Applying ASP for Knowledge-Based Link Prediction with Explanation Generationin Feature-Rich Networks

Çiçek Güven, Martin Atzmueller, Dietmar Seipel

    Research output: Contribution to journalArticleScientificpeer-review

    Abstract

    Link prediction is challenging, especially based on (scarce) historic data or in cold start scenarios. In this paper, we show how to apply answer set programming (ASP) for formalizing link prediction in feature-rich networks, that is - in particular - using domain knowledge for network (and graph) analysis. We show, that applying ASP for link prediction provides a powerful declarative approach, as exemplified using simple predictors, and demonstrate according explanation generation using ASP. We present the application of the proposed methodological approach for explicative link prediction and analysis with explanation generation using different datasets.
    Original languageEnglish
    Pages (from-to)1305-1315
    JournalTransactions on Network Science and Engineering
    Volume8
    Issue number2
    DOIs
    Publication statusPublished - 2021

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