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

2019 ◽  
Author(s):  
Yuda Munarko ◽  
Dewan M. Sarwar ◽  
Koray Atalag ◽  
David P. Nickerson
Keyword(s):  
Natural Language ◽  
Semantic Annotation ◽  
Apical Plasma Membrane ◽  
Rdf Graph ◽  
Text Feature ◽  
Natural Language Interface ◽  
Natural Language Query ◽  
Description Framework ◽  
Entity Discovery ◽  
Resource Description

AbstractMotivationSemantic 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.ResultsWe 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.Availabilityhttps://github.com/napakalas/NLIMED

scholarly journals Semantic Interoperability in Heterogeneous IoT Infrastructure for Healthcare

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Sohail Jabbar ◽  
Farhan Ullah ◽  
Shehzad Khalid ◽  
Murad Khan ◽  
Kijun Han
Keyword(s):  
Semantic Annotation ◽  
Semantic Interoperability ◽  
Data Resource ◽  
Rdf Graph ◽  
Heterogeneous Devices ◽  
Data Formats ◽  
Significant Burden ◽  
Description Framework ◽  
Iot Devices ◽  
Resource Description

Interoperability remains a significant burden to the developers of Internet of Things’ Systems. This is due to the fact that the IoT devices are highly heterogeneous in terms of underlying communication protocols, data formats, and technologies. Secondly due to lack of worldwide acceptable standards, interoperability tools remain limited. In this paper, we proposed an IoT based Semantic Interoperability Model (IoT-SIM) to provide Semantic Interoperability among heterogeneous IoT devices in healthcare domain. Physicians communicate their patients with heterogeneous IoT devices to monitor their current health status. Information between physician and patient is semantically annotated and communicated in a meaningful way. A lightweight model for semantic annotation of data using heterogeneous devices in IoT is proposed to provide annotations for data. Resource Description Framework (RDF) is a semantic web framework that is used to relate things using triples to make it semantically meaningful. RDF annotated patients’ data has made it semantically interoperable. SPARQL query is used to extract records from RDF graph. For simulation of system, we used Tableau, Gruff-6.2.0, and Mysql tools.

Integrating Heterogeneous Enterprise Data Using Ontology in Supply Chain Management

2019 ◽  
pp. 71-102
Author(s):  
Kamalendu Pal
Keyword(s):  
Relational Databases ◽  
Query Languages ◽  
Heterogeneous Data ◽  
Integration Framework ◽  
Rdf Graph ◽  
Semantic Web Technology ◽  
Business Operations ◽  
Semantic Translation ◽  
Description Framework ◽  
Resource Description

Many industries prefer worldwide business operations due to the economic advantage of globalization on product design and development. These industries increasingly operate globalized multi-tier supply chains and deliver products and services all over the world. This global approach produces huge amounts of heterogeneous data residing at various business operations, and the integration of these data plays an important role. Integrating data from multiple heterogeneous sources need to deal with different data models, database schema, and query languages. This chapter presents a semantic web technology-based data integration framework that uses relational databases and XML data with the help of ontology. To model different source schemas, this chapter proposes a method based on the resource description framework (RDF) graph patterns and query rewriting techniques. The semantic translation between the source schema and RDF ontology is described using query and transformational language SPARQL.

An integration approach of multi-source heterogeneous fuzzy spatiotemporal data based on RDF

2021 ◽  
Vol 40 (1) ◽  
pp. 1065-1082
Author(s):  
Luyi Bai ◽  
Nan Li ◽  
Huilei Bai
Keyword(s):  
World Wide ◽  
Integration Method ◽  
Graph Model ◽  
Spatiotemporal Data ◽  
Rdf Graph ◽  
Integration Approach ◽  
The World ◽  
Description Framework ◽  
Resource Description ◽  
Significant Superiority

With the growing importance of the fuzzy spatiotemporal data in information application, there is an increasing need for researching on the integration method of multi-source heterogeneous fuzzy spatiotemporal data. In this paper, we first propose a fuzzy spatiotemporal RDF graph model based on RDF (Resource Description Framework) that proposed by the World Wide Web Consortium (W3C) to represent data in triples (subject, predicate, object). Secondly, we analyze and classify the related heterogeneous problems of multi-source heterogeneous fuzzy spatiotemporal data, and use the fuzzy spatiotemporal RDF graph model to define the corresponding rules to solve these heterogeneous problems. In addition, based on the characteristics of RDF triples, we analyze the heterogeneous problem of multi-source heterogeneous fuzzy spatiotemporal data integration in RDF triples, and provide the integration methods FRDFG in this paper. Finally, we report our experiments results to validate our approach and show its significant superiority.

