Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/8096
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dc.contributor.authorShah, A.-
dc.contributor.authorBasile, V.-
dc.contributor.authorCabrio, E.-
dc.contributor.authorSowmya, Kamath S.-
dc.date.accessioned2020-03-30T10:18:04Z-
dc.date.available2020-03-30T10:18:04Z-
dc.date.issued2017-
dc.identifier.citationCEUR Workshop Proceedings, 2017, Vol.1935, , pp.1-10en_US
dc.identifier.urihttps://idr.nitk.ac.in/jspui/handle/123456789/8096-
dc.description.abstractObtaining and representing common-sense knowledge, useful in a robotics scenario for planning and making inference about the robots' surroundings, is a challenging problem, because such knowledge is typically found in unstructured repositories such as text corpora or small handmade resources. The work described in this paper presents a methodology for automatically creating a default knowledge base about real-world objects for the robotics domain. The proposed method relies on clustering frame instances extracted from natural language text as a way of distilling default knowledge. We collect and parse a natural language corpus using the Web as a source, then perform an agglomerative clustering of frame instances according to an appropriately defined similarity measure, and finally extract prototypical frame instances from each cluster and publish them in LOD-complaint format to promote reuse and interoperability.en_US
dc.titleFrame instance extraction and clustering for default knowledge buildingen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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