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Joint International Semantic Technology Conference, JIST 2011

作者： Zhou, Wenlei Wang, Haofen Chao, Jiansong Zhang, Weinan Yu, Yong APEX Data and Knowledge Management Lab. Department of Computer Science Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783642299223

Measuring semantic relatedness plays an important role in information retrieval and Natural Language Processing. However, little attention has been paid to measuring semantic relatedness between named entities, which is also very significant. As the existing knowledge based approaches have the entity coverage issue and the statistical based approaches have unreliable result to low frequent entities, we propose a more comprehensive approach by leveraging Linked Open data (LOD) to solve these problems. LOD consists of lots of data sources from different domains and provides rich a priori knowledge about the entities in the world. By exploiting the semantic associations in LOD, we propose a novel algorithm, called LODDO, to measure the semantic relatedness between named entities. The experimental results show the high performance and robustness of our approach. © 2012 Springer-Verlag.

关键词： knowledge based systems

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Application development using modeling and dynamical systems analysis 09

Application development using modeling and dynamical systems...

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1st International Workshop on Context-Aware Middleware and Services, CAMS 2009

作者： O'Neill, Eleanor McGlinn, Kris Lewis, David Bailey, Eoin Dobson, Simon McCartney, Kevin Knowledge and Data Engineering Group School of Computer Science and Statistics Trinity College Dublin Ireland Systems Research Group School of Computer Science and Informatics UCD Dublin Ireland Centre for Architectural Education College of Science Engineering and Food Science University College Cork Ireland

ISBN: (纸本)9781605585253

Research on context aware systems is handicapped by the lack of readily available large scale data sets, as well as by the lack of tools by which researchers can interact effectively with such data sets across a range of scales and granularities. We show how virtual reality combined with a dynamical systems analysis approach can start to address these gaps. Simulation allows for generation of simulated sensor data at runtime and actuations of entities in the virtual world. The ease of sensor deployment and configurations in a simulated environment allows for rapid reconfigurations enabling generation of the required large scale data sets for analysis. Using these data sets, dynamical systems analysis can determine if a given application is functioning in a manner that is deemed to be correct. Copyright © 2009 ACM.

关键词： Virtual reality

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Image description mining and hierarchical clustering on data records using HR-tree

Image description mining and hierarchical clustering on data...

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8th Asia-Pacific Web Conference, APWeb 2006: Frontiers of WWW Research and Development

作者： Zhang, Cong-Le Huang, Sheng Xue, Gui-Rong Yu, Yong Apex Data and Knowledge Management Lab. Department of Computer Science and Engineering Shanghai JiaoTong University Shanghai 200030 China

ISBN: (纸本)3540311424

Since we can hardly get semantics from the low-level features of the image, it is much more difficult to analyze the image than textual information on the Web. Traditionally, textual information around the image is used to represent the high-level features of the image. We argue that such "flat" representation can not describe images well. In this paper, Hierarchical Representation (HR) and HR-Tree are proposed for image description. Salient phrases in HR-Tree are further to distinguish this image with others sharing the same ancestor concepts. First, we design a method to extract the salient phrases for the images in data records. Then HR-Trees are built using these phrases. Finally, new hierarchical clustering algorithm based on HR-Tree is proposed for users' browsing conveniently. We demonstrate some HR-Trees and clustering results in experimental section. These results illustrate the advantages of our methods. © Springer-Verlag Berlin Heidelberg 2006.

关键词： data mining

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Emergent semantics from folksonomies: A quantitative study

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Journal on data Semantics VI - Special Issue on Emergent Semantics

作者： Zhang, Lei Wu, Xian Yu, Yong APEX Data and Knowledge Management Lab. Department of Computer Science and Engineering Shanghai JiaoTong University Shanghai 200030 China

