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5th international conference on web Intelligence, mining and Semantics, WIMS 2015

作者： Bayerl, Sebastian Granitzer, Michael Media Computer Science University of Passau Germany

ISBN: (纸本)9781450332934

Discovering and integrating relevant real-live datasets are essential tasks, when it comes to handling Linked data. Similar to data Warehousing approaches, Linked data can be prepared to enable sophisticated data analysis. the developed open source framework bacon enables interactive and crowed-sourced data Integration on Linked data (Linked data Integration), utilizing the RDF data Cube Vocabulary and the semantic properties of Linked Open data. Discovering suitable datasets on-the-y in local or remote repositories sets up the ensuing integration process. Based on well-known data Warehousing processes, the semantic nature of the data is taken into account to handle and merge RDF data Cubes. To do so, structure and content of the cubes must be analyzed and processed. A similarity measure has been developed to find similarly structured cubes. the user is offered a graphical interface, where he can search for suitable cubes and modify their structure based on semantic properties. this process is fostered by a set of automated suggestions to support inexperienced users and also domain experts. © 2015 acm.

关键词： data fusion

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Scalable Machine Learning on High-Dimensional Vectors: From data Series to Deep Network Embeddings 2020

Scalable Machine Learning on High-Dimensional Vectors: From ...

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10th international conference on web Intelligence, mining and Semantics, WIMS 2020 and 5th international conference on Real-time Intelligent Systems, RTIS 2020

作者： Echihabi, Karima Zoumpatianos, Kostas Palpanas, themis IRDA Rabat IT Center ENSIAS Mohammed v University Morocco Harvard University United States France

ISBN: (纸本)9781450375429

there is an increasingly pressing need, by several applications in diverse domains, for developing techniques able to analyze very large collections of static and streaming sequences (a.k.a. data series), predominantly in real-time. Examples of such applications come from Internet of things installations, neuroscience, astrophysics, and a multitude of other scientific and application domains that need to apply machine learning techniques for knowledge extraction. It is not unusual for these applications, for which similarity search is a core operation, to involve numbers of data series in the order of hundreds of millions to billions, which are seldom analyzed in their full detail due to their sheer size. Such application requirements have driven the development of novel similarity search methods that can facilitate scalable analytics in this context. At the same time, a host of other methods have been developed for similarity search of high-dimensional vectors in general. All these methods are now becoming increasingly important, because of the growing popularity and size of sequence collections, as well as the growing use of high-dimensional vector representations of a large variety of objects (such as text, multimedia, images, audio and video recordings, graphs, database tables, and others) thanks to deep network embeddings. In this work, we review recent efforts in designing techniques for indexing and analyzing massive collections of data series, and argue that they are the methods of choice even for general high-dimensional vectors. Finally, we discuss the challenges and open research problems in this area. © 2020 Owner/Author.

关键词： Deep learning

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Modeling and predicting information search behavior 15

Modeling and predicting information search behavior

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5th international conference on web Intelligence, mining and Semantics, WIMS 2015

作者： Karanam, Saraschandra Van Oostendorp, Herre Sanchiz, Mylène Chevalier, Aline Chin, Jessie Fu, Wai Tat Utrecht University Utrecht Netherlands Université Toulouse Toulouse France University of Illinois UrbanaIL United States

ISBN: (纸本)9781450332934

this paper looks at two limitations of cognitive models of webnavigation: first, they do not account for the entire process of information search and second, they do not account for the differences in search behavior caused by aging. To address these limitations, data from an experiment in which two types of information search tasks (simple and difficult), presented to both young and old participants was used. We found that in general difficult tasks demand significantly more time, significantly more clicks, significantly more reformulations and are answered significantly less accurately than simple tasks. Older persons inspect the search engine result pages significantly longer, produce significantly fewer reformulations with difficult tasks than younger persons, and are significantly more accurate than younger persons with simple tasks. We next used a cognitive model of web-navigation called CoLiDeS to predict which search engine result a user would choose to click. Old participants were found to click more often only on search engine results with high semantic similarity with the query. search engine results generated by old participants were of higher semantic similarity value (computed w.r.t the query) than those generated by young participants only in the second cycle. Match between modelpredicted clicks and actual user clicks was found to be significantly higher for difficult tasks compared to simple tasks. Potential improvements in enhancing the modeling and its applications are discussed. © 2015 acm.

