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Information granulation for web-based information support systems

JingTao Yao, Yiyu Y. Yao, Belur V. Dasarathy
2003 Data Mining and Knowledge Discovery: Theory, Tools, and Technology V  
In particular, we apply clustering methods for the granulation of different entities involved in IRSS. Two types of granulations, single-level and multi-level granulations, are investigated.  ...  In this paper, we discuss the potential applications of data mining techniques for the design of Web based information retrieval support systems (IRSS).  ...  They allow multi-representation of documents. Granular computing plays an important role in the construction of document models. The retrieval models deals with the search functionality.  ... 
doi:10.1117/12.509158 dblp:conf/dmkdttt/YaoY03 fatcat:h2sbluyijfck3kdnx32j7bemgi

Granular Computing for the Design of Information Retrieval Support Systems [chapter]

Y. Y. Yao
2004 Network Theory and Applications  
One can use a hierarchical granulation of document collection, namely, a layered and multi-resolution representation of documents.  ...  A document is described by different representations at various levels. Hence, a cluster-based IR system implicitly employs multi-representation of documents.  ... 
doi:10.1007/978-1-4613-0227-8_10 fatcat:fefy2x2u5jdmnfx7ez3g7tcmzu

Applying multi-view based metadata in personalized ranking for recommender systems

Marcos A. Domingues, Camila V. Sundermann, Flávio M. M. Barros, Marcelo G. Manzato, Maria G. C. Pimentel, Solange O. Rezende, Stanley Oliveira
2015 Proceedings of the 30th Annual ACM Symposium on Applied Computing - SAC '15  
Our proposal uses a unsupervised learning method to construct topic hierarchies with named entity recognition as privileged information.  ...  In this paper, we propose a multi-view based metadata extraction technique from unstructured textual content in order to be applied in recommendation algorithms based on latent factors.  ...  MULTI-VIEW BASED METADATA Most existing clustering methods usually represent the textual information by using only the terms of the documents, i.e., by using bag-of-words (technical information/ view).  ... 
doi:10.1145/2695664.2695955 dblp:conf/sac/DominguesSBMPR15 fatcat:7zmmx5ujafa75iqnhvxz2uyuuq

Document clustering of scientific texts using citation contexts

Bader Aljaber, Nicola Stokes, James Bailey, Jian Pei
2009 Information retrieval (Boston)  
Many existing document clustering techniques use the "bag-of-words" model to represent the content of a document.  ...  We also compare these text-based clustering techniques with a link-based clustering  ...  Hierarchical Document Clustering (HAC). For the three different representations used in this work, we cluster documents using a Hierarchical clustering algorithm.  ... 
doi:10.1007/s10791-009-9108-x fatcat:im3bvp2sg5fjdn2jtl2eegdqe4

Building A Hierarchical, Granular Knowledge Cube

Alexander Denzler, Marcel Wehrle, Andreas Meier
2015 Zenodo  
In this paper, the concept behind such a construct, called a granular knowledge cube, is defined, and its intended use as an instrument that manages to cope with different data types and detect knowledge  ...  A knowledge base stores facts and rules about the world that applications can use for the purpose of reasoning.  ...  The most commonly used representation methods for the semi-manual approach belong to either the group of formalisms or the group of Semantic Web languages.  ... 
doi:10.5281/zenodo.1107720 fatcat:ulvr4rpxbjeublxnzxrput46g4

Hierarchical Stream Clustering Based NEWS Summarization System

M. Arun Manicka Raja, S. Swamynathan
2022 Computers Materials & Continua  
This is alleviated in our proposed system by tagging the news feed with domain corpuses and organizing the news streams with the hierarchical structure with topic wise representation.  ...  The major contributions of this work involve domain corpus based news collection, news content extraction, hierarchical clustering of the news and summarization of news.  ...  Multi granularity hierarchical representation [8] is the content representation of the data for easy access of the fine grain level data.  ... 
doi:10.32604/cmc.2022.019451 fatcat:hla3cpqu5jez3povfmgwvjzaee

Home Photo Retrieval: Time Matters [chapter]

Philippe Mulhem, Joo-Hwee Lim
2003 Lecture Notes in Computer Science  
With semantic content representation extracted using Visual Keywords and Extended Conceptual Graphs, we demonstrate the effectiveness of photo retrieval on 2400 timestamped heterogeneous home photos with  ...  In this paper, we propose the use of temporal events for organizing and representing home photos using structured document formalism and hence a new way to retrieve photos of an event using both image  ...  query by example, with the VK (c.f. 3.1) and CG (c.f. 3.2) representations, using α equal to 0.9.  ... 
doi:10.1007/3-540-45113-7_32 fatcat:ldmibsiqvvf5fn2lk26n7l5db4

An Overview of Web Data Clustering Practices [chapter]

