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2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)最新文献

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A Framework for Automatic Personalised Ontology Learning 一种自动个性化本体学习框架
2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0025
M. A. Bashar, Yuefeng Li, Yang Gao
{"title":"A Framework for Automatic Personalised Ontology Learning","authors":"M. A. Bashar, Yuefeng Li, Yang Gao","doi":"10.1109/WI.2016.0025","DOIUrl":"https://doi.org/10.1109/WI.2016.0025","url":null,"abstract":"Understanding or acquiring a user's information needs from their local information repository (e.g. a set of example-documents that are relevant to user information needs) is important in many applications. However, acquiring the user's information needs from the local information repository is very challenging. Personalised ontology is emerging as a powerful tool to acquire the information needs of users. However, its manual or semi-automatic construction is expensive and time-consuming. To address this problem, this paper proposes a model to automatically learn personalised ontology by labelling topic models with concepts, where the topic models are discovered from a user's local information repository. The proposed model is evaluated by comparing against ten baseline models on the standard dataset RCV1 and a large ontology LCSH. The results show that the model is effective and its performance is significantly improved.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"67 1","pages":"105-112"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76068670","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}
引用次数: 6
A Journey of Bounty Hunters: Analyzing the Influence of Reward Systems on StackOverflow Question Response Times 赏金猎人之旅:分析奖励制度对StackOverflow问题响应时间的影响
2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0114
Philipp Berger, Patrick Hennig, Tom Bocklisch, Tom Herold, C. Meinel
{"title":"A Journey of Bounty Hunters: Analyzing the Influence of Reward Systems on StackOverflow Question Response Times","authors":"Philipp Berger, Patrick Hennig, Tom Bocklisch, Tom Herold, C. Meinel","doi":"10.1109/WI.2016.0114","DOIUrl":"https://doi.org/10.1109/WI.2016.0114","url":null,"abstract":"Question and Answering (Q&A) platforms are an important source for information and a first place to go when searching for help. Q&A sites, like StackOverflow (SO), use reward systems to incentivize users to answer fast and accurately. In this paper we study and predict the response time for those questions on StackOverflow, that benefit from an additional incentive through so called bounties. Shaped by different motivations and rules these questions perform unlike regular questions. As our key finding we note that topic related factors provide a much stronger evidence than previously found factors for these questions. Finally, we compare models based on these features predicting the response time in the context of bounty questions.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"15 1","pages":"644-649"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85779266","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}
引用次数: 11
Emotion Detection Using Kinect 3D Facial Points 使用Kinect 3D面部点进行情感检测
2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0063
Zhan Zhang, Liqing Cui, Xiaoqian Liu, T. Zhu
{"title":"Emotion Detection Using Kinect 3D Facial Points","authors":"Zhan Zhang, Liqing Cui, Xiaoqian Liu, T. Zhu","doi":"10.1109/WI.2016.0063","DOIUrl":"https://doi.org/10.1109/WI.2016.0063","url":null,"abstract":"With the development of pattern recognition and artificial intelligence, emotion recognition based on facial expression has attracted a great deal of research interest. Facial emotion recognition are mainly based on facial images. The commonly used datasets are created artificially, with obvious facial expression on each facial images. Actually, emotion is a complicated and dynamic process. If a person is happy, probably he/she may not keep obvious happy facial expression all the time. Practically, it is important to recognize emotion correctly even if the facial expression is not clear. In this paper, we propose a new method of emotion recognition, i.e., to identify three kinds of emotion: sad, happy and neutral. We acquire 1347 3D facial points by Kinect V2.0. Key facial points are selected and feature extraction is conducted. Principal Component Analysis (PCA) is employed for feature dimensionality reduction. Several classical classifiers are used to construct emotion recognition models. The best performance of classification on all, male and female data are 70%, 77% and 80% respectively.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"3 2","pages":"407-410"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91474543","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}
引用次数: 18
Core Periphery Structures in Weighted Graphs Using Greedy Growth 基于贪心增长的加权图的核心外围结构
2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0012
D. Sardana, R. Bhatnagar
