=Paper=
{{Paper
|id=Vol-3603/Paper7
|storemode=property
|title=The Potential of Ontologies for the Empirical Assessment of Machine Learning
Techniques in Operational Oceanography
|pdfUrl=https://ceur-ws.org/Vol-3603/Paper7.pdf
|volume=Vol-3603
|authors=Enrique Wulff
|dblpUrl=https://dblp.org/rec/conf/icbo/Wulff23
}}
==The Potential of Ontologies for the Empirical Assessment of Machine Learning
Techniques in Operational Oceanography ==
https://ceur-ws.org/Vol-3603/Paper7.pdf
The potential of ontologies for the empirical assessment of
machine learning techniques in operational oceanography
Enrique Wulff 1
1
Marine Sciences Institute of Andalusia, Spanish National Research Council (CSIC) Campus del
Río San Pedro Cadiz 11510, Spain
Abstract
A role for ontologies is key for the digital transformation of operational oceanography
processes to the adoption of artificial intelligence and machine learning. Marine ontologies, a
common concept among these tools, can lead to lower costs and more flexibility in identifying
and classifying marine data. This study explores a demonstration that proves the potential of
ontologies to fulfill the requirements outlined in the case of how to visualize computer datasets.
A selective network of records, including visual and textual features that can be annotated from
video and image sequences, with subsea parameters as the target of interest. The sample is
divided into ontology and machine learning (ML) datasets to predict the importance of data
visualization methods. The predicted suitability is strong with data classification that belongs
to the machine learning dataset. However, the initial results from the study are encouraging,
because ontologies' tools are proposed as automatic reasoning mechanisms. This proof of
principle shows that it is almost guaranteed that marine ontologies can be built to make visual
patterns for marine data usable by different communities, which could be used to identify
"interesting" functions at the intersection of computer vision and machine learning in general.
Keywords 1
Ontology, machine learning, artificial intelligence, data visualization, classification
1. Introduction
An improved ontological representation of marine data as a paradigm for pattern analysis software
development requires more work on combining different modes of inference (OWL, ML), the design
of algorithms for data classification (DC) and visual data recognition (DR) for signal and image analysis
[1]. This poses the problem of how should marine databases be represented. An ontology of a domain
is an “explicit formal specification of the terms in the domain and relations among them” [2]. An
ontology fully describes the subject area as a dictionary, in a way it is the ideal tool when we focus on
the generation of contextual descriptions for images (in 3D shape retrieval for example [3]). Most of
pattern analysis algorithms in oceanography, are to be used for object detection and recognition
research, motivated by this challenge it can be proved that an ontology could be a relevant approach to
the problem of marine data recognition and classification.
The marine data received from wireless sensor networks are heterogeneous in nature. For instance,
the existing marine acoustic data cannot meet the amount of data required for training models [4]. In
particular, positioning and orientation systems, and other sensor technology, is based on multi-beam
echo sounder system acceptance and quality assurance. An automated system producing multiple
overlapping range images that was the first for correctly registered mapping of the ocean floor [5].
Whether data come from GIS technology, the Web or any other present or future approach they share
common ground [6]. A role for ontologies is key in the development of application software for the
acquisition, analysis and display of real time marine data, for the generation of model scenario databases
for their retrieval, and display at the time of an event and for the decision support systems following a
Proceedings of the International Conference on Biomedical Ontologies 2023, August 28th-September 1st, 2023, Brasilia, Brazil
EMAIL: enrique.wulff@icman.csic.es
ORCID: 0000-0001-8104-6147
©️ 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
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Workshop ISSN 1613-0073
Proceedings
70
standard procedure [7-8]. To define and develop intelligent systems, has been proposed in recent times,
giving a rise in both precision and recall as well as facilitating system interoperability through data
harmonization [9-12]. Ensuring interoperability between marine databases is a huge challenge. Terms
and codes used to structure exploit the data comes from many sources and are continuously evolving.
