Abstract
Question answering over knowledge graphs targets to leverage facts in knowledge graphs to answer natural language questions. The presence of large number of facts, particularly in huge and well-known knowledge graphs such as DBpedia, makes it difficult to access the knowledge graph for each given question. This paper describes a generic solution based on Personal Page Rank for extracting a small subset from the knowledge graph as a knowledge subgraph which is likely to capture the answer of the question. Given a natural language question, relevant facts are determined by a bi-directed propagation process based on Personal Page Rank. Experiments are conducted over FreeBase, DBPedia and WikiMovie to demonstrate the effectiveness of the approach in terms of recall and size of the extracted knowledge subgraphs.
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Notes
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The surface form of an edge is the value of rdfs:label if the edge does not have a label, the variable part of its URI is adopted as the surface form.
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The GrafNet repository on the Github is reused according to the proposed approach.
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Time complexity of NPR is O(m * n) [m = no. of iterations, n= no. of nodes].
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After constructing knowledge subgraphs, GAQA obtains answers of given questions over the extracted subgraphs based a graph-alignment method and then reports the results.
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The task of identifying query pattern needs end users’ assistance in GAQA.
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Acknowledgement
This research has been supported by the project WordLiftNG within the Eureka, Eurostars Programme (grant agreement number 877857 with the Austrian Research Promotion Agency (FFG)) and the project KI-NET within the Interreg Osterreich-Bayern 2014–2020 programme (grant agreement number AB 292).
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Aghaei, S., Angele, K., Fensel, A. (2022). Building Knowledge Subgraphs in Question Answering over Knowledge Graphs. In: Di Noia, T., Ko, IY., Schedl, M., Ardito, C. (eds) Web Engineering. ICWE 2022. Lecture Notes in Computer Science, vol 13362. Springer, Cham. https://doi.org/10.1007/978-3-031-09917-5_16
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