Ranking target objects of navigational queries

  • Authors:

  • Louiqa Raschid;Yao Wu;Woei-Jyh Lee;María Esther Vidal;Panayiotis Tsaparas;Padmini Srinivasan;Aditya Kumar Sehgal

  • Affiliations:

  • University of Maryland, College Park;University of Maryland, College Park;University of Maryland, College Park;Universidad Simón Bolívar, Caracas, Venezuela;University of Helsinki, Helsinki, Finland;The University of Iowa, Iowa City, USA;The University of Iowa, Iowa City, USA

  • Venue:
  • WIDM '06 Proceedings of the 8th annual ACM international workshop on Web information and data management
  • Year:
  • 2006

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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.