关键词： Semantics

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Autonomous Knowledge-oriented Clustering Using Decision-theoretic Rough Set theory

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FUNDAMENTA INFORMATICAE 2012年第2-3期115卷 141-156页

作者： Yu, Hong Chu, Shuangshuang Yang, Dachun Chongqing Univ Posts & Telecommun Inst Comp Sci & Technol Chongqing 400065 Peoples R China ZTE Corp Chongqing R&D Inst Chongqing 400060 Peoples R China

In many applications, clusters tend to have vague or imprecise boundaries. It is desirable that clustering techniques should consider such an issue. the decision-theoretic rough set (DTRS) model is a typical probabilistic rough set model, which has the ability to deal with imprecise, uncertain, and vague information. this paper proposes an autonomous clustering method using the decision-theoretic rough set model based on a knowledge-oriented clustering framework. In order to get the initial knowledge-oriented clustering, the threshold values are produced autonomously based on semantics of clustering without human intervention. Furthermore, this paper estimates the risk of a clustering scheme based on the decision-theoretic rough set by considering various loss functions, which can process the different granular overlapping boundary. An autonomous clustering algorithm is proposed, which is not only experimented with the synthetic data and the standard data but also applied in the web search results clustering. the results of experiments show that the proposed method is effective and efficient.

关键词： clustering knowledge-oriented clustering decision-theoretic rough set theory autonomous data mining

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Multidisciplinary Information Retrieval 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Michail Salampasis Birger Larsen

ISBN: (数字)9783642312748

ISBN: (纸本)9783642312731

this book constitutes the proceedings of the 5th international Information Retrieval Facility conference, IRFC 2012, held in Vienna, Austria, July 2-3, 2012. the 12 papers presented were carefully reviewed and selected from 17 high-quality submissions. IRF conferences wish to bring young researchers into contact with industry at an early stage. this fifth conference aimed to tackle four complementary research areas: information retrieval, machine translations for search solutions, and interactive information access. the papers are organized into topical sections on patent search, web search, applications, and query formulation and analysis.

关键词： Information Storage and Retrieval data mining and Knowledge Discovery Information Systems Applications (incl. Internet) User Interfaces and Human Computer Interaction Artificial Intelligence

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A Hybrid Approach to Interpretable Analysis of Research Paper Collections 2020

A Hybrid Approach to Interpretable Analysis of Research Pape...

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10th international conference on web Intelligence, mining and Semantics, WIMS 2020 and 5th international conference on Real-time Intelligent Systems, RTIS 2020

作者： Mirkin, Boris Frolov, Dmitry Vlasov, Alex Nascimento, Susana Fenner, Trevor Department of Data Analysis and Artificial Intelligence HSE University Moscow Russia Department of Computer Science and Information Systems Birkbeck University of London London United Kingdom Natimatica Ltd. Moscow Russia Department of Computer Science and NOVA LINCS Universidade Nova de Lisboa Caparica Portugal Department of Computer Science and Information Systems Birkbeck University of London London United Kingdom

ISBN: (纸本)9781450375429

We define and find a most specific generalization of a fuzzy set of topics assigned to leaves of the rooted tree of a taxonomy. this generalization lifts the set to a "head subject"in the higher ranks of the taxonomy, that is supposed to "tightly"cover the query set, possibly bringing in some errors, both "gaps"and "offshoots". Our method involves two more automated analysis techniques: a fuzzy clustering method, FADDIS, involving both additive and spectral properties, and a purely structural string-to-text relevance measure based on suffix trees annotated by frequencies. We apply this to extract research tendencies from two collections of research papers: (a) about 18000 research papers published in Springer journals on data science for 20 years, and (b) about 27000 research papers retrieved from Springer and Elsevier journals in response to data science related queries. We consider a taxonomy of data Science based on the Association for Computing Machinery Classification of Computing System (acm-CCS 2012). Our findings allow us to make some comments on the tendencies of research that cannot be derived by using more conventional techniques. © 2020 acm.

关键词： Taxonomies

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Enabling spatial-visual search for geospatial image databases 5

Enabling spatial-visual search for geospatial image database...

