A Collective, Probabilistic Approach to Schema Mapping: Appendix
In this appendix we provide additional supplementary material to "A Collective, Probabilistic Approach to Schema Mapping." We include an additional extended example, supplementary experiment details, and proof for the complexity result stated in the main paper.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
A Probabilistic Approach to Knowledge Translation
In this paper, we focus on a novel knowledge reuse scenario where the knowledge in the source schema needs to be translated to a semantically heterogeneous target schema. We refer to this task as "knowledge translation" …
Transfer LearningTranslationGeographical Hidden Markov Tree for Flood Extent Mapping (With Proof Appendix)
Flood extent mapping plays a crucial role in disaster management and national water forecasting. Unfortunately, traditional classification methods are often hampered by the existence of noise, obstacles and heterogeneity…
General ClassificationManagementFew-Shot Learning of Visual Compositional Concepts through Probabilistic Schema Induction
The ability to learn new visual concepts from limited examples is a hallmark of human cognition. While traditional category learning models represent each example as an unstructured feature vector, compositional concept …
Deep LearningFew-Shot LearningSpectral decomposition method of dialog state tracking via collective matrix factorization
The task of dialog management is commonly decomposed into two sequential subtasks: dialog state tracking and dialog policy learning. In an end-to-end dialog system, the aim of dialog state tracking is to accurately estim…
dialog state trackingManagementNatural Language Understandingspeech-recognition+1Making Sense of schema.org with WordNet
The schema.org initiative was designed to introduce machine readable metadata into the World Wide Web. This paper investigates conceptual biases in the schema through a mapping exercise between schema.org types and WordN…