Athena Vakali, Jaroslav Pokorný, Theodore Dalamagas
2004 Lecture Notes in Computer Science  
in clustering employed over the Web.  ...  Clustering is a challenging topic in the area of Web data management.  ...  In this context, various approaches for clustering of Web documents using the Website topology have been proposed in the literature.  ... 
doi:10.1007/978-3-540-30192-9_59 fatcat:cil7pgmogfdcbehyihgsihqx4a

Multi-document Summarization via Deep Learning Techniques: A Survey

Congbo Ma, Wei Emma Zhang, Mingyu Guo, Hu Wang, QUAN Z. Sheng
2022 ACM Computing Surveys  
Multi-document summarization (MDS) is an effective tool for information aggregation that generates an informative and concise summary from a cluster of topic-related documents.  ...  . • We discuss the open issues of deep learning based multi-document summarization and identify the future research directions of this ield.  ...  We used Google Scholar as the main search engine to select representative works from 2015 to 2021.  ... 
doi:10.1145/3529754 fatcat:r4lngnzrgjbfziazokpd2c5s44

Multi-document Summarization via Deep Learning Techniques: A Survey [article]

Congbo Ma, Wei Emma Zhang, Mingyu Guo, Hu Wang, Quan Z. Sheng
2021 arXiv   pre-print
Multi-document summarization (MDS) is an effective tool for information aggregation that generates an informative and concise summary from a cluster of topic-related documents.  ...  Words, sentences and documents are three granularity of semantic units that are connected by a granularity hierarchical relation graph.  ...  However, many existing deep learning methods do not make full use of this hierarchical relationship in the document cluster [35, 73, 132 ].  ... 
arXiv:2011.04843v3 fatcat:zfi52xxef5g2tjkaw6hgjpwa5i

Web object indexing using domain knowledge

Muyuan Wang, Zhiwei Li, Lie Lu, Wei-Ying Ma, Naiyao Zhang
2005 Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining - KDD '05  
Finally, the structure attributes of the web object are extracted with the knowledge document that best matches the web object.  ...  Our approach also indicates a new way to use trust-worthy Deep Web knowledge to help organize dispersive information of Surface Web.  ...  With hierarchical domain knowledge, web objects can be indexed using the structure information contained in the knowledge document that best matches the web object.  ... 
doi:10.1145/1081870.1081905 dblp:conf/kdd/WangLLMZ05 fatcat:fny2rdfparfyljq6swmy2qrgjy

Towards a Job Title Classification System [article]

Faizan Javed, Matt McNair, Ferosh Jacob, Meng Zhao
2016 arXiv   pre-print
The system leverages a varied collection of classification as well clustering algorithms.  ...  Preliminary results are presented using experimental evaluation on real world industrial data.  ...  We show that using this approach of treating classes separately alleviates processing time, enhances scalability, and results in more accurate job title classification with a finer granularity in the set  ... 
arXiv:1606.00917v1 fatcat:pwf25q36ufdz3efc4aweesp2ha

Beyond hyperlinks

Xuanhui Wang, Bin Tan, Azadeh Shakery, ChengXiang Zhai
2009 Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09  
In this paper, we propose to leverage search logs to allow a user to browse beyond hyperlinks with a multi-resolution topic map constructed based on search logs.  ...  Specifically, we treat search logs as "footprints" left by previous users in the information space and build a multi-resolution topic map to semantically capture and organize them in multiple granularities  ...  In this section, we adopt a bottom-up hierarchical clustering method. Generating Multi-Resolution Map Nodes We use hierarchical star clustering to build map nodes and their zooming relations.  ... 
doi:10.1145/1645953.1646110 dblp:conf/cikm/WangTSZ09 fatcat:krfse7hegrhtphif225h4ml5rq

Self–Organizing Map Representation for Clustering Wikipedia Search Results [chapter]

Julian Szymański
2011 Lecture Notes in Computer Science  
The Vector Space Model representation based on terms has been used to build groups of similar articles extracted from Kohonen Self-Organizing Maps with DBSCAN clustering.  ...  We introduce hierarchical organization of the categorized articles changing the granularity of SOM network.  ...  Employing DBSCAN clustering we extract from the SOM groups of the most similar documents. Changing the SOM granularity we construct hierarchical categories that organize documents set.  ... 
doi:10.1007/978-3-642-20042-7_15 fatcat:rldsrirsqfaczmpbajjzq3kch4

A language-based approach to indexing heterogeneous multimedia lifelog

Peng-Wen Cheng, Snehal Chennuru, Senaka Buthpitiya, Ying Zhang
2010 International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction on - ICMI-MLMI '10  
By quantizing the heterogeneous high dimensional sensory data into text representation, we are able to apply statistical natural language processing techniques to index, recognize, segment, cluster, retrieve  ...  In this paper, we present a novel approach to indexing lifelogs using activity language.  ...  Using Multi-level HMM, such as the Hierarchical Hidden Markov Model (HHMM) [6] allows for unsupervised discovery of structures at multiple level in video segmentation and activity recognition [20] .  ... 
doi:10.1145/1891903.1891937 dblp:conf/icmi/ChenCBZ10 fatcat:voiryho4erfk7mllr2xentjb7q
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