The anatomy of a large-scale hypertextual Web search engine
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Authoritative sources in a hyperlinked environment
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Learning domain-independent string transformation weights for high accuracy object identification
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XRANK: ranked keyword search over XML documents
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Mining data records in Web pages
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Learning block importance models for web pages
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Link fusion: a unified link analysis framework for multi-type interrelated data objects
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2D Conditional Random Fields for Web information extraction
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Ranking objects based on relationships
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CiteSeerχ: a scalable autonomous scientific digital library
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Contextual search and name disambiguation in email using graphs
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Ranking web objects from multiple communities
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Pseudo-anchor text extraction for searching vertical objects
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Web projections: learning from contextual subgraphs of the web
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Dynamic personalized pagerank in entity-relation graphs
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Shine: search heterogeneous interrelated entities
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Learning to rank typed graph walks: local and global approaches
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Identification of time-varying objects on the web
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Representing and Quantifying Rank - Change for the Web Graph
Algorithms and Models for the Web-Graph
Characterizing and predicting community members from evolutionary and heterogeneous networks
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SESQ: A Model-Driven Method for Building Object Level Vertical Search Engines
ER '08 Proceedings of the 27th International Conference on Conceptual Modeling
Identifying Potentially Important Concepts and Relations in an Ontology
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
A User Reputation Model for DLDE Learning 2.0 Community
ICADL 08 Proceedings of the 11th International Conference on Asian Digital Libraries: Universal and Ubiquitous Access to Information
Effective latent space graph-based re-ranking model with global consistency
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RankClus: integrating clustering with ranking for heterogeneous information network analysis
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Webpage understanding: beyond page-level search
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PathRank: Web Page Retrieval with Navigation Path
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A generalized Co-HITS algorithm and its application to bipartite graphs
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Ranking-based clustering of heterogeneous information networks with star network schema
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Query result clustering for object-level search
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WIT: web people search disambiguation using random walks
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A general markov framework for page importance computation
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A graphical framework for contextual search and name disambiguation in email
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A new digital literature retrieval system based on information analysis
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Anchor text extraction for academic search
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A brief survey of computational approaches in social computing
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Language models for web object retrieval
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Popularity weighted ranking for academic digital libraries
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Using structured tokens to identify webpages for data extraction
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Context-aware citation recommendation
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Yet another paper ranking algorithm advocating recent publications
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A combined approach for evaluating papers, authors and scientific journals
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Meta-search based web resource discovery for object-level vertical search
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Hierarchical link analysis for ranking web data
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iHypR: Prominence ranking in networks of collaborations with hyperedges1
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Hybrid entity clustering using crowds and data
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A review of ranking approaches for semantic search on Web
Information Processing and Management: an International Journal
SPRING: ranking the results of SPARQL queries on linked data
Proceedings of the 17th International Conference on Management of Data
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In contrast with the current Web search methods that essentially do document-level ranking and retrieval, we are exploring a new paradigm to enable Web search at the object level. We collect Web information for objects relevant for a specific application domain and rank these objects in terms of their relevance and popularity to answer user queries. Traditional PageRank model is no longer valid for object popularity calculation because of the existence of heterogeneous relationships between objects. This paper introduces PopRank, a domain-independent object-level link analysis model to rank the objects within a specific domain. Specifically we assign a popularity propagation factor to each type of object relationship, study how different popularity propagation factors for these heterogeneous relationships could affect the popularity ranking, and propose efficient approaches to automatically decide these factors. Our experiments are done using 1 million CS papers, and the experimental results show that PopRank can achieve significantly better ranking results than naively applying PageRank on the object graph.