SPARQL Query-Builder for Medical Temporal Data

Vcelak, Petr; Kryl, Martin; Kleckova, Jana

doi:10.1109/cisp-bmei.2018.8633266

Cited by 3 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For an easier use of SPARQL features, Ferré [31] complemented their work with natural language verbalism and readability. Another textual-based GUI was SPARQL Query-Builder which generated the WHERE clause based on ontology classes and attributes [115]. Visual-based GUIs such as SparqlBlocks [17] and ViziQuer [16] provided different user experiences by minimising typing activity.…”

Section: Introductionmentioning

confidence: 99%

Smart Searching for the Physiome Project

Munarko

2024

Preprint

View full text Add to dashboard Cite

The Physiome Project's standards for biosimulation models in the Physiome Model Repository (PMR) aim to create a virtual physiological human. Despite an increase in models, users need help finding relevant ones. This thesis proposes approaches to aid search using semantic annotations and Natural Language Processing (NLP). The first approach is NLIMED, which converts free-text queries to SPARQL, while the second one is CASBERT, which encodes model entities for exploratory searches. Integrated into a web-based tool, CASBERT enables comprehensive searches, facilitating model discovery and maximizing FAIRness. These methods can be extended to other standards like SBML, promising multi-scale, multi-repository searches in the future. We further investigate CASBERT to identify unannotated entities by leveraging the hierarchical structure of the model. This exploration led to the development of a functional search tool called BMSE.

show abstract

Section: Introductionmentioning

confidence: 99%

Smart Searching for the Physiome Project

Munarko

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…For an easier use of SPARQL features, Ferré ( 2014 ) complemented their work with natural language verbalism and readability. Another textual-based GUI was SPARQL Query-Builder which generated the WHERE clause based on ontology classes and attributes (Vcelak et al, 2018 ). Visual-based GUIs such as SparqlBlocks (Ceriani and Bottoni, 2017 ) and ViziQuer (Čerāns et al, 2019 ) provided different user experiences by minimizing typing activity.…”

Section: Introductionmentioning

confidence: 99%

NLIMED: Natural Language Interface for Model Entity Discovery in Biosimulation Model Repositories

et al. 2022

View full text Add to dashboard Cite

Semantic annotation is a crucial step to assure reusability and reproducibility of biosimulation models in biology and physiology. For this purpose, the COmputational Modeling in BIology NEtwork (COMBINE) community recommends the use of the Resource Description Framework (RDF). This grounding in RDF provides the flexibility to enable searching for entities within models (e.g., variables, equations, or entire models) by utilizing the RDF query language SPARQL. However, the rigidity and complexity of the SPARQL syntax and the nature of the tree-like structure of semantic annotations, are challenging for users. Therefore, we propose NLIMED, an interface that converts natural language queries into SPARQL. We use this interface to query and discover model entities from repositories of biosimulation models. NLIMED works with the Physiome Model Repository (PMR) and the BioModels database and potentially other repositories annotated using RDF. Natural language queries are first “chunked” into phrases and annotated against ontology classes and predicates utilizing different natural language processing tools. Then, the ontology classes and predicates are composed as SPARQL and finally ranked using our SPARQL Composer and our indexing system. We demonstrate that NLIMED's approach for chunking and annotating queries is more effective than the NCBO Annotator for identifying relevant ontology classes in natural language queries.Comparison of NLIMED's behavior against historical query records in the PMR shows that it can adapt appropriately to queries associated with well-annotated models.

show abstract

“…For an easier use of SPARQL features, Ferré (2014) complemented their work with natural language verbalism and readability. Another textual-based GUI was SPARQL Query-Builder which generated the WHERE clause based on ontology classes and attributes (Vcelak et al, 2018). Visual-based GUIs such as SparqlBlocks (Ceriani and Bottoni, 2017) and ViziQuer ( Čerāns et al, 2019) provided different user experiences by minimising typing activity.…”

Section: Introductionmentioning

confidence: 99%

NLIMED: Natural Language Interface for Model Entity Discovery in Biosimulation Model Repositories

Munarko

Atalag

et al. 2019

Preprint

View full text Add to dashboard Cite

Motivation: Semantic annotation is a crucial step to assure reusability and reproducibility of biosimulation models in biology and physiology. For this purpose, the COmputational Modeling in BIology NEtwork (COMBINE) community recommend the use of the Resource Description Framework (RDF). The RDF implementation provides the flexibility of model entity searching (e.g. flux of sodium across apical plasma membrane) by utilising SPARQL. However, the rigidity and complexity of SPARQL syntax and the nature of semantic annotation which is not merely as a simple triple yet forming a tree-like structure may cause a difficulty. Therefore, the availability of an interface to convert a natural language query to SPARQL is beneficial. Results: We propose NLIMED, a natural language query to SPARQL interface to retrieve model entities from biosimulation models. Our interface can be applied to various repositories utilising RDF such as the PMR and Biomodels. We evaluate our interface by collecting RDF in the biosimulation models coded using CellML in PMR. First, we extract RDF as a tree structure and then store each subtree of a model entity as a modified triple of a model entity name, path, and class ontology into the RDF Graph Index. We also extract class ontology's textual metadata from the BioPortal and CellML and manage it in the Text Feature Index. With the Text Feature Index, we annotate phrases resulted by the NLQ Parser (Stanford parser or NLTK parser) into class ontologies. Finally, the detected class ontologies then are composed as SPARQL by incorporating the RDF Graph Index. Our annotator performance is far more powerful compared to the available service provided by BioPortal with F-measure of 0.756 and our SPARQL composer can find all possible SPARQL in the collection based on the annotation results. Currently, we already implement our interface in Epithelial Modelling Platform tool. Availability: https://github.com/napakalas/NLIMED Contact: ymun794@aucklanduni.ac.nz, d.nickerson@auckland.ac.nz

show abstract

SPARQL Query-Builder for Medical Temporal Data

Cited by 3 publications

References 11 publications

Smart Searching for the Physiome Project

Smart Searching for the Physiome Project

NLIMED: Natural Language Interface for Model Entity Discovery in Biosimulation Model Repositories

NLIMED: Natural Language Interface for Model Entity Discovery in Biosimulation Model Repositories

Contact Info

Product

Resources

About