The problem pattern analysis (PA) is facing consists in finding an adequate visualization, a "good"
figure, since humans are only capable of perceiving objects in at most three dimensions [13]. This
means that pattern analysis has to find a method to reduce the heterogeneity of the set of data under
study, thus allowing an analysis of the problem of stability of pattern. For practical reasons usually only
recognition and classification of those data are allowed (best practices must be carried out by focusing
on structure and naming consistency) [14]. Image recognition tasks are at the centre of the ongoing
machine learning revolution, an approach that in the monitoring of coastal seas is focused on using
automated classification algorithms based on random forest or deep learning approaches [15]. However,
the field of marine image processing lacks the large numbers of annotations in images required [16].
The lack of correspondence between the visual representation of the image and its meaning calls for the
performance of Machine Learning, expressed through semantic resources such as ontologies.
The problem of trying to solve the visual parameters of images or videos focuses on tasks such as
object detection, data recognition, and multi-level data classification. Such an example could be that of
studying how the air and sea interact with each other during El Niño/La Niña onsets, by using pattern
analysis with ocean data assimilation techniques [17]. This is an issue where content-based image
retrieval is approached in terms of Machine Intelligence [18][19]. As such Pattern Analysis and
Machine Intelligence (PAMI) is an element of scholarship proposed in the last thirty years and where
it has been a continuous need to develop new data recognition and classification methods and advanced
equipment for solving modern practical problems [20].
1.1. Pattern Analysis [and Machine Intelligence (PAMI)] and Marine
Ontologies
Rethinking pattern analysis of marine data means to investigate the rich variety of application
scenarios offered by marine ontologies. While otherwise adding value to public data using semantic
web axioms and machine learning to support annotation contribute to pose and solve issues involving
ocean data classification.
Application of ontologies in ocean data grows out of an Artificial Intelligence (AI) engagement with
marine data metrics of interoperability and reuse. Ontologies serve as such a tool and method to assess
the added value robotic technology brings into the marine environment (autonomous underwater
vehicles (AUVs) or (ocean floor observation systems) OFOSs). From a pattern recognition point of
view, ontologies for describing sensors and sensor networks work in the context of Sensor Web
applications. Knowledge representation in the Internet of Things (IoT) presents a general architecture
of Sensor Web applications. And that is why it provides huge numbers of interconnected data across an
extended variety of various ocean regions, which classifications depend on the specific context and
resources of LinkedData.
By using ontological representation, the best of technical progress, undertaken by a community to
unambiguously set definitions and interconnect concepts in various field, is captured. The use of
ontologies for representing database entities has proven to be advantageous in the field of Pattern
Analysis and Machine Intelligence (PAMI) (see Table 1).
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Table 1
The main features provided by ontologies in support of PAMI
Ontology feature Utility in PAMI
Classes and relations When ontology reasoning is applied to sensor data,
rdf:type will be connected to a class name of an
ontology
Domain vocabulary Ontologies provide a domain vocabulary that can be
exploited to create a dense network of relationships
among the entities, and serve
software applications, and GIS
Metadata and descriptions Biodiversity data, especially in marine domain, have
database entities represented as ontologies where
these last are primarily used for metadata that
describe raw data providing contextual information
Axioms and formal declarations Ontology axioms and applied reasoning on them are
related to the recognition of object presence in a time
interval
The concept of marine ontologies may be the solution in developing systems and workflows that
would meet the various possible marine data requirements and from them derive up to standard
products/maps without human assistance except at the user interface. As shown in Figure 1, research
on ontology topics can be followed from different perspectives. The index is the percentage of the
publications in the ontology sub-areas of research. It covers semantic web, web services and so forth.
Especially, the semantic web, data integration, and web service have attracted the attention of a large
number of researchers in recent years while the research on the topics of data source, relation extraction
and heterogeneous data seems less consistent. One element is the major cause of these problems, as far
as a common ontology for marine data is necessary to enable exchange and integration of data.