ISBN: (纸本)3540367128

Defining and using ontology to annotate web resources with semantic markups is generally perceived as the primary way to implement the vision of the Semantic Web. The ontology provides a shared and machine understandable semantics for web resources that agents and applications can utilize. This top-down approach (in the sense that an ontology is defined first on top of existing web resources and then used later to markup them), however, has a high barrier to entry and is difficult to scale up. In this paper, we investigate using a bottom-up approach for semantically annotating web resources as supported by the now widely popular social bookmarks services on the web where users can annotate and categorize web resources using "tags" freely choosen by the user without any pre-existing global semantic model. This kind of informal social categories is coined as "folksonomies". We show how global semantics can be statistically inferred from the folksonomies to semantically annotate the web resources. The global semantic model also disambiguate the tags and group synonymous tags together. Finally, we show that there indeed are hierarchical relations among the emerged concepts in the folksonomy and it is plausible to further identify them if we use more advanced probabilistic models. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Semantics

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Web of data and web of entities: Identity and reference in interlinked data in the semantic web

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Philosophy and Technology 2012年第1期25卷 5-26页

作者： Bouquet, Paolo Stoermer, Heiko Vignolo, Massimiliano Department of Information Engineering and Computer Science University of Trento Trento Italy Fondazione Bruno Kessler Data Knowledge Management Unit Trento Trento Italy

Using web standards, such as uniform resource identifiers (URIs), XML and HTTP, for naming and describing resources which are not information objects is the key difference between the Web as we know it today and the Semantic Web. Naming and interlinking this type of resources by HTTP URIs (instead of individual constants in a formal language) is the key feature which distinguishes traditional knowledge representation from web-scale knowledge representation. However, this use of URIs brought back attention to the old philosophical problem of identity and reference in a new form. In this paper, we analyze the new version of the problem, provide a formal model for dealing with it when interlinking knowledge on the Web, and argue for the need of a distinction between the use of URIs for describing and accessing resources, and the use of URIs for fixing the reference. We show that in the current practice of linking data these roles are not clearly distinguished, and that this fact may cause unwanted effects and prevent some basic forms of data integration. We also discuss the role of an entity name system as a potential piece of infrastructure for fixing the reference in the Semantic Web. © 2011 Springer-Verlag.

关键词： Identity Linked data Reference Resource Semantic Web

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Multilayer perceptron ensembles in a truly sparse training context

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Neural Computing and Applications 2025年

作者： van der Wal, Peter R. D. Strisciuglio, Nicola Azzopardi, George Mocanu, Decebal Constatin Information Systems Group Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Nijenborgh 9 Groningen9747 AG Netherlands Data Management and Biometrics Group Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Hallenweg 19 Enschede7522 NH Netherlands Department of Computer Science Faculty of Science Technology and Medicine University of Luxembourg Maison du Nombre 6 avenue de la Fonte Esch4364 Luxembourg

Ensemble learning for artificial neural networks (ANNs) is an effective method to enhance predictive performance. However, ANNs are computationally and memory intensive, and naively training multiple networks can lead to excessive training times and costs. An effective tool for improving ensemble efficiency is introducing topological sparsity. Even though several implementations of efficient ensembles have been proposed, none of them can provide actual benefits in terms of computational overhead as the sparsity is simulated using binary masks. In this paper, we address this issue by introducing a Truly Sparse Ensemble without binary masks and directly incorporate native sparsity. We also propose two algorithms for initializing new subnetworks within the ensemble, leveraging this native topological sparsity to enhance subnetwork diversity. We demonstrate the performance of the resulting models at high levels of sparsity on several datasets in terms of classification accuracy, floating point operations (FLOPs), and actual running time. The proposed methods outperform all baseline dense and truly sparse models on tabular data, successfully diversify the training trajectory of the subnetworks, and increase the topological distance between subnetworks after re-initialization. © The Author(s) 2025.

关键词： Digital arithmetic

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Smart water quality analysis using IoT and big data analytics: A review 2

Smart water quality analysis using IoT and big data analytic...

