Query Driven Conceptual Browsing : A Semi-Automated Approach for Building and Exploring Concepts on the Web

The presence of communities, which are groups of highly cross referenced pages together representing a single concept, is a striking feature of the World Wide Web. Quite often a group of communities, each topically coherent within itself, may be related through a common concept manifested in each of them. Motivated by this observation, we present a method for query-driven conceptual browsing for exploring concepts on the Web starting from a userspecified query. We show how this idea is related to prior work on learning concept maps and on Web Mining, and discuss the application of conceptual browsing for user-driven exploration and discovery of new concepts on the Web

Cluster Generation and Cluster Labelling for Web Snippets: A Fast and Accurate Hierarchical Solution

This paper describes Armil, a meta-search engine that groups into disjoint labelled clusters the Web snippets returned by auxiliary search engines. The cluster labels generated by Armil provide the user with a compact guide to assessing the relevance of each cluster to her information need. Strik- ing the right balance between running time and cluster well- formedness was a key point in the design of our system. Both the clustering and the labelling tasks are performed on the ?y by processing only the snippets provided by the auxil- iary search engines, and use no external sources of knowl- edge. Clustering is performed by means of a fast version of the furthest-point-?rst algorithm for metric k-center cluster- ing. Cluster labelling is achieved by combining intra-cluster and inter-cluster term extraction based on a variant of the information gain measure. We have tested the clustering ef- fectiveness of Armil against Vivisimo, the de facto industrial standard in Web snippet clustering, using as benchmark a comprehensive set of snippets obtained from the Open Di- rectory Project hierarchy. According to two widely accepted external\u27 metrics of clustering quality, Armil achieves bet- ter performance levels by 10%. We also report the results of a thorough user evaluation of both the clustering and the cluster labelling algorithms. On a standard 1GHz ma- chine, Armil performs clustering and labelling altogether in less than one second

Ranking clustered Keyword Search On Semi structured data

No Abstrac

Refinement of Document Clustering by Using NMF

PACLIC 21 / Seoul National University, Seoul, Korea / November 1-3, 200

Diversity Maximization Under Matroid Constraints

Aggregator websites typically present documents in the form of representative clusters. In order for users to get a broader perspective,it is important to deliver a diversified set of representative documents in those clusters. One approach to diversification is to maximize the average dissimilarity among documents. Another way to capture diversity is to avoid showing several documents from the same category (e.g. from the same news channel). We model the latter approach as a (partition) matroid constraint, and study diversity maximization problems under matroid constraints. We present the first constant-factor approximation algorithm for this problem,using a new technique. Our local search 0:5-approximation algorithm is also the first constant-factor approximation for the maxdispersion problem under matroid constraints. Our combinatorial proof technique for maximizing diversity under matroid constraints uses the existence of a family of Latin squares which may also be of independent interest. In order to apply these diversity maximization algorithms in the context of aggregator websites and as a preprocessing step for our diversity maximization tool, we develop greedy clustering algorithms that maximize weighted coverage of a predefined set of topics. Our algorithms are based on computing a set of cluster centers, where clusters are formed around them. We show the better performance of our algorithms for diversity and coverage maximization by running experiments on real (Twitter) and synthetic data in the context of real-time search over micro-posts. Finally we perform a user study validating our algorithms and diversity metrics