Using Semantic Web Technologies to Underpin the SNOMED CT Query Language

Casteleiro, Mercedes Argüello; Tsarkov, Dmitry; Parsia, Bijan; Sattler, Ulrike

doi:10.1007/978-3-319-71078-5_20

Cited by 4 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Casteleiro et.al. [2] deals with two different types of models in this work namely generative and selective model. Generative models use Recurrent Neural Network where hidden state is designed based on the sense of the context.…”

Section: Review Of Literaturesmentioning

confidence: 99%

Asynchronous Learning of Personal Assistant from User Histories Stored in Cloud

2019

IJITEE

View full text Add to dashboard Cite

A personal assistant always provides us with a daily schedule, remembers all the events and intimates us at right time. Most of the times, our routine jobs are forgotten and user may get to know new information about our most obsessed things later only. Not everyone could afford a personal assistant to help with the daily activities or help the executive in remembering him/her about the activities. Due to heavy industrious works, a human mind naturally tend lose its integrity to forget most important tasks which may lead in missing flight timings or losing a relationship or even millions of amount. People most of the time tend to know the newer updates or information about the things they have been searching for only when they go looking for it again or when they see in others post. The proposed personal assistant system called Naturally Artificial Sanguine and Tangled Youngster (N.A.S.T.Y) does not violate any of the privacy issues and helps the user to remember their search topics and mobile applications usage statistics. The system has asynchronous method run continuously in the cloud server and finds the user's recent favourites and activities and stores that in separate PostgreSQL database table. To find the activities and favourites of the user, an android application is developed using Android Studio which gathers the usage statistics of the user's phone. The user wants to search any content over the web the proposed system helps to do it efficiently. The summarized text is shown as output in user mobile phone.

show abstract

Section: Review Of Literaturesmentioning

confidence: 99%

Asynchronous Learning of Personal Assistant from User Histories Stored in Cloud

2019

IJITEE

View full text Add to dashboard Cite

show abstract

“…One potential direction to address this issue is an amalgamation of semantics (i.e., creating meaningful structures) and capabilities of artificial intelligence. Therefore, our target is merging two prominent areas in the contemporary web landscape, i.e., web semantics and web mining (Casteleiro et al, 2017). Ontology plays an important role in improving our understanding of web data.…”

Section: Introductionmentioning

confidence: 99%

“…Ontology plays an important role in improving our understanding of web data. Nowadays, Ontology is hugely being utilized in various research and applications domains such as including artificial intelligence, web semantics, and natural language processing (Casteleiro et al, 2017). Two implementations based on ontological representations of SNOMED CT are proposed, one based on the OWL API and the other on the W3C SPARQL 1.1 query language.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Semantic Web Retrieval Through Ontology-Driven Feature Extraction: A Novel Proposition

Khan,

Sharif,

Mehmood

et al. 2024

Journal of Computer Science

View full text Add to dashboard Cite

Web images represent unstructured data sets which often lead to challenges when users try to locate distinct images via text-based searches on the web. Such difficulties stem from different factors, e.g., redundant image storage, irrelevant metadata tags, and incorrect associations. To overcome this issue, we propose a semantic model based on the ontology language that enables users to find images that exactly match their queries. The proposed technique employs a simple procedure where users generate image captions by constructing an ontology for each image in the repository. In order to fit the existing ontology domains, the ontology generation relies on information gathered from the image's visual and textual elements, including low-level features like color, name, and shape. Next, constructing the ontology establishes accurate relationships with existing ontology concepts using the "an" and "is a part of" relationships. The resulting text with immersed ontology information yields accurate results, leading to easy retrieval using semantics keyword searches. Our framework relies on two main ontology concepts, i.e., animals and vehicles. In this study, we used a dataset of MAT files comprising images, content, and information to study the ontology of animals (e.g., wolves, foxes, and dogs) as well as the ontology of vehicles. The overall comparative evaluation of the proposed framework was performed under various conditions to obtain valuable insights.

show abstract

“…The main one is related to inference performance over the EL+ subset used in SNOMED CT. Previous studies [47,48] showed big differences on query execution times between reasoners even for simple queries, such as computing the descendants of a concept. Moreover, in some cases, execution times were significantly high.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, one of the studies [48] showed a correlation between the execution times of the queries and the number of returned subclasses. Additionally, when translating the SNOMED CT Query Language into SPARQL 1.1 under the RDF entailment regime [47], the authors found that the significantly larger number of pre-computed files required to generate the inferred model was a drawback. Considering the previous shortcomings and the performance of graph databases, it was determined that Neo4j and Cypher could be used for the execution of ECs [49].…”

Section: Discussionmentioning

confidence: 99%

Enrichment of Archetypes with Domain Knowledge to Enhance the Consistency of Electronic Health Records

Solano¹

View full text Add to dashboard Cite

show abstract

Using Semantic Web Technologies to Underpin the SNOMED CT Query Language

Cited by 4 publications

References 15 publications

Asynchronous Learning of Personal Assistant from User Histories Stored in Cloud

Asynchronous Learning of Personal Assistant from User Histories Stored in Cloud

Enhancing Semantic Web Retrieval Through Ontology-Driven Feature Extraction: A Novel Proposition

Enrichment of Archetypes with Domain Knowledge to Enhance the Consistency of Electronic Health Records

Contact Info

Product

Resources

About