Linked Context: A Linked Data Approach to Personalised Service Provisioning

Zhao, Xia; Reiff-Marganiec, Stephan; Domingue, John

doi:10.1109/icws.2012.16

Cited by 8 publications

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the processes are mostly hard-coded to match the logic framework. • The other trend is to apply Semantic Web standards for semantically encoding services description and their QoS properties (Semantic Web Services SWS) [33,34]. The main benefit is that semantic annotation has an embedded logical reasoning framework to deal with composition tasks.…”

Section: Services and Machine Learning Ontologiesmentioning

confidence: 99%

AIMS: An Automatic Semantic Machine Learning Microservice Framework to Support Biomedical and Bioengineering Research

O'Neill¹,

Kermanizadeh²

2023

Preprint

View full text Add to dashboard Cite

The fusion of machine learning, biomedical, and bioengineering research offers novel ways to understand, diagnose, and treat various health conditions. However, the complexities of biomedical and bioengineering data, coupled with the intricate process of developing and deploying machine learning solutions, often pose significant challenges to researchers in these fields. We introduce a new Automatic Semantic Machine Learning Microservice Framework (AIMS) that addresses these challenges by automating key aspects of the machine learning pipeline, from data preprocessing to model evaluation. Our proposed framework emphasizes model interpretability, the integration of domain knowledge, and the efficient handling of biomedical data. Furthermore, it incorporates self-supervised knowledge learning strategies, enabling the automatic generation, optimization, and continuous adaptation of machine learning models in response to new tasks and data. We illustrate the potential of our framework through three case studies in biomedical research, showcasing its capacity to streamline research processes, improve scientific exploration quality, and provide a foundation for significant breakthroughs.

show abstract

Section: Services and Machine Learning Ontologiesmentioning

confidence: 99%

AIMS: An Automatic Semantic Machine Learning Microservice Framework to Support Biomedical and Bioengineering Research

O'Neill¹,

Kermanizadeh²

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Logic is processing data from sensors and facilitates decision making about the current user's context. [23], [51], [12], [59] describe various approaches in that regard. A survey on context data distribution for mobile ubiquitous devices is also described in [4].…”

Section: Ubiquitous Devicementioning

confidence: 99%

Ubiquitous Semantic Applications

Ermilov

Khalili

Auer

2014

International Journal on Semantic Web and Information Systems

View full text Add to dashboard Cite

Recently practical approaches for development of ubiquitous semantic applications have made quite some progress. In particular in the area of the ubiquitous access to the semantic data the authors recently observed a large number of approaches, systems and applications being described in the literature. With this survey the authors aim to provide an overview on the rapidly emerging field of Ubiquitous Semantic Applications (UbiSA). The authors conducted a systematic literature review comprising a thorough analysis of 48 primary studies out of 172 initially retrieved papers. The authors obtained a comprehensive set of quality attributes for UbiSA together with corresponding application features suggested for their realization. The quality attributes include aspects such as mobility, usability, heterogeneity, collaboration, customizability and evolvability. The primary studies were surveyed in the light of these quality attributes and the authors performed a thorough analysis of five ubiquitous semantic applications, six frameworks for UbiSA, three UbiSA specific ontologies, five ubiquitous semantic systems and nine general approaches. The proposed quality attributes facilitate the evaluation of existing approaches and the development of novel, more effective and intuitive UbiSA.

show abstract

“…The other trend is to apply semantic web standards for semantically encoding service descriptions and their QoS properties (semantic web services (SWS)) [33,34]. The main benefit is that semantic annotation has an embedded logical reasoning framework to deal with composition tasks.…”

mentioning

confidence: 99%

AIMS: An Automatic Semantic Machine Learning Microservice Framework to Support Biomedical and Bioengineering Research

Yu,

O’Neill,

Kermanizadeh

2023

Bioengineering

Self Cite

View full text Add to dashboard Cite

The fusion of machine learning and biomedical research offers novel ways to understand, diagnose, and treat various health conditions. However, the complexities of biomedical data, coupled with the intricate process of developing and deploying machine learning solutions, often pose significant challenges to researchers in these fields. Our pivotal achievement in this research is the introduction of the Automatic Semantic Machine Learning Microservice (AIMS) framework. AIMS addresses these challenges by automating various stages of the machine learning pipeline, with a particular emphasis on the ontology of machine learning services tailored to the biomedical domain. This ontology encompasses everything from task representation, service modeling, and knowledge acquisition to knowledge reasoning and the establishment of a self-supervised learning policy. Our framework has been crafted to prioritize model interpretability, integrate domain knowledge effortlessly, and handle biomedical data with efficiency. Additionally, AIMS boasts a distinctive feature: it leverages self-supervised knowledge learning through reinforcement learning techniques, paired with an ontology-based policy recording schema. This enables it to autonomously generate, fine-tune, and continually adapt to machine learning models, especially when faced with new tasks and data. Our work has two standout contributions demonstrating that machine learning processes in the biomedical domain can be automated, while integrating a rich domain knowledge base and providing a way for machines to have self-learning ability, ensuring they handle new tasks effectively. To showcase AIMS in action, we have highlighted its prowess in three case studies of biomedical tasks. These examples emphasize how our framework can simplify research routines, uplift the caliber of scientific exploration, and set the stage for notable advances.

show abstract

Linked Context: A Linked Data Approach to Personalised Service Provisioning

Cited by 8 publications

References 17 publications

AIMS: An Automatic Semantic Machine Learning Microservice Framework to Support Biomedical and Bioengineering Research

AIMS: An Automatic Semantic Machine Learning Microservice Framework to Support Biomedical and Bioengineering Research

Ubiquitous Semantic Applications

AIMS: An Automatic Semantic Machine Learning Microservice Framework to Support Biomedical and Bioengineering Research

Contact Info

Product

Resources

About