2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)最新文献_第2页

Temporal TF-IDF: A High Performance Approach for Event Summarization in Twitter 时态TF-IDF: Twitter中事件摘要的高性能方法

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0087

Nasser Alsaedi, P. Burnap, O. Rana

引用次数: 18

Hierarchical Agglomerative Clustering Using Common Neighbours Similarity 基于共同邻居相似性的层次聚类

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0093

M. Makrehchi

{"title":"Hierarchical Agglomerative Clustering Using Common Neighbours Similarity","authors":"M. Makrehchi","doi":"10.1109/WI.2016.0093","DOIUrl":"https://doi.org/10.1109/WI.2016.0093","url":null,"abstract":"Hierarchical clustering has been well-studied in the community of machine learning. Hierarchical clustering algorithms are deterministic, stable, and do not need a pre-determined number of clusters as input. However, they are not scalable for very large data due to their non-linear complexity. In this paper, a new approach is proposed to reduce the complexity of Hierarchical Clustering, improve the purity of the clustering algorithm, and reduce the chaining factor. The proposed method has the following components: (i) A new combination similarity based on common-neighbours of graph theory is proposed, (ii) In every iteration, instead of calculating the centroids for new clusters, new centroids are estimated from centroids in previous iteration, and (iii) In each iteration, instead of merging only one pair of objects, multiple pairs are merged at the same time. In addition to the proposed combination similarity, four well-known methods including centroid-based, group-based, complete-link, and single-link, have been also implemented. All five methods are tested and evaluated using two metrics: purity and imbalance or chaining factor. We show that our proposed algorithm outperforms other classic methods.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"48 1","pages":"546-551"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88907284","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

Mining Topically Coherent Patterns for Unsupervised Extractive Multi-document Summarization 挖掘主题一致模式的无监督抽取多文档摘要

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0028

Yutong Wu, Yuefeng Li, Yue Xu, Wei Huang

引用次数: 3

Sound of Network: Capturing Network Structure by Signal Response 网络之声:通过信号响应捕捉网络结构

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0064

Shin-ya Sato

{"title":"Sound of Network: Capturing Network Structure by Signal Response","authors":"Shin-ya Sato","doi":"10.1109/WI.2016.0064","DOIUrl":"https://doi.org/10.1109/WI.2016.0064","url":null,"abstract":"Structural analysis of networks has attracted a lot of attention from researchers. While previous studies have devised structural indices for quantitatively describing network structures, they mostly focus on specific structural characteristics and the structural information provided by them is often fragmental. This paper proposes methods for representing and analyzing structural characteristics of networks that provide a comprehensive understanding of network structure. It describes a new approach for grasping the structural features of networks, wherein a signal is input at a vertex of a given network, and the signal propagates through the network and eventually returns to the input vertex through various routes. The returning signal at the input vertex can be thought of as the response of the network to the input signal and can be used as a representation of the structural characteristics of the network that can be analyzed using existing signal analysis techniques. The paper also describes experiments examining the properties of response signals in artificial networks and presents a method for identifying the structural characteristics of a network by analyzing its response signal.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"109 1","pages":"411-416"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89551917","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

Inferring Your Expertise from Twitter: Integrating Sentiment and Topic Relatedness 从Twitter推断你的专业知识:整合情感和主题相关性

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0027

Yu Xu, Dong Zhou, S. Lawless

{"title":"Inferring Your Expertise from Twitter: Integrating Sentiment and Topic Relatedness","authors":"Yu Xu, Dong Zhou, S. Lawless","doi":"10.1109/WI.2016.0027","DOIUrl":"https://doi.org/10.1109/WI.2016.0027","url":null,"abstract":"The ability to understand the expertise of users in Social Networking Sites (SNSs) is a key component for delivering effective information services such as talent seeking and user recommendation. However, users are often unwilling to make the effort to explicitly provide this information, so existing methods aimed at user expertise discovery in SNSs primarily rely on implicit inference. This work aims to infer a user's expertise based on their posts on the popular micro-blogging site Twitter. The work proposes a sentiment-weighted and topic relation-regularized learning model to address this problem. It first uses the sentiment intensity of a tweet to evaluate its importance in inferring a user's expertise. The intuition is that if a person can forcefully and subjectively express their opinion on a topic, it is more likely that the person has strong knowledge of that topic. Secondly, the relatedness between expertise topics is exploited to model the inference problem. The experiments reported in this paper were conducted on a large-scale dataset with over 10,000 Twitter users and 149 expertise topics. The results demonstrate the success of our proposed approach in user expertise inference and show that the proposed approach outperforms several alternative methods.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"2 1","pages":"121-128"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75273551","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