Terminology is used to describe similar data can vary between marine specialties or world ocean
regions, which can complicate data searches and data integration.
100.000 Semantic Web % of 9152
90.000 Web Service % of 1466
Heterogeneous Data % of 12
80.000
Data Integration % of 1612
70.000
Ontology Learning % of 730
60.000
Information Extraction % of 1005
50.000
Data Source % of 320
40.000
Relation Extraction % of 189
30.000
20.000
10.000
0
Figure 1: Ontology subareas of research (dependent variable: percent of publication in ontology sub-
areas)
The ontology-based research illustrates especially how those involved with marine data should be
informed about marine ontology developments. Opportunities to enhance their development will
contribute to the success of ontology in the way that certain concepts and ideas start to unfold. It is
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customary to consider emerging observational patterns making sense out of methods that captures
concepts leading to finding out what is visible. To conduct this insight, for example in coastal web
atlases (CWA), developers should intensify future efforts to improve data discovery, sharing, and
integration on the base of ontologies.
1.2. Ontologies and Marine Robotics
Marine data classification has been studied widely in the field of marine robotics; while pattern
recognition is a process of finding regularities and similarities in data using machine learning data which
is the perspective of marine robotics. Marine robotics has undergone a phase of dramatic increase and
its quantitative landscape, status quo and current workflow is shaped by its own pattern analysis, data
recognition and classification issues.
From the point of view of marine robotics key issues in ocean data management concern two
different PAMI realities representing, detecting, and tracking features and the process of integrating
real sensor data with a model of an ocean process. As a standard knowledge representation ontology
can facilitate the development of these marine robotic applications in various ways:
Providing a consistent set of terminology (domain vocabulary), and concepts in the robot's
knowledge representation (definitions, relations, domain axioms and taxonomy)
Enabling design pattern guidelines for content analysis of complex tasks, environment, etc.
Ensuring a common repository of knowledge that can be shared among various robotic
systems
Highlighting more efficient new relations through the analysis of data generated using
ontologies
1.3. Contribution of this paper
The purpose of this paper is to identify relevant pattern analysis research in marine data classification
and recognition, and to review its intersection with the state-of-the art in marine ontologies. It focuses
on the 3D modeling and analysis domain, computer vision and interactions are described for machine
learning (ML) and marine ontologies.
2. Method
All the R&D efforts in pattern analysis, classification and recognition of data have been kept rising
over the current period (1991 to 2021). To obtain a general understanding of this research question
concerning marine data we systematically reviewed the IEEE Pattern Analysis and Machine
Intelligence, IEEE Access, IEEE Journal of Oceanic Engineering, Sensors, and Information
Visualization. Initially, we identified the appropriate subset of articles from these conferences and
journals. We then conducted an in-depth qualitative analysis of the relevant work, re-removing and
refining the characteristics of the marine data interaction of PA. The histogram theory inspired us to
take a general approach to this analysis, which systematically analyzes the data until significant
categories appear. This methodological approach is based on define and refine categories based on a
representative set of qualitative data, here are documents that are then used to progressively build a
theoretical model. This approach has been used in pattern analysis and related areas such as data
classification and data recognition before, and recognized its role for the importance of establishing a
much-needed theoretical framework for visualization.
2.1. State-of-the-art of Pattern Analysis and data classification and
recognition in marine data
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We started our efforts with a carefully selected list of important publications in interactive machine
learning and marine data. Using these candidate documents, we first tried an open approach to coding
to identify "interesting" features at the intersection of computer vision and machine learning in general.
Although this resulted in a high-level structure [19], it was impractical to make the analysis more
concrete. Therefore, we decided to analyze a much larger set of sample articles, with two implications
for our methodological options. (1) We understood the need to look at specific pattern analysis problem
(in our scenario, intelligent driving, image synthesis, and object pose measurement) searching to make
the research more focused, practical, and concrete. (2) We needed automated methods to narrow down
the pool of potentially interesting articles.