Natural language interface to video database

2001 ◽  
Vol 7 (1) ◽  
pp. 1-27 ◽  
Author(s):  
T. R. GAYATRI ◽  
S. RAMAN
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Input Word ◽  
Video Database ◽  
Video Objects ◽  
Natural Language Interface ◽  
Different Types ◽  
Natural Language Query ◽  
Slot Filling ◽  
Three Phases

In this paper, we discuss a natural language interface to a database of structured textual descriptions in the form of annotations of video objects. The interface maps the natural language query input on to the annotation structures. The language processing is done in three phases of expectations and implications from the input word, disambiguation of noun implications and slot-filling of prepositional expectations, and finally, disambiguation of verbal expectations. The system has been tested with different types of user inputs, including ill-formed sentences, and studied for erroneous inputs and for different types of portability issues.

scholarly journals Providing Adverse Outcome Pathways from the AOP-Wiki in Semantic Web Format to Increase Usability and Accessibility of the Content

2021 ◽  
Author(s):  
Marvin Martens ◽  
Chris Evelo ◽  
Egon Willighagen
Keyword(s):  
Resource Description Framework ◽  
Adverse Outcome ◽  
Semantic Annotation ◽  
Adverse Outcome Pathway ◽  
Dublin Core ◽  
Description Framework ◽  
Federated Queries ◽  
Key Events ◽  
Adverse Outcome Pathways ◽  
Resource Description

<div>The AOP-Wiki is the main environment for the development and storage of Adverse Outcome Pathways. These Adverse Outcome Pathways describe mechanistic information about toxicodynamic processes and can be used to develop effective risk assessment strategies. However, it is challenging to automatically and systematically parse, filter, and use its contents. We explored solutions to better structure the AOP-Wiki content and to link it with chemical and biological resources. Together this allows more detailed exploration which can be automated.</div><div><br></div><div>We converted the complete AOP-Wiki content into Resource Description Framework. We used over twenty ontologies for the semantic annotation of property-object relations, including the ChemInformatics Ontology, Dublin Core, and the Adverse Outcome Pathway Ontology. The latter was used over 8,000 times. Furthermore, over 3,500 link-outs were added to twelve chemical databases and over 6,500 link-outs to four gene and protein databases. </div><div><br></div><div>SPARQL queries can be used against the Resource Description Framework to answer biological and toxicological questions, such as listing measurement methods for all Key Events leading to an Adverse Outcome of interest. The full power that the use of this new resource provides becomes apparent when combining the content with external databases using federated queries. For example, we can link genes related to Key Events with molecular pathway on WikiPathways in which they occur and find all Adverse Outcome Pathways caused by stressors that are part of a particular chemical group. Overall, the AOP-Wiki Resource Description Framework allows new ways to explore the rapidly growing Adverse Outcome Pathway knowledge and makes the integration of this database in automated workflows possible.</div>

scholarly journals gst-store: Querying Large Spatiotemporal RDF Graphs

2017 ◽  
Vol 1 (2) ◽  
pp. 84-103 ◽  
Author(s):  
Dong Wang ◽  
Lei Zou ◽  
Dongyan Zhao
Keyword(s):  
Query Language ◽  
Data Management System ◽  
Rdf Graph ◽  
Spatiotemporal Information ◽  
Simple Protocol ◽  
Rdf Data ◽  
Description Framework ◽  
Query Algorithm ◽  
Rdf Graphs ◽  
Resource Description

