11 Result(s)
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Chapter
Federated Learning over Harmonized Data Silos
Federated Learning is a distributed machine learning approach that enables geographically distributed data silos to collaboratively learn a joint machine learning model without sharing data. Most of the existi...
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Chapter and Conference Paper
Towards Sparsified Federated Neuroimaging Models via Weight Pruning
Federated training of large deep neural networks can often be restrictive due to the increasing costs of communicating the updates with increasing model sizes. Various model pruning techniques have been design...
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Chapter and Conference Paper
NSEEN: Neural Semantic Embedding for Entity Normalization
Much of human knowledge is encoded in text, available in scientific publications, books, and the web. Given the rapid growth of these resources, we need automated methods to extract such knowledge into machine...
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Chapter and Conference Paper
Abstract Meaning Representations as Linked Data
The complex relationship between natural language and formal semantic representations can be investigated by the development of large, semantically-annotated corpora. The “Abstract Meaning Representation” (AMR...
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Chapter and Conference Paper
Leveraging Linked Data to Discover Semantic Relations Within Data Sources
Map** data to a shared domain ontology is a key step in publishing semantic content on the Web. Most of the work on automatically map** structured and semi-structured sources to ontologies focuses on seman...
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Chapter and Conference Paper
Finding Concept Coverings in Aligning Ontologies of Linked Data
Despite the recent growth in the size of the Linked Data Cloud, the absence of links between the vocabularies of the sources has resulted in heterogenous schemas. Our previous work tried to find conceptual map...
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Chapter and Conference Paper
A Graph-Based Approach to Learn Semantic Descriptions of Data Sources
Semantic models of data sources and services provide support to automate many tasks such as source discovery, data integration, and service composition, but writing these semantic descriptions by hand is a ted...
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Chapter and Conference Paper
Semi-automatically Map** Structured Sources into the Semantic Web
Linked data continues to grow at a rapid rate, but a limitation of a lot of the data that is being published is the lack of a semantic description. There are tools, such as D2R, that allow a user to quickly co...
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Chapter and Conference Paper
Discovering Concept Coverings in Ontologies of Linked Data Sources
Despite the increase in the number of linked instances in the Linked Data Cloud in recent times, the absence of links at the concept level has resulted in heterogenous schemas, challenging the interoperability...
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Chapter and Conference Paper
Rapidly Integrating Services into the Linked Data Cloud
The amount of data available in the Linked Data cloud continues to grow. Yet, few services consume and produce linked data. There is recent work that allows a user to define a linked service from an online ser...
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Article
Compiling Source Descriptions for Efficient and Flexible Information Integration
Integrating data from heterogeneous data sources is a critical problem that has received a great deal of attention in recent years. There are two competing approaches to address this problem. The traditional a...