During this process, we repaired the retrieval practice in cleaning its criteria and coding selection
multiple times. Our final workflow consisted of four main steps, shown in Figure 2: 1.) Obtaining
ontology research trends, 2.) Reviewing the previous research and application of pattern analysis, 3.)
Identifying marine data classification and recognition issues, 4.) Searching for pattern analysis and
machine learning parameters to encode for a large part of the ontologies' semantic content.
2.2. Sample network of records
Our common goal was the ontology research developed and how its implementation interacts in the
pattern analysis and marine data communities. We decided to take a representative sample of papers,
made up of every paper ever published in a Web of Science (WoS) source titles in the marine pattern
analysis community from 1991 to 2021. From the database (WoS), is defined a collection of pattern
analysis (PA) and marine data records (6048), data classification (DC) and marine data papers (3242)
and data recognition (DR) and marine data records (1214), for a total of 9,899 records.
2.2.1. Paper metadata-based filtering
Methodological options were driven by the idea that the state of the art of ontology (machine/deep
learning) research could be determined by using metadata. By metadata, we refer to aspects of the
words-in-title that were deemed essential to facilitate a meaningful analysis in a full-content context.
The initial synthesis was accomplished by deciding on a uniform list of metadata and their distribution
along the years, as found in Figure 1. Based on this metadata definition, we implemented metadata lists
from the sets of records in PA and data classification and recognition. The final metadata lists and
statistics from this metadata filtering process are provided in Figures 2a,b,c.
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Figure 2: Comparison of metadata efforts in tracing WoS records to measure pattern analysis, marine
data classification and marine data recognition shown in three log-scale histograms; 25 top metadata
required with pattern analysis capture the data to detect, recognize and identify target of interests
from physical, optical, fluid, and chemical underwater parameters (a); histogram estimated by image
or video parameters with an emphasis on multilevel data classification is reported by its 25 top
metadata (b); ranking title terms (metadata) of the documents on data recognition (c)
We formed a set of primary papers in marine ontologies with and without the initial criteria on PA
and machine intelligence for data classification and recognition. Not all of these metadata allow to
express the semantic content of an image. The discrepancy between the visual presentation of the image
and its meaning requires machine learning performance expressed in terms of semantic resources as a
ontology. So from the data set, including records in PA and corresponding DC and DR values (9899)
are extracted two test sets on ontologies (42) and machine learning (210). In this way, the use of marine
ontologies as the data classification and recognition technique focuses on the viability of using
ontologies to solve the problem of pattern analysis.
2.2.2. Manual and automatic sample check
The 42 ontological papers were manually checked using the following criteria. First, we checked if
the paper is a theoretical and evaluative framework or if it deals with a combination of applied or
technical visual methods; as we planned to build a theoretical model for visualization. Second, we
checked whether the paper addresses the combination of pattern analysis (PA), data classification (DC)
and data recognition (DR), and whether the interaction returns to the visualization area. This had the
advantage to present an interesting one workflow for the multi-source, multi-format, multi-dimension
characteristic of marine data. Moreover, there is a return to the visualization area in its framework
design that considers underlying data patterns. Given our focus on visualization we include this model
that even feedback to the analysis of the 3D marine data. One major advantage of this method is its
ability to define a semantic model of the issue under scrutinize (PA, DC & DR) combined with the
associated domain of visualization to list the data visualization theories brought by marine data and
observations, that range from the digital transformation of operational oceanography processes to the
adoption of artificial intelligence. On this basis, we manually analyzed the first 42 candidate relevant
documents obtained. Table 2 provides a partial list of the 42 specific ontological contexts detected in
the PA and DC and DR data sources, and the extent to which they provide the ontology tools they use.
