HAR: Hub, Authority and Relevance Scores in Multi-Relational Data for Query Search

@inproceedings{Li2012HARHA,
  title={HAR: Hub, Authority and Relevance Scores in Multi-Relational Data for Query Search},
  author={Xutao Li and Michael K. Ng and Yunming Ye},
  booktitle={SDM},
  year={2012}
}
In this paper, we propose a framework HAR to study the hub and authority scores of objects, and the relevance scores of relations in multi-relational data for query search. The basic idea of our framework is to consider a random walk in multi-relational data, and study in such random walk, limiting probabilities of relations for relevance scores, and of objects for hub scores and authority scores. The main contribution of this paper is to (i) propose a framework (HAR) that can compute the hub… 

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References

SHOWING 1-10 OF 30 REFERENCES
MultiRank: co-ranking for objects and relations in multi-relational data
TLDR
The proposed framework (MultiRank) to determine the importance of both objects and relations simultaneously based on a probability distribution computed from multi-relational data and an efficient iterative algorithm to solve a set of tensor (multivariate polynomial) equations to obtain such probability distribution is proposed.
SALSA: the stochastic approach for link-structure analysis
TLDR
It is proved that SALSA is quivalent to a weighted in degree analysis of the link-sturcutre of WWW subgraphs, making it computationally more efficient than the Mutual reinforcement approach, and comparisions reveal a topological Phenomenon called the TKC effect which prevents the Mutual Reinforcement approach from identifying meaningful authorities.
Topic Distillation with Query-Dependent Link Connections and Page Characteristics
TLDR
Experiments with the TREC .GOV collection, an 18GB crawl of the US .gov domain from 2002, show that using external sources of evidence can significantly improve search effectiveness, with combinations of evidence leading to significant performance gains over both full-text and anchor-text baselines.
TOPDIS: Tensor-based Ranking for Data Search and Navigation
TLDR
T TOPDIS, a set of algorithmic tools to determine prominent elements in large semantic graphs is introduced, a formalisation of the method using the concepts of multilinear algebra, evaluate scalability of the algorithm and quality of the results, and show how TOPDIS can improve search over structured data in general.
Topic-sensitive PageRank
TLDR
A set of PageRank vectors are proposed, biased using a set of representative topics, to capture more accurately the notion of importance with respect to a particular topic, and are shown to generate more accurate rankings than with a single, generic PageRank vector.
Respect my authority!: HITS without hyperlinks, utilizing cluster-based language models
TLDR
It is found that the cluster-document graphs presented give rise to much better retrieval performance than previously proposed document-only graphs do and that computing authority scores for clusters constitutes an effective method for identifying clusters containing a large percentage of relevant documents.
A generalized Co-HITS algorithm and its application to bipartite graphs
TLDR
This paper proposes a novel and general Co-HITS algorithm to incorporate the bipartite graph with the content information from both sides as well as the constraints of relevance, and investigates the algorithm based on two frameworks, including the iterative and the regularization frameworks.
MetaFac: community discovery via relational hypergraph factorization
TLDR
The proposed MetaFac (MetaGraph Factorization), a framework that extracts community structures from various social contexts and interactions, outperform baseline methods by an order of magnitude and is able to extract meaningful communities based on the social media contexts.
TripleRank: Ranking Semantic Web Data by Tensor Decomposition
TLDR
This paper presents TripleRank, a novel approach for faceted authority ranking in the context of RDF knowledge bases that captures the additional latent semantics of Semantic Web data by means of statistical methods in order to produce richer descriptions of the available data.
CubeSVD: a novel approach to personalized Web search
TLDR
Experimental evaluations using a real-world data set collected from an MSN search engine show that CubeSVD achieves encouraging search results in comparison with some standard methods.
...
...