Interpreting Linked Data Search Results using Markov Logic

Linked Data (LD) follows the web in providing low barriers to publication, and in deploying web-scale keyword search as a central way of identifying relevant data.As in the web, searches initially identify results in broadly the form in which they were published, and the published form may be provided to the user as the result of a search.This will be satisfactory in some cases, but the diversity of publishers means that the results of the search may be obtained from many different sources, and described in many different ways.As such, there seems to be an opportunity to add value to search results by providing users with an integrated representation that brings together features from different sources.This involves an on-the-fly and automated data integration process being applied to search results, which raises the question as to what technologies might be most suitable for supporting the integration of LD search results.In this paper, we investigate the use of Markov Logic, which brings together first order logic and probabilistic graphical models to support both learning and inference in uncertain domains.Specifically, we: (i) characterise key features of LD search results that are relevant to their integration; (ii) discuss how these motivate the use of an approach based on Markov Logic; (iii) describe some initial experiences in the use of Markov Logic for interpreting search results; and (iv) present some avenues for future investigation.