(PDF) An ontology-based approach to auto-tagging articles
PDF | This paper proposes an auto-tagging methodology using tags defined in the ontology. The auto-tagging methodology consists of two main processes:... | Find, read and cite all the research you need on ResearchGate
Formalizing Tag-Based Metadata With the Brick Ontology
Current efforts establishing semantic metadata standards for the built environment span academia, industry and standards bodies. For these standards to be effective, they must be clearly defined and easily extensible, encourage consistency in their usage, and integrate cleanly with existing industrial standards, such as BACnet. There is a natural tension between informal tag-based systems that rely upon idiom and convention for meaning, and formal ontologies amenable to automated tooling. We present a qualitative analysis of Project Haystack, a popular tagging system for building metadata, and identify a family of inherent interpretability and consistency issues in the tagging model that stem from its lack of a formal definition. To address these issues, we present the design and implementation of the Brick+ ontology, a drop-in replacement for Brick with clear formal semantics that enables the inference of a valid Brick model from an informal Haystack model, and demonstrate this inference across five Haystack models.
A Review of Ontology‐Based Tag Recommendation Approaches
Tag recommender schemes suggest related tags for an untagged resource and better tag suggestions to tagged resources. Tagging is very important if the user identifies the tag that is more precise to ...
An open source taxonomy for the future of education. Mapping the learning and talent innovation landscape. - Pull requests · HolonIQ/learning-landscape
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GitHub - HolonIQ/learning-landscape: An open source taxonomy for the future of education. Mapping the learning and talent innovation landscape.
An open source taxonomy for the future of education. Mapping the learning and talent innovation landscape. - GitHub - HolonIQ/learning-landscape: An open source taxonomy for the future of education...
(PDF) CareerVis: Hierarchical Visualization of Career Pathway Data
PDF | We present our CareerVis system, an interactive visualization tool to aid career education for high school and freshman college students. In... | Find, read and cite all the research you need on ResearchGate
Using predicate and provenance information from a knowledge graph for drug efficacy screening | Journal of Biomedical Semantics | Full Text
Background Biomedical knowledge graphs have become important tools to computationally analyse the comprehensive body of biomedical knowledge. They represent knowledge as subject-predicate-object triples, in which the predicate indicates the relationship between subject and object. A triple can also contain provenance information, which consists of references to the sources of the triple (e.g. scientific publications or database entries). Knowledge graphs have been used to classify drug-disease pairs for drug efficacy screening, but existing computational methods have often ignored predicate and provenance information. Using this information, we aimed to develop a supervised machine learning classifier and determine the added value of predicate and provenance information for drug efficacy screening. To ensure the biological plausibility of our method we performed our research on the protein level, where drugs are represented by their drug target proteins, and diseases by their disease proteins. Results Using random forests with repeated 10-fold cross-validation, our method achieved an area under the ROC curve (AUC) of 78.1% and 74.3% for two reference sets. We benchmarked against a state-of-the-art knowledge-graph technique that does not use predicate and provenance information, obtaining AUCs of 65.6% and 64.6%, respectively. Classifiers that only used predicate information performed superior to classifiers that only used provenance information, but using both performed best. Conclusion We conclude that both predicate and provenance information provide added value for drug efficacy screening.
By Rob Hill, CRO at ProFinda Can a Machine Learning powered Knowledge Graph transform Talent Acquisition? The true transformation of talent acquisition will only occur when we start to focus the power of Machine Intelligence on understanding and mapping out the knowledge contained within an organisation. This can be achieved by building a knowledge graph of … The power of Machine Learning to drive Talent Acquisition Read More »
