Semantic representation of monogenean haptoral Bar image annotation

Abstract

BackgroundDigitised monogenean images are usually stored in file system directories in an unstructured manner. In this paper we propose a semantic representation of these images in the form of a Monogenean Haptoral Bar Image (MHBI) ontology, which are annotated with taxonomic classification, diagnostic hard part and image properties. The data we used are basically of the monogenean species found in fish, thus we built a simple Fish ontology to demonstrate how the host (fish) ontology can be linked to the MHBI ontology. This will enable linking of information from the monogenean ontology to the host species found in the fish ontology without changing the underlying schema for either of the ontologies.ResultsIn this paper, we utilized the Taxonomic Data Working Group Life Sciences Identifier (TDWG LSID) vocabulary to represent our data and defined a new vocabulary which is specific for annotating monogenean haptoral bar images to develop the MHBI ontology and a merged MHBI-Fish ontologies. These ontologies are successfully evaluated using five criteria which are clarity, coherence, extendibility, ontology commitment and encoding bias.ConclusionsIn this paper, we show that unstructured data can be represented in a structured form using semantics. In the process, we have come up with a new vocabulary for annotating the monogenean images with textual information. The proposed monogenean image ontology will form the basis of a monogenean knowledge base to assist researchers in retrieving information for their analysis.

A model of a digital biological ecosystem

“…Integrating fish species data from all the available sources helps us explore new relationships such as host-parasite relationship among fish and monogeneans discussed briefly in these papers (Abu et al, 2013a(Abu et al, , 2013b. These relationships that are modelled in the digital biological ecosystem and shown visually by GIS can be further explored by marine scientists in their research.…”

“…Through the proposed system we intend to provide integrated data and information in an easily understandable format. This data integration effort also will assist researchers to explore relationships in biological ecosystems as they are modelled in the digital counterpart (for instance, the relationships between fish and their monogenean parasites that we recently published; Abu et al, 2013aAbu et al, , 2013b.…”

Automated plant identification using artificial neural network and support vector machine

“…With such obvious problems and various difficulties faced by researchers with manual identifications, automated identification systems seem to offer a possible solution. The idea of automated identification is not novel since it has been developed in various biological organisms previously (Abu et al 2013a(Abu et al , 2013bLeow et al 2015;Kalafi et al 2016;Morwenna et al 2016;Salimi et al 2016;Wong et al 2016;Kalafi et al 2017). Several classification methods such as neural network, structural, fuzzy and transform-based techniques have been used in biological image identification systems.…”

A Knowledge-Driven Geospatially Enabled Framework for Geological Big Data

“…In addition, common technologies are widely used when performing semantic-related research. Jena is a free and open source Java framework for building semantic web and linked data applications [36][37][38], RDF is a framework for describing the available resources and their relationships on a network [28,36,[39][40][41], SPARQL (Simple Protocol and RDF Query Language) is a graph-based query language for RDF [37,[42][43][44][45], and SWRL (Semantic Web Rule Language) is utilized to provide rules for semantic networks [33,[46][47][48].…”