Abstract The Simple Protocol and RDF Query Language (SPARQL) query language allows users to issue a structural query over a resource description framework (RDF) graph. However, the lack of a spatiotemporal query language limits the usage of RDF data in spatiotemporal-oriented applications. As the spatiotemporal information continuously increases in RDF data, it is necessary to design an effective and efficient spatiotemporal RDF data management system. In this paper, we formally define the spatiotemporal information-integrated RDF data, introduce a spatiotemporal query language that extends the SPARQL language with spatiotemporal assertions to query spatiotemporal information-integrated RDF data, and design a novel index and the corresponding query algorithm. The experimental results on a large, real RDF graph integrating spatial and temporal information (> 180 million triples) confirm the superiority of our approach. In contrast to its competitors, gst-store outperforms by more than 20%-30% in most cases.

scholarly journals Providing Adverse Outcome Pathways from the AOP-Wiki in Semantic Web Format to Increase Usability and Accessibility of the Content

2021 ◽  
Author(s):  
Marvin Martens ◽  
Chris Evelo ◽  
Egon Willighagen
Keyword(s):  
Resource Description Framework ◽  
Adverse Outcome ◽  
Semantic Annotation ◽  
Adverse Outcome Pathway ◽  
Dublin Core ◽  
Description Framework ◽  
Federated Queries ◽  
Key Events ◽  
Adverse Outcome Pathways ◽  
Resource Description

<div>The AOP-Wiki is the main environment for the development and storage of Adverse Outcome Pathways. These Adverse Outcome Pathways describe mechanistic information about toxicodynamic processes and can be used to develop effective risk assessment strategies. However, it is challenging to automatically and systematically parse, filter, and use its contents. We explored solutions to better structure the AOP-Wiki content and to link it with chemical and biological resources. Together this allows more detailed exploration which can be automated.</div><div><br></div><div>We converted the complete AOP-Wiki content into Resource Description Framework. We used over twenty ontologies for the semantic annotation of property-object relations, including the ChemInformatics Ontology, Dublin Core, and the Adverse Outcome Pathway Ontology. The latter was used over 8,000 times. Furthermore, over 3,500 link-outs were added to twelve chemical databases and over 6,500 link-outs to four gene and protein databases. </div><div><br></div><div>SPARQL queries can be used against the Resource Description Framework to answer biological and toxicological questions, such as listing measurement methods for all Key Events leading to an Adverse Outcome of interest. The full power that the use of this new resource provides becomes apparent when combining the content with external databases using federated queries. For example, we can link genes related to Key Events with molecular pathway on WikiPathways in which they occur and find all Adverse Outcome Pathways caused by stressors that are part of a particular chemical group. Overall, the AOP-Wiki Resource Description Framework allows new ways to explore the rapidly growing Adverse Outcome Pathway knowledge and makes the integration of this database in automated workflows possible.</div>

NLP and the Representation of Data on the Semantic Web

2021 ◽  
pp. 393-426
Author(s):  
Jose L. Martinez-Rodriguez ◽  
Ivan Lopez-Arevalo ◽  
Jaime I. Lopez-Veyna ◽  
Ana B. Rios-Alvarado ◽  
Edwin Aldana-Bobadilla
Keyword(s):  
Natural Language Processing ◽  
Semantic Web ◽  
Natural Language ◽  
Language Processing ◽  
Resource Description Framework ◽  
Basic Unit ◽  
Starting Point ◽  
Description Framework ◽  
Resource Description

One of the goals of data scientists and curators is to get information (contained in text) organized and integrated in a way that can be easily consumed by people and machines. A starting point for such a goal is to get a model to represent the information. This model should ease to obtain knowledge semantically (e.g., using reasoners and inferencing rules). In this sense, the Semantic Web is focused on representing the information through the Resource Description Framework (RDF) model, in which the triple (subject, predicate, object) is the basic unit of information. In this context, the natural language processing (NLP) field has been a cornerstone in the identification of elements that can be represented by triples of the Semantic Web. However, existing approaches for the representation of RDF triples from texts use diverse techniques and tasks for such purpose, which complicate the understanding of the process by non-expert users. This chapter aims to discuss the main concepts involved in the representation of the information through the Semantic Web and the NLP fields.

Sign in / Sign up

Export Citation Format

Share Document