Text Cube: Computing IR Measures for Multidimensional Text Database Analysis
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Ranking-based clustering of heterogeneous information networks with star network schema
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Promotion analysis in multi-dimensional space
Proceedings of the VLDB Endowment
Mining knowledge from databases: an information network analysis approach
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Mining advisor-advisee relationships from research publication networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Mapping web pages to database records via link paths
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Growing parallel paths for entity-page discovery
Proceedings of the 20th international conference companion on World wide web
Unexpected results in automatic list extraction on the web
ACM SIGKDD Explorations Newsletter
Extracting general lists from web documents: a hybrid approach
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
Construction and analysis of web-based computer science information networks
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
Chronos: facilitating history discovery by linking temporal records
Proceedings of the VLDB Endowment
Tracking and analyzing TV content on the web through social and ontological knowledge
Proceedings of the 11th european conference on Interactive TV and video
The parallel path framework for entity discovery on the web
ACM Transactions on the Web (TWEB)
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WINACS (Web-based Information Network Analysis for Computer Science) is a project that incorporates many recent, exciting developments in data sciences to construct a Web-based computer science information network and to discover, retrieve, rank, cluster, and analyze such an information network. With the rapid development of the Web, huge amounts of information are available in the form of Web documents, structures, and links. It has been a dream of the database and Web communities to harvest such information and reconcile the unstructured nature of the Web with the neat, semi-structured schemas of the database paradigm. Taking computer science as a dedicated domain, WINACS first discovers related Web entity structures, and then constructs a heterogeneous computer science information network in order to rank, cluster and analyze this network and support intelligent and analytical queries.