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Content Aggregation on Knowledge Bases using Graph Clustering

by Christoph Schmitz, Andreas Hotho, Gerd Stumme - In Proceedings of the 3rd European Semantic Web Conference, Budva , 2006
"... Abstract. Recently, research projects such as PADLR and SWAP have developed tools like Edutella or Bibster, which are targeted at establish-ing peer-to-peer knowledge management (P2PKM) systems. In such a system, it is necessary to obtain provide brief semantic descriptions of peers, so that routing ..."
Abstract - Cited by 2 (2 self) - Add to MetaCart
that routing algorithms or matchmaking processes can make decisions about which communities peers should belong to, or to which peers a given query should be forwarded. This paper proposes the use of graph clustering techniques on knowl-edge bases for that purpose. Using this clustering, we can show that our

Towards Content Aggregation on Knowledge Bases through Graph Clustering

by Christoph Schmitz
"... Recently, several research projects such as PADLR and SWAP have developed tools like Edutella or Bibster, which have been targeted at establishing peer-to-peer knowledge management (P2PKM) systems. In such a system, it is necessary for participants to provide brief descriptions of themselves, so tha ..."
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that routing algorithms or matchmaking processes can make decisions about which communities peers should belong to, or to which peer a given query should be forwarded. In this talk, I propose the use of graph clustering techniques on knowledge bases for that purpose. After a brief round-trip over an ontology-based

Efficient Aggregation for Graph Summarization

by Yuanyuan Tian, Richard A. Hankins, Jignesh M. Patel
"... Graphs are widely used to model real world objects and their relationships, and large graph datasets are common in many application domains. To understand the underlying characteristics of large graphs, graph summarization techniques are critical. However, existing graph summarization methods are mo ..."
Abstract - Cited by 83 (5 self) - Add to MetaCart
are mostly statistical (studying statistics such as degree distributions, hop-plots and clustering coefficients). These statistical methods are very useful, but the resolutions of the summaries are hard to control. In this paper, we introduce two database-style operations to summarize graphs. Like the OLAP

A graph-oriented model for articulation of ontology interdependencies

by Prasenjit Mitra, Gio Wiederhold, Martin L. Kersten - In Proceedings, 7th International Conference on Extending Database Technology , 2000
"... Abstract: Ontologies are knowledge structures to explicate the contents, essential properties, and relationships between terms in a knowledge source. Many sources are now accessible with associated ontologies. Most prior work on use of ontologies relies on the construction of a single global ontolog ..."
Abstract - Cited by 159 (4 self) - Add to MetaCart
the ontologies amenable for automatic composition based on the accumulated knowledge rules, we represent them using a graph-oriented model extended with a small algebraic operator set. ONION, a user-friendly toolkit, aids the experts in bridging the semantic gap in real-life settings. Our framework provides a

Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization

by Bamshad Mobasher, Honghua Dai, Tao Luo, Miki Nakagawa - Data Mining and Knowledge Discovery , 2002
"... Web usage mining, possibly used in conjunction with standard approaches to personalization such as collaborative filtering, can help address some of the shortcomings of these techniques, including reliance on subjective user ratings, lack of scalability, and poor performance in the face of high-dime ..."
Abstract - Cited by 142 (15 self) - Add to MetaCart
present and experimentally evaluate two techniques, based on clustering of user transactions and clustering of pageviews, in order to discover overlapping aggregate profiles that can be effectively used by recommender systems for real-time Web personalization. We evaluate these techniques both in terms

Semi-supervised graph clustering: a kernel approach

by Brian Kulis, Sugato Basu, Inderjit Dhillon, Raymond Mooney , 2008
"... Semi-supervised clustering algorithms aim to improve clustering results using limited supervision. The supervision is generally given as pairwise constraints; such constraints are natural for graphs, yet most semi-supervised clustering algorithms are designed for data represented as vectors. In this ..."
Abstract - Cited by 94 (3 self) - Add to MetaCart
Semi-supervised clustering algorithms aim to improve clustering results using limited supervision. The supervision is generally given as pairwise constraints; such constraints are natural for graphs, yet most semi-supervised clustering algorithms are designed for data represented as vectors

Event-based analysis of video

by Lihi Zelnik-manor, Michal Irani - In Proc. CVPR , 2001
"... Dynamic events can be regarded as long-term temporal objects, which are characterized by spatiotemporal features at multiple temporal scales. Based on this, we design a simple statistical distance measure between video sequences (possibly of different lengths) based on their behavioral content. This ..."
Abstract - Cited by 145 (2 self) - Add to MetaCart
of long video sequences based on behavioral content, and (iii) clustering events within long video sequence into event-consistent sub-sequences (i.e., into event-consistent “clusters”). These tasks are performed without prior knowledge of the types of events, their models, or their temporal extents. Our

Graph-based hierarchical conceptual clustering

by Istvan Jonyer, Lawrence B. Holder, Diane J. Cook - International Journal on Artificial Intelligence Tools , 2001
"... Hierarchical conceptual clustering has been proven to be a useful data mining technique. Graph-based representation of structural information has been shown to be successful in knowledge discovery. The Subdue substructure discovery system provides the advantages of both approaches. In this paper we ..."
Abstract - Cited by 32 (5 self) - Add to MetaCart
Hierarchical conceptual clustering has been proven to be a useful data mining technique. Graph-based representation of structural information has been shown to be successful in knowledge discovery. The Subdue substructure discovery system provides the advantages of both approaches. In this paper we

Bipartite Graph Partitioning and Content-based Image Clustering

by Guoping Qiu , 2004
"... This paper presents a method to model the images and their content descriptors in large image databases using bipartite graphs. A graph partitioning algorithm is then developed to cluster the images and their content description features simultaneously such that each cluster is automatically associa ..."
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This paper presents a method to model the images and their content descriptors in large image databases using bipartite graphs. A graph partitioning algorithm is then developed to cluster the images and their content description features simultaneously such that each cluster is automatically

Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms

by Stan Salvador, Philip Chan , 2003
"... Many clustering and segmentation algorithms both suffer from the limitation that the number of clusters/segments are specified by a human user. It is often impractical to expect a human with sufficient domain knowledge to be available to select the number of clusters/segments to return. In this pape ..."
Abstract - Cited by 101 (2 self) - Add to MetaCart
. In this paper, we investigate techniques to determine the number of clusters or segments to return from hierarchical clustering and segmentation algorithms. We propose an efficient algorithm, the L method, that finds the “knee ” in a ‘ # of clusters vs. clustering evaluation metric ’ graph. Using the knee
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