Flexible and efficient querying and ranking on hyperlinked data sources

Varadarajan, Ramakrishna; Hristidis, Vagelis; Raschid, Louiqa; Vidal, María-Esther; Ibáñez, Luis; Rodríguez-Drumond, Héctor

doi:10.1145/1516360.1516425

Cited by 13 publications

(15 citation statements)

References 19 publications

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“…BinRank [9] stores subgraphs such that any keyword query can be answered by performing ObjectRank on one subgraph. lgOR [5] is an extension of ObjectRank that is tailored to a layered graph; this is a restricted graph where there can only be edges between nodes in adjacent layers. A graphsampling technique is applied to approximate the lgOR scores.…”

Section: Approximation Methodsmentioning

confidence: 99%

“…The success of PageRank [1] in ranking Web pages resulted in many flavors of authority flow-based ranking techniques for data in entity-relationship graphs [2], [3], [4], [5]. A key feature of ranking in entity-relationship graphs is that they provide intuitive personalization opportunities by adjusting the authority flow parameters associated with each edge type or relationship type.…”

Section: Introductionmentioning

confidence: 99%

“…As explained in Varadarajan et al [5], a biologist user may assign more importance (higher edge weight) to a protein-to-protein edge type, whereas another user may assign a higher importance to a paper-to-paper citation edge type.…”

Section: Introductionmentioning

confidence: 99%

“…illustrates how a user interacts with the ranking system at precomputation and query times. During precomputation, the user-specific personalized WAV can be computed in two ways: (a) It can be set for each user by domain experts according to the user's profile [5], [6]. (b) WAV Θ is learned automatically by interacting with the user and exploiting user relevance feedback as explained in [16].…”

Section: Introductionmentioning

confidence: 99%

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Efficient Ranking on Entity Graphs with Personalized Relationships

Hristidis

Raschid

2014

IEEE Trans. Knowl. Data Eng.

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Abstract-Authority flow techniques like PageRank and ObjectRank can provide personalized ranking of typed entity-relationship graphs. There are two main ways to personalize authority flow ranking: Node-based personalization, where authority originates from a set of user-specific nodes; Edge-based personalization, where the importance of different edge types is user-specific. We propose the first approach to achieve efficient edge-based personalization using a combination of precomputation and runtime algorithms.In particular, we apply our method to ObjectRank, where a personalized weight assignment vector (WAV) assigns different weights to each edge type or relationship type. Our approach includes a repository of rankings for various WAVs. We consider the following two classes of approximation: (a) SchemaApprox is formulated as a distance minimization problem at the schema level; (b) DataApprox is a distance minimization problem at the data graph level. SchemaApprox is not robust since it does not distinguish between important and trivial edge types based on the edge distribution in the data graph. In contrast, DataApprox has a provable error bound. Both SchemaApprox and DataApprox are expensive so we develop efficient heuristic implementations, ScaleRank and PickOne respectively. Extensive experiments on the DBLP data graph show that ScaleRank provides a fast and accurate personalized authority flow ranking.

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Section: Approximation Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Ranking on Entity Graphs with Personalized Relationships

Hristidis

Raschid

2014

IEEE Trans. Knowl. Data Eng.

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“…Expressive query languages and effective ranking techniques are required to convert this data into browsable knowledge. We propose the Graph Information Discovery (GID) framework [VHR+09] to support sophisticated user queries on a rich web of annotated and hyperlinked data entries, where query answers need to be ranked in terms of some customized ranking criteria, e.g., PageRank or ObjectRank.…”

Section: Graph Information Discovery (Gid)mentioning

confidence: 99%