{"title":"Core Periphery Structures in Weighted Graphs Using Greedy Growth","authors":"D. Sardana, R. Bhatnagar","doi":"10.1109/WI.2016.0012","DOIUrl":"https://doi.org/10.1109/WI.2016.0012","url":null,"abstract":"Core periphery structure is a meso-scale property of complex networks. Core periphery structures can help identify the relationships between cohesive core clusters surrounded by sparse peripheries. The knowledge about such relationships can have many practical applications in real world complex networks. For example, in a web based network between all blogs on different topics, peripheries connecting popular groups could help in the study of flow of information across the web. In this paper, we propose a construction of core periphery structures for weighted graphs. We present a greedy growth based algorithm to extract core periphery structures in weighted graphs. We also score the core periphery associations as a measure of distance between them. Through extensive experimentation using two synthetic and two real world Protein-Protein Interaction (PPI) networks, we demonstrate the usefulness of core periphery structures over simple overlapping clusters obtained by a state of the art clustering algorithm called ClusterONE.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"49 1","pages":"1-8"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87631253","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}
引用次数: 3
Context-Aware Entity Disambiguation in Text Using Markov Chains 基于马尔可夫链的文本上下文感知实体消歧
2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0018
Lei Zhang, Achim Rettinger, Patrick Philipp
{"title":"Context-Aware Entity Disambiguation in Text Using Markov Chains","authors":"Lei Zhang, Achim Rettinger, Patrick Philipp","doi":"10.1109/WI.2016.0018","DOIUrl":"https://doi.org/10.1109/WI.2016.0018","url":null,"abstract":"In recent years, the amount of entities in large knowledge bases has been increasing rapidly. Such entities can help to bridge unstructured text with structured knowledge and thus be beneficial for many entity-centric applications. The key issue is to link entity mentions in text with entities in knowledge bases, where the main challenge lies in mention ambiguity. Many methods have been proposed to tackle this problem. However, most of the methods assume certain characteristics of the input mentions and documents, e.g., only named entities are considered. In this paper, we propose a context-aware approach to collective entity disambiguation of the input mentions in text with different characteristics in a consistent manner. We extensively evaluate the performance of our approach over 9 datasets and compare it with 14 state-of-the-art methods. Experimental results show that our approach outperforms the existing methods in most cases.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"19 1","pages":"49-56"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86103384","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}
引用次数: 4
Experiments with Semantic Enrichment for Event Classification in Tweets 基于语义丰富的推文事件分类实验
2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0084
Simone Aparecida Pinto Romero, Karin Becker
{"title":"Experiments with Semantic Enrichment for Event Classification in Tweets","authors":"Simone Aparecida Pinto Romero, Karin Becker","doi":"10.1109/WI.2016.0084","DOIUrl":"https://doi.org/10.1109/WI.2016.0084","url":null,"abstract":"Twitter has become key for bringing awareness about real-world events, but the identification of event related posts goes beyond filtering keywords. Semantic enrichment using knowledge sources such as the Linked Open Data (LOD) cloud, has been proposed to deal with the poor textual contents of tweets for event classification. However, each work considers a particular type of event, underlined by specific assumptions according to the application purpose. In a search for an approach that suits different types of events, in this paper we identify different types of semantic features, and propose a process for semantic enrichment that involves the mapping of textual tokens into semantic concepts, the extraction of corresponding semantic properties from the LOD cloud, and their interpolation for event classification. We evaluate the contribution of each type of semantic feature using different tweet datasets representing events of distinct natures, and knowledge extracted from DBPedia.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"52 1","pages":"503-506"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86765588","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}
引用次数: 4
From Opinion Lexicons to Sentiment Classification of Tweets and Vice Versa: A Transfer Learning Approach 从观点词汇到推文的情感分类，反之亦然:一种迁移学习方法
2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.29
Felipe Bravo-Marquez, E. Frank, B. Pfahringer
{"title":"From Opinion Lexicons to Sentiment Classification of Tweets and Vice Versa: A Transfer Learning Approach","authors":"Felipe Bravo-Marquez, E. Frank, B. Pfahringer","doi":"10.1109/WI.2016.29","DOIUrl":"https://doi.org/10.1109/WI.2016.29","url":null,"abstract":"Message-level and word-level polarity classification are two popular tasks in Twitter sentiment analysis. They have been commonly addressed by training supervised models from labelled data. The main limitation of these models is the high cost of data annotation. Transferring existing labels from a related problem domain is one possible solution for this problem. In this paper, we propose a simple model for transferring sentiment labels from words to tweets and vice versa by representing both tweets and words using feature vectors residing in the same feature space. Tweets are represented by standard NLP features such as unigrams and part-of-speech tags. Words are represented by averaging the vectors of the tweets in which they occur. We evaluate our approach in two transfer learning problems: 1) training a tweet-level polarity classifier from a polarity lexicon, and 2) inducing a polarity lexicon from a collection of polarity-annotated tweets. Our results show that the proposed approach can successfully classify words and tweets after transfer.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"52 1","pages":"145-152"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89923499","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}
