Special Issue on Semantic Deep Learning at the Semantic Web Journal

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Semantic Web technologies and deep learning share the goal of creating intelligent artifacts that emulate human capacities such as reasoning, validating, and predicting. Both fields have been impacting data and knowledge analysis considerably as well as their associated abstract representations. Deep learning is a term used to refer to deep neural network algorithms that learn data representations by means of transformations with multiple processing layers. These architectures have frequently been applied in NLP to feature learning from raw data, such as part-of-speech-tagging, morphological tagging, language modeling, and so forth. Semantic Web technologies and knowledge representation, on the other hand, boost the re-use and sharing of knowledge in a structured and machine readable fashion. Semantic resources such as WikiData, Yago, BabelNet or DBpedia, as well as knowledge base construction and completion methods have been successfully applied to improved systems addressing semantically intensive tasks (e.g. Question Answering).

Topics include, but are not limited to:

Structured knowledge in deep learning:
— learning and applying knowledge graph embeddings
— applications of knowledge-rich embeddings
— neural networks and logic rules
— learning semantic similarity and encoding distances as knowledge graph
— ontology-based text classification
— multilingual resources for neural representations of linguistics
— semantic role labeling

Deep reasoning and inferences:
— commonsense reasoning and vector space models
— reasoning with deep learning methods
Learning knowledge representations with deep learning:
— word embeddings for ontology matching and alignment
— deep learning and semantic web technologies for specialized domains
— deep learning ontologies
— deep learning models for learning knowledge representations from text
— deep learning ontological annotations

Joint tasks:
-mining multilingual natural language for SPARQL queries
-information retrieval and extraction with knowledge graphs and deep learning models
-knowledge-based deep word sense disambiguation and entity linking
-investigation of compatibilities and incompatibilities between deep learning and Semantic

Web approaches:
-neural networks for learning Linked Data

Submission deadline: 28 February 2018. Papers submitted before the deadline will be reviewed upon receipt.

Submission Instructions:

Guest Editors:
The guest editors can be reached at semdeepgooglegroups.com.
Luis Espinosa Anke, Cardiff University, UK
Thierry Declerck, DFKI GmbH, Germany
Dagmar Gromann, Technical University Dresden, Germany

Об авторе Лидия Пивоварова

СПбГУ - старший преподаватель, University of Helsinki - PhD student http://philarts.spbu.ru/structure/sub-faculties/itah_phil/teachers/pivovarova
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