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5th IEEE international conference on Multimedia Big data, BigMM 2019

作者： Shahabi, Cyrus University of Southern California Computer Science Department SAL 300 Los AngelesCA90007 United States

ISBN: (纸本)9781728155272

Due to the ubiquity of GPS-equipped cameras, captured photos are automatically tagged with camera location (referred to as geo-tagged images) at the acquisition time. Subsequently, web and mobile applications are emerging that provide search capabilities for images that are similar to a given query image and within a given geographical region (referred to as spatial-visual search). In this talk, I focus on two research challenges to enable spatial-visual search. First, I will discuss the need for new index structures to expedite image search based on both spatial and visual relevance. Hence, I present a generic index structure which supports organizing the images using their spatial and visual properties, and also supports an adaptive distribution of images based on different local partitioning either spatially or visually. Second, I discuss the challenge of inferring a location for the legacy images that are collected without spatial metadata. To utilize and support spatial-visual search for such images, there is a need for an accurate image localization technique for non-geo-tagged images. Moreover, even for geo-tagged images, the camera location of an image can be different from the location of the scene depicted in the image (referred to as scene location) rendering its native localization inaccurate. To address this problem, I present a framework for image scene localization using a CNN-based classification. I conclude by showing how spatial-visual search can be used for retrieving images to be analyzed for various smart-city applications in Los Angeles (e.g., street cleanliness and graffiti recognition). © 2019 IEEE.

关键词： Location

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AMiner: mining Deep Knowledge from Big Scholar data

AMiner: Mining Deep Knowledge from Big Scholar Data

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25th international conference on World Wide web (WWW)

作者： Tang, Jie Tsinghua Univ Dept Comp Sci & Technol Tsinghua Natl Lab Informat Sci & Technol TNList Beijing Peoples R China

ISBN: (纸本)9781450341448

AMiner(1) is the second generation of the ArnetMiner system [6]. We focus on developing author-centric analytic and mining tools for gaining a deep understanding of the large and heterogeneous networks formed by authors, papers, venues, and knowledge concepts. One fundamental goal is how to extract and integrate semantics from different sources. We have developed algorithms to automatically extract researchers' profiles from the web [5] and resolve the name ambiguity problem [3], and connect different professional networks [9]. We also developed methodologies to incorporate knowledge from the Wikipedia and other sources into the system [7, 2] to bridge the gap between network science and the web mining research. In this talk, I will focus on answering two fundamental questions for author-centric network analysis: who is who? and who are similar to each other? the system has been in operation since 2006 and has collected more than 100,000,000 author profiles, 100,000,000 publication papers, and 7,800,000 knowledge concepts. It has been widely used for collaboration recommendation [4], similarity analysis [8], and community evolution [1].

关键词： Academic search Network analysis Knowledge graph Big scholar data

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Autonomous Knowledge-oriented Clustering Using Decision-theoretic Rough Set theory 1

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5th international conference on Rough Sets and Knowledge Technology (RSKT)

ISBN: (数字)9783642162480

ISBN: (纸本)9783642162473

关键词： clustering knowledge-oriented clustering decision-theoretic rough set theory autonomous data mining

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ASCOT: a text mining-based web-service for efficient search and assisted creation of clinical trials

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BMC MEDICAL INFORMATICS AND DECISION MAKING 2012年第1-Sup期12卷 S3-S3页

作者： Korkontzelos, Ioannis Mu, Tingting Ananiadou, Sophia Univ Manchester Natl Ctr Text Min Manchester M1 7DN Lancs England Univ Manchester Sch Comp Sci Manchester M1 7DN Lancs England

Clinical trials are mandatory protocols describing medical research on humans and among the most valuable sources of medical practice evidence. searching for trials relevant to some query is laborious due to the immense number of existing protocols. Apart from search, writing new trials includes composing detailed eligibility criteria, which might be time-consuming, especially for new researchers. In this paper we present ASCOT, an efficient search application customised for clinical trials. ASCOT uses text mining and data mining methods to enrich clinical trials with metadata, that in turn serve as effective tools to narrow down search. In addition, ASCOT integrates a component for recommending eligibility criteria based on a set of selected protocols.

关键词： Recommendation System Vector Space Model Unify Medical Language System Candidate Term Cluster Label

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