Building an Ontology for the Domain of Plant Science using ProtΓ©gΓ©
October 10, 2018 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Sara Hosseinzadeh Kassani, Peyman Hosseinzadeh Kassani
arXiv ID
1810.04606
Category
cs.IR: Information Retrieval
Citations
3
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Due to the rapid development of technology, large amounts of heterogeneous data generated every day. Biological data is also growing in terms of the quantity and quality of data considerably. Despite the attempts for building a uniform platform to handle data management in Plant Science, researchers are facing the challenge of not only accessing and integrating data stored in heterogeneous data sources but also representing the implicit and explicit domain knowledge based on the available plant genomic and phenomic data. Ontologies provide a framework for describing the structures and vocabularies to support the semantics of information and facilitate automated reasoning and knowledge discovery. In this paper, we focus on building an ontology for Arabidopsis Thaliana in Plant Science domain. The aim of this study is to provide a conceptual model of Arabidopsis Thaliana as a reference plant for botany and other plant sciences, including concepts and their relationships.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Information Retrieval
R.I.P.
π»
Ghosted
π
π
Old Age
Neural Graph Collaborative Filtering
R.I.P.
π»
Ghosted
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
R.I.P.
π»
Ghosted
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
R.I.P.
π
404 Not Found
Graph Neural Networks for Social Recommendation
R.I.P.
π»
Ghosted
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted