Comparing Personalized Pagerank and Activation Spreading in Wikipedia Diagram-Based Search
This work was partially supported by the Intel Science and Technology Center for Big Data, through a gift from the University Industry Research Corporation.
2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL)
Diagram Navigation (DN) is based on using existing diagrams for a domain as maps to navigate and query a collection from different perspectives. With a relatively small number of manual connections, such as ones between diagram concepts and related documents, a domain expert can integrate their perspective of a domain (depicted in a diagram) into the navigation system of a collection. DN utilizes the abundance of internal connections in a collection, such as Wikipedia hyperlinks to access the entire collection. In a Diagram-to-Content (D2C) query, an end user selects a diagram concept to retrieve a ranked list of related collection documents. In a Content-to-Diagram (C2D) query, DN highlights related concepts in a diagram based on document(s) selected by the user. To increase D2C ranking performance, we study and tune Personalized PageRank and an energy-spreading algorithm. We report key differences in how the algorithms rank D2C queries. We show that the tested algorithms are affected differently by Wikipedia graph structures, such as categories and hyperlinks from article templates. We also show that diagrams not only can provide overviews, but they also positively bias the ranking of D2C queries.
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Benotman, H., & Maier, D. (2021, September). Comparing Personalized PageRank and Activation Spreading in Wikipedia Diagram-Based Search. In 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL) (pp. 41-50). IEEE.