a given target article.
{"title":"Supporting News Article Understanding by Detecting Subject-Background Event Relations","authors":"Shotaro Tanaka, A. Jatowt, Katsumi Tanaka","doi":"10.1109/WI.2016.0044","DOIUrl":"https://doi.org/10.1109/WI.2016.0044","url":null,"abstract":"Typically, news articles mention not just one but multiple events. These events can be classified into subject or background events. The former are events that the article is written about, while the latter are additional events referred to in order to explain the background of the subject events (e.g., causal relations, circumstances or the consequences of the main event). Background events are considered to play an important role in helping to understand articles. In this paper, we first propose to classify content of news articles into subject or background event descriptions. In the second part of the paper, we demonstrate a novel solution for improving the news article search. Based on the subject and background relationship structure between events and articles, our method outputs news articles that help with understanding of a given target article.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"39 1","pages":"256-263"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85733724","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}
Probabilistic topic models are powerful techniques which are widely used for discovering topics or semantic content from a large collection of documents. However, because topic models are entirely unsupervised, they may lead to topics that are not understandable in applications. Recently, several knowledge-based topic models have been proposed which primarily use word-level domain knowledge in the model to enhance the topic coherence and ignore the rich information carried by entities (e.g persons, location, organizations, etc.) associated with the documents. Additionally, there exists a vast amount of prior knowledge (background knowledge) represented as ontologies and Linked Open Data (LOD), which can be incorporated into the topic models to produce coherent topics. In this paper, we introduce a novel entity-based topic model, called EntLDA, to effectively integrate an ontology with an entity topic model to improve the topic modeling process. Furthermore, to increase the coherence of the identified topics, we introduce a novel ontology-based regularization framework, which is then integrated with the EntLDA model. Our experimental results demonstrate the effectiveness of the proposed model in improving the coherence of the topics.
{"title":"Discovering Coherent Topics with Entity Topic Models","authors":"M. Allahyari, K. Kochut","doi":"10.1109/WI.2016.0015","DOIUrl":"https://doi.org/10.1109/WI.2016.0015","url":null,"abstract":"Probabilistic topic models are powerful techniques which are widely used for discovering topics or semantic content from a large collection of documents. However, because topic models are entirely unsupervised, they may lead to topics that are not understandable in applications. Recently, several knowledge-based topic models have been proposed which primarily use word-level domain knowledge in the model to enhance the topic coherence and ignore the rich information carried by entities (e.g persons, location, organizations, etc.) associated with the documents. Additionally, there exists a vast amount of prior knowledge (background knowledge) represented as ontologies and Linked Open Data (LOD), which can be incorporated into the topic models to produce coherent topics. In this paper, we introduce a novel entity-based topic model, called EntLDA, to effectively integrate an ontology with an entity topic model to improve the topic modeling process. Furthermore, to increase the coherence of the identified topics, we introduce a novel ontology-based regularization framework, which is then integrated with the EntLDA model. Our experimental results demonstrate the effectiveness of the proposed model in improving the coherence of the topics.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"17 1","pages":"26-33"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88417419","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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