NDLI: Ranking target objects of navigational queries

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Ranking target objects of navigational queries

Content Provider	ACM Digital Library
Author	Vidal, María Esther Raschid, Louiqa Sehgal, Aditya Kumar Srinivasan, Padmini Wu, Yao Lee, Woei-Jyh Tsaparas, Panayiotis
Abstract	Web navigation plays an important role in exploring public interconnected data sources such as life science data. A navigational query in the life science graph produces a result graph which is a layered directed acyclic graph (DAG). Traversing the result paths in this graph reaches a target object set (TOS). The challenge for ranking the target objects is to provide recommendations that re ect the relative importance of the retrieved object, as well as its relevance to the specific query posed by the scientist. We present a metric layered graph PageRank (lgPR) to rank target objects based on the link structure of the result graph. LgPR is a modification of PageRank; it avoids random jumps to respect the path structure of the result graph. We also outline a metric layered graph ObjectRank (lgOR) which extends the metric ObjectRank to layered graphs. We then present an initial evaluation of lgPR. We perform experiments on a real-world graph of life sciences objects from NCBI and report on the ranking distribution produced by lgPR. We compare lgPR with PageRank. In order to understand the characteristics of lgPR, an expert compared the Top K target objects (publications in the PubMed source) produced by lgPR and a word-based ranking method that uses text features extracted from an external source (such as Entrez Gene) to rank publications.
Starting Page	27
Ending Page	34
Page Count	8
File Format	PDF
ISBN	1595935258
DOI	10.1145/1183550.1183558
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2006-11-10
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Navigational query Ranking Link analysis Pagerank
Content Type	Text
Resource Type	Article

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