Abstract
Recent efforts towards digitization of cultural heritage artifacts have resulted in a surge of information around these artifacts. However, the organization of these artifacts falls short with respect to accessing the facts across these entities. In this paper, we present a method to harvest the knowledge and form a knowledge graph from the digitized artifacts in the Museums of India repository via distant supervision to enable better accessibility of the facts and ability to extract new insights around the artifacts. Triples extracted from an open information extractor are first canonicalized to a standard taxonomy based on a metric-based scoring. Since a standard taxonomy is insufficient to capture all the relationships, we propose a sequential clustering based approach to add artifact specific relationships to the taxonomy (and to the knowledge graph). The graph is enriched by inferring missing facts based on a probabilistic soft logic approach seeded from a frequent item set framework. Human evaluation of the final knowledge graph showed an accuracy of \(75\%\) on par with knowledge bases like DBpedia.
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Sancheti, A., Maheshwari, P., Chaturvedi, R., Monsy, A.V., Goyal, T., Srinivasan, B.V. (2018). Harvesting Knowledge from Cultural Heritage Artifacts in Museums of India. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10938. Springer, Cham. https://doi.org/10.1007/978-3-319-93037-4_25
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