Optimizations for Multiple Collective Sources in Delivery Systems 配送系统中多集源的优化

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0075

Lixin Fu, J. Jarabek

引用次数: 1

Enterprise Knowledge Graphs: A Backbone of Linked Enterprise Data 企业知识图谱:关联企业数据的主干

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0083

Mikhail Galkin, S. Auer, S. Scerri

{"title":"Enterprise Knowledge Graphs: A Backbone of Linked Enterprise Data","authors":"Mikhail Galkin, S. Auer, S. Scerri","doi":"10.1109/WI.2016.0083","DOIUrl":"https://doi.org/10.1109/WI.2016.0083","url":null,"abstract":"Semantic technologies in enterprises have recently received increasing attention from both the research and industrial side. The concept of Linked Enterprise Data (LED) describes a framework to incorporate benefits of semantic technologies into enterprise IT environments. However, LED still remains an abstract idea lacking a point of origin, i.e., station zero from which it comes to existence. In this paper we argue and demonstrate that Enterprise Knowledge Graphs (EKGs) might be considered as an embodiment of LED lifting corporate information management to a semantic level which ultimately allows for real artificial intelligence applications. By EKG we refer to a semantic network of concepts, properties, individuals and links representing and referencing foundational and domain knowledge relevant for an enterprise. Although the concept of EKGs was not invented yesterday, both enterprise and semantic communities have not yet come up with a formal comprehensive framework for designing such graphs. In this paper we aim to join the dots between the expanding interest in EKGs expressed by those communities and the lack of blueprints for realizing the EKGs. A thorough study of the key design concepts provides a multi-dimensional aspects matrix from which an enterprise is able to choose specific features of the highest priority. We emphasize the importance of various data fusion approaches, e.g., unified and federated. In the extensive evaluation section we investigate the effect of the chosen approach on the EKG performance along several dimensions, e.g., basic reasoning and OWL entailment which account for machine understanding of the EKG data, and access control subsystem which is of the utmost importance in large enterprises.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"6 1","pages":"497-502"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84681396","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: 25

Domain-Specific Term Extraction for Concept Identification in Ontology Construction 本体构建中概念识别的领域特定术语提取

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0016

Kiruparan Balachandran, Surangika Ranathunga

{"title":"Domain-Specific Term Extraction for Concept Identification in Ontology Construction","authors":"Kiruparan Balachandran, Surangika Ranathunga","doi":"10.1109/WI.2016.0016","DOIUrl":"https://doi.org/10.1109/WI.2016.0016","url":null,"abstract":"An ontology is a formal and explicit specification of a shared conceptualization. Manual construction of domain ontology does not adequately satisfy requirements of new applications, because they need a more dynamic ontology and the possibility to manage a considerable quantity of concepts that humans cannot achieve alone. Researchers have discussed ontology learning as a solution to overcome issues related to the manual construction of ontology. Ontology learning is either an automatic or semi-automatic process to apply methods for building ontology from scratch, or enriching or adapting an existing ontology. This research focuses on improving the process of term extraction for identifying concepts in ontology learning. Available approaches for term extraction process are limited in various ways. These limitations include: (1) obtaining domain-specific terms from a domain expert as seed words without automatically discovering them from the corpus, and (2) unsuitable usage of corpora in discovering domain-specific terms for multiple domains. Our study uses linguistic analysis and statistical calculations to extract domain-specific simple and complex terms to overcome this first limitation. To eliminate the second limitation, we use multiple contrastive corpora that reduce the biasness in using a single contrastive corpus. Evaluations show that our system is better at extracting terms when compared with the previous research that used the same corpora.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"28 1","pages":"34-41"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85450816","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: 10

Stance Classification by Recognizing Related Events about Targets 基于目标相关事件识别的姿态分类

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0100

Akira Sasaki, Junta Mizuno, Naoaki Okazaki, Kentaro Inui

引用次数: 13

Credibility as Signal: Predicting Evaluations of Credibility by a Signal-Based Model 可信度作为信号:基于信号的模型预测可信度评估

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0026

Grzegorz Kowalik, A. Wierzbicki, Tomasz Borzyszkowski, Wojciech Jaworski

引用次数: 2