引用次数: 12
Mining Social Media Content for Crime Prediction 挖掘社交媒体内容用于犯罪预测
2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0089
S. Aghababaei, M. Makrehchi
{"title":"Mining Social Media Content for Crime Prediction","authors":"S. Aghababaei, M. Makrehchi","doi":"10.1109/WI.2016.0089","DOIUrl":"https://doi.org/10.1109/WI.2016.0089","url":null,"abstract":"Social media provides increasing opportunities for users to voluntarily share their thoughts and concerns in a large volume of data. While user-generated data from each individual may not provide considerable information, when combined, they include hidden variables, which may convey significant events. In this paper, we pursue the question of whether social media context can provide socio-behavior \"signals\" for crime prediction. The hypothesis is that crowd publicly available data in social media, in particular Twitter, may include predictive variables, which can indicate the changes in crime rates. We developed a model for crime trend prediction where the objective is to employ Twitter content to identify whether crime rates have dropped or increased for the prospective time frame. We also present a Twitter sampling model to collect historical data to avoid missing data over time. The prediction model was evaluated for different cities in the United States. The experiments revealed the correlation between features extracted from the content and crime rate directions. Overall, the study provides insight into the correlation of social content and crime trends as well as the impact of social data in providing predictive indicators.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"31 1","pages":"526-531"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88169216","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}
引用次数: 39
Managing Evolving Trust Policies within Open and Decentralized Communities 在开放和分散的社区中管理不断发展的信任政策
2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0119
Reda Yaich
{"title":"Managing Evolving Trust Policies within Open and Decentralized Communities","authors":"Reda Yaich","doi":"10.1109/WI.2016.0119","DOIUrl":"https://doi.org/10.1109/WI.2016.0119","url":null,"abstract":"Online communities promise a new era of flexible and dynamic collaborations. However, these features also raise new security challenges, especially regarding how trust is managed. In this paper, we focus on situations wherein communities participants collaborate with each others via software agents that take trust decisions on their behalf based on policies. Due to the open and dynamic nature of Online Communities, participants can neither anticipate all possible interactions nor have foreknowledge of sensitive resources and potentially malicious partners. This makes the specification of trust policies complex and risky, especially for collective (i.e., community-level) policies, motivating the need for policies evolution. The aim of this paper is to introduce an approach in order to manage the evolution of trust policies within online communities. Our scenario allows any member of the community to trigger the evolution of the community-level policy and make the other members of the community converge towards it.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"10 1","pages":"668-673"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90154666","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}
引用次数: 0
Fusing Search Results from Possible Alternative Queries 从可能的替代查询融合搜索结果
2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0105
Ashraf Bah Rabiou, Ben Carterette
{"title":"Fusing Search Results from Possible Alternative Queries","authors":"Ashraf Bah Rabiou, Ben Carterette","doi":"10.1109/WI.2016.0105","DOIUrl":"https://doi.org/10.1109/WI.2016.0105","url":null,"abstract":"Data fusion has been shown to be a simple and effective way to improve retrieval results. Most existing data fusion methods combine ranked lists from different retrieval functions for a single given query—but in most real search settings, the diversity of retrieval functions required to achieve good fusion performance is not available. This paper presents a method for data fusion based on combining ranked lists from different queries that users could have entered for their information need, keeping the retrieval function fixed. We argue that if we can obtain a set of \"possible queries\" for an information need, we can achieve high effectiveness by fusing the rankings over the possible queries. In order to demonstrate effectiveness, we present experimental results on 5 different datasets covering tasks such as ad-hoc search, novelty and diversity search, and search in the presence of implicit user feedback. Our results show strong performances for our method, it is competitive with state-of-the-art methods on the same datasets, and in some cases outperforms them.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"29 1","pages":"606-609"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81210164","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}
引用次数: 0
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