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2nd IEEE International Conference on Electronic engineering, ICEEM 2021

作者： Hemdan, Ezz El-Din Essa, Youssef M. El-Sayed, Ayman Shouman, Marwa Moustafa, Abdullah N. Menofia University Computer Science and Engineering Department Faculty of Electronic Engineering Menouf Egypt Principle Data and Knowledge. Bayer Berlin Germany

ISBN: (纸本)9781665418423

In recent times, water quality monitoring, observing, and testing have become a significant research study with the advancement on the Internet of Things, and Big data analytics research ideas. The relationship between water demand and supply is very crucial for every country and is likewise a complex challenge to satisfy this requirement around the world. Water is compulsory for human survival being on the earth. Therefore, for survival, the preservation and taking care of the existing water resource are also equally substantial. Moreover, for a healthier society, access to clean and safe water resources is also imperative. To recognize the water quality effects and provide an automated water quality mentoring and a testing system can support in guaranteeing the safety of the water. This paper presents a study on water quality analysis using IoT and Big data analytics. This can help in developing an agile environment that can handle the massive flow of water big data generated by the smart sensors spread everywhere around us. © 2021 IEEE.

关键词： data Analytics

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The Linked data Mining Challenge 2016 5

The Linked Data Mining Challenge 2016

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5th Joint Workshop on data Mining and knowledge Discovery meets Linked Open data and the 1st International Workshop on Completing and Debugging the Semantic Web, Know@LOD 2016 and CoDeS 2016

作者： Ristoski, Petar Paulheim, Heiko Svátek, Vojtěch Zeman, Václav University of Mannheim Research Group Data and Web Science Germany University of Economics Department of Information and Knowledge Engineering Prague Czech Republic

The 2016 edition of the Linked data Mining Challenge, conducted in conjunction with Know@LOD 2016, has been the fourth edition of this challenge. This year's dataset collected music album ratings, where the task was to classify well and badly rated music albums. The best solution submitted reached an accuracy of almost 92:5%, which is a clear advancement over the baseline of 69:38%.

关键词： Linked data

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Unsupervised Statistical Text Simplification

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IEEE Transactions on knowledge and data engineering 2021年第4期33卷 1802-1806页

作者： Qiang, Jipeng Wu, Xindong Department of Computer Science Yangzhou University Yangzhou Jiangsu225127 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei Anhui10084 China

Most recent approaches for Text Simplification (TS) have drawn on insights from machine translation to learn simplification rewrites from the monolingual parallel corpus of complex and simple sentences, yet their effectiveness strongly relies on large amounts of parallel sentences. However, there has been a serious problem haunting TS for decades, that is, the availability of parallel TS corpora is scarce or not fit for the learning task. In this paper, we will focus on one especially useful and challenging problem of unsupervised TS without a single parallel sentence. To the best of our knowledge, we present the first unsupervised text simplification system based on phrase-based machine translation system, which leverages a careful initialization of phrase tables and language models. On the widely used WikiLarge and WikiSmall benchmarks, our system respectively obtains 39.08 and 25.12 SARI points, even outperforms some supervised baselines. © 1989-2012 IEEE.

关键词： Machine translation

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Generic pattern trees for exhaustive exceptional model mining

Generic pattern trees for exhaustive exceptional model minin...

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2012 European Conference on Machine Learning and Principles and Practice of knowledge Discovery in databases, ECML-PKDD 2012

作者： Lemmerich, Florian Becker, Martin Atzmueller, Martin Artificial Intelligence and Applied Computer Science Group University of Würzburg Würzburg D-97074 Germany Knowledge and Data Engineering Group University of Kassel Kassel 34121 Germany

ISBN: (纸本)9783642334856

Exceptional model mining has been proposed as a variant of subgroup discovery especially focusing on complex target concepts. Currently, efficient mining algorithms are limited to heuristic (non exhaustive) methods. In this paper, we propose a novel approach for fast exhaustive exceptional model mining: We introduce the concept of valuation bases as an intermediate condensed data representation, and present the general GP-growth algorithm based on FP-growth. Furthermore, we discuss the scope of the proposed approach by drawing an analogy to data stream mining and provide examples for several different model classes. Runtime experiments show improvements of more than an order of magnitude in comparison to a naive exhaustive depth-first search. © 2012 Springer-Verlag.

关键词： Heuristic algorithms

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