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Table 2
List of the ontological contexts detected in the PA and DC and DR data sources (A/B:
applied//theoretical; DV: data visualization feedback)
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After an automated process, the set of 210 papers corresponding to machine learning was filtered
based on the fact that one of the most frequently used data visualization techniques in machine learning
is the histogram plot. ML-based data visualization techniques were approached through metadata
generated from a base histogram and classified into four levels: disseminative, observational, analytical
and model-developmental. That is to say, a theoretical framework, because a visualization technique
that builds on machine learning therefore attests its power for interactive analysis of heterogeneous
marine data, it can deliver relevant pattern analysis content in the appropriate mode. Table 3 lists these
visualization levels. Through boxplot with the ontological and machine learning (ML) datasets, we
found differential expression of how their values are spread out and detect schematically their outliers
(Figure 3). The temporal analysis parameters for machine learning (ML) are listed in Table 4.
Table 3
Levels of data visualization in machine learning methods for pattern analysis (marine data) (metadata
generated from a base histogram
Figure 3: Differential expressions of relevance from the Ontological and Machine learning (ML)
datasets (dependent variable: ln(cit))
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Table 4
Machine learning for pattern analysis time statistics (marine data)
3. Results and discussion
3.1. Ontology research trends review
The outputs of review using the ontology research trends are shown in Figure 1. As mentioned in
Section 2.1, database entities represented as ontology terms result in a rich variety of scenarios that
store in annotations their features and strengths. The ultimate goal is a system that combines visual and
textual semantics to regularly annotate video sequences final aim is a system that will link the visual
and text semantics in order to routinely annotate video sequences with the appropriate keywords of a
domain expert. Most ontology-based cognitive vision promising results occurred in 2008.
By 2015, heterogeneous marine data, believed by visualization techniques to be of strategic
importance, had their top priority. It is also the subject of scientific research, as evidenced by a large
number of research papers, books, and reports. Highlights were the initiative to create in marine systems
well-founded ontologies embedding these domain semantic and logical frameworks in the underwater
environments thus providing opportunities for intelligent observatory units. The details of
communication ontology that can be used by Remotely Operated Vehicles (ROVs) to transmit data and
commands between vehicles and operators is defined by OWL in SWARMs platform. SWARMs users
can estimate the rate of spread of pollutants and determine the level of pollution and the estimated size
of the polluted area. In marine biology, search engines do use ontologies like SWEET for engaging the
coral reef research community via a cyberinfrastructure network.
3.2. State of the art of pattern analysis in marine data classification and
recognition
The outputs selection was initially based on the idea that machine learning (ML) enhanced by the
ontology is able to compare pattern analysis performances using marine data. But ontologies specific
coverage statistics are few, and it is difficult to say what actually constitutes a significant part of the
terms in an ontology.
Typically, a generic ontology design pattern is developed for data from observations on the Semantic
Web by unlocking the potential of compositional definitions, proposed to distinguish reliable relations
for pattern analysis expression. These definitions are the necessary information to start with, because
they are partitioned into mutually exclusive cross-products sets, many of which reference other
candidate ontologies for chemical entities, proteins, biological qualities and zoological entities. An
example of such a case is the Environment Ontology (ENVO) which, using the expressivity of OWL,
grows in acceptance and participation in new user communities, thus offering an example of ontology's
classes increased granularity in their logical definitions, allowing more flexibility in semantically
advanced questions, inferences and analysis.
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Ontologies such as the Extensive Observation Ontology (OBOE), Observations and Measurements
(O&M), Semantic Sensor Network (SSN), and SWEET can be interconnected and expanded to include
additional concepts more specific to the field of remote sensing, including the basic concepts that remote
sensing professionals rely on to interpret remote sensing images (e.g., concepts, associated with spectral
bands, spectra or texture indices). Examples of such a remote sensing ontology have already been
applied, but have not yet been used in any upper ontology.
In this sense, under a definition of data classification as a process of clustering these data into a
series of groups or categories, regardless of the method used for this purpose, research on pattern
analysis finds a framework for marine data based on ontologies as an active output for computer vision.
From this framework for an ontology-based ocean image classification that describes how to create
ontological models for low- and high-level features, classifiers and rule-based expert systems, a much
larger set of sources appeared.
3.3. