2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K)最新文献_第3页

CBIR search engine for user designed query (UDQ) 用户设计查询(UDQ)的CBIR搜索引擎

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005614703720379

T. Jaworska

引用次数: 2

Comparing summarisation techniques for informal online reviews 比较非正式在线评论的摘要技术

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005612203220329

Mhairi McNeill, R. Raeside, Martin Graham, I. Roseboom

引用次数: 3

Learning query expansion from association rules between terms 从术语之间的关联规则中学习查询扩展

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005642705250530

Ahlem Bouziri, C. Latiri, Éric Gaussier, Yassin Belhareth

{"title":"Learning query expansion from association rules between terms","authors":"Ahlem Bouziri, C. Latiri, Éric Gaussier, Yassin Belhareth","doi":"10.5220/0005642705250530","DOIUrl":"https://doi.org/10.5220/0005642705250530","url":null,"abstract":"Query expansion technique offers an interesting solution for obtaining a complete answer to a user query while preserving the quality of retained documents. This mainly relies on an accurate choice of the added terms to an initial query. In this paper, we attempt to use data mining methods to extract dependencies between terms, namely a generic basis of association rules between terms. Face to the huge number of derived association rules and in order to select the optimal combination of query terms from the generic basis, we propose to model the problem as a classification problem and solve it using a supervised learning algorithm. For this purpose, we first generate a training set using a genetic algorithm based approach that explores the association rules space in order to find an optimal set of expansion terms, improving the MAP of the search results, we then build a model able to predict which association rules are to be used when expanding a query. The experiments were performed on SDA 95 collection, a data collection for information retrieval. The main observation is that the hybridization of textmining techniques and query expansion in an intelligent way allows us to incorporate the good features of all of them. As this is a preliminary attempt in this direction, there is a large scope for enhancing the proposed method.","PeriodicalId":102743,"journal":{"name":"2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128603724","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: 5

Markov chain based method for in-domain and cross-domain sentiment classification 基于马尔可夫链的域内和跨域情感分类方法

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005636001270137

Giacomo Domeniconi, G. Moro, A. Pagliarani, Roberto Pasolini

{"title":"Markov chain based method for in-domain and cross-domain sentiment classification","authors":"Giacomo Domeniconi, G. Moro, A. Pagliarani, Roberto Pasolini","doi":"10.5220/0005636001270137","DOIUrl":"https://doi.org/10.5220/0005636001270137","url":null,"abstract":"Sentiment classification of textual opinions in positive, negative or neutral polarity, is a method to understand people thoughts about products, services, persons, organisations, and so on. Interpreting and labelling opportunely text data polarity is a costly activity if performed by human experts. To cut this labelling cost, new cross domain approaches have been developed where the goal is to automatically classify the polarity of an unlabelled target text set of a given domain, for example movie reviews, from a labelled source text set of another domain, such as book reviews. Language heterogeneity between source and target domain is the trickiest issue in cross-domain setting so that a preliminary transfer learning phase is generally required. The best performing techniques addressing this point are generally complex and require onerous parameter tuning each time a new source-target couple is involved. This paper introduces a simpler method based on the Markov chain theory to accomplish both transfer learning and sentiment classification tasks. In fact, this straightforward technique requires a lower parameter calibration effort. Experiments on popular text sets show that our approach achieves performance comparable with other works.","PeriodicalId":102743,"journal":{"name":"2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114328042","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

Social network analysis for predicting emerging researchers 预测新兴研究人员的社会网络分析

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005593500270035

Syed Masum Billah, Susan Gauch

{"title":"Social network analysis for predicting emerging researchers","authors":"Syed Masum Billah, Susan Gauch","doi":"10.5220/0005593500270035","DOIUrl":"https://doi.org/10.5220/0005593500270035","url":null,"abstract":"Finding rising stars in academia early in their careers has many implications when hiring new faculty, applying for promotion, and/or requesting grants. Typically, the impact and productivity of a researcher are assessed by a popular measurement called the h-index that grows linearly with the academic age of a researcher. Therefore, h-indices of researchers in the early stages of their careers are almost uniformly low, making it difficult to identify those who will, in future, emerge as influential leaders in their field. To overcome this problem, we make use of social network analysis to identify young researchers most likely to become successful as measured by their h-index. We assume that the co-authorship graph reveals a great deal of information about the potential of young researchers. We built a social network of 62,886 researchers using the data available in CiteSeerx. We then designed and trained a linear SVM classifier to identify emerging authors based on their personal attributes and/or their networks of co-authors. We evaluated our classifier's ability to predict the future research impact of a set of 26,170 young researchers, those with an h-index of less than or equal to two in 2005. By examining their actual impact six years later, we demonstrate that the success of young researchers can be predicted more accurately based on their professional network than their established track records.","PeriodicalId":102743,"journal":{"name":"2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122052340","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

Knowledge discovery and modeling based on Conditional Fuzzy Clustering with Interval Type-2 fuzzy 基于区间2型模糊条件模糊聚类的知识发现与建模

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005617804400444

Yeong-Hyeon Byeon, Keun-Chang Kwak

引用次数: 2

Overlapping kernel-based Community Detection with node attributes 与节点属性重叠的基于内核的社区检测

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005640205170524

D. Maccagnola, E. Fersini, Rabah Djennadi, E. Messina

引用次数: 3

Pseudo Relevance Feedback technique and Semantic Similarity for Corpus-based Expansion 基于语料库扩展的伪相关反馈技术和语义相似度

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005626904450450

M. Mohd, Jaffar Atwan, Kiyoaki Shirai

{"title":"Pseudo Relevance Feedback technique and Semantic Similarity for Corpus-based Expansion","authors":"M. Mohd, Jaffar Atwan, Kiyoaki Shirai","doi":"10.5220/0005626904450450","DOIUrl":"https://doi.org/10.5220/0005626904450450","url":null,"abstract":"The adaptation of a Query Expansion (QE) approach for Arabic documents may produce the worst rankings or irrelevant results. Therefore, we have introduced a technique, which is to utilise the Arabic WordNet in the corpus and query expansion level. A Point-wise Mutual Information (PMI) corpus-based measure is used to semantically select synonyms from the WordNet. In addition, Automatic Query Expansion (AQE) and Pseudo Relevance Feedback (PRF) methods were also explored to improve the performance of the Arabic information retrieval (AIR) system. The experimental results of our proposed techniques for AIR shows that the use of Arabic WordNet in the corpus and query level together with AQE, and the adaptation of PMI in the expansion process have successfully reduced the level of ambiguity as these techniques select the most appropriate synonym. It enhanced knowledge discovery by taking care of the relevancy aspect. The techniques also demonstrated an improvement in Mean Average Precision by 49%, with an increase of 7.3% in recall in comparison to the baseline.","PeriodicalId":102743,"journal":{"name":"2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125791347","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: 1

A new approach for collaborative filtering based on Bayesian network inference 基于贝叶斯网络推理的协同过滤新方法

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005635204750480

Loc X. Nguyen

引用次数: 2

Non-negative Matrix Factorization for binary data 二元数据的非负矩阵分解

2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) Pub Date : 2015-11-12 DOI: 10.5220/0005614805550563

J. Larsen, L. Clemmensen

{"title":"Non-negative Matrix Factorization for binary data","authors":"J. Larsen, L. Clemmensen","doi":"10.5220/0005614805550563","DOIUrl":"https://doi.org/10.5220/0005614805550563","url":null,"abstract":"We propose the Logistic Non-negative Matrix Factorization for decomposition of binary data. Binary data are frequently generated in e.g. text analysis, sensory data, market basket data etc. A common method for analysing non-negative data is the Non-negative Matrix Factorization, though this is in theory not appropriate for binary data, and thus we propose a novel Non-negative Matrix Factorization based on the logistic link function. Furthermore we generalize the method to handle missing data. The formulation of the method is compared to a previously proposed logistic matrix factorization without non-negativity constraint on the features. We compare the performance of the Logistic Non-negative Matrix Factorization to Least Squares Non-negative Matrix Factorization and Kullback-Leibler (KL) Non-negative Matrix Factorization on sets of binary data: a synthetic dataset, a set of student comments on their professors collected in a binary term-document matrix and a sensory dataset. We find that choosing the number of components is an essential part in the modelling and interpretation, that is still unresolved.","PeriodicalId":102743,"journal":{"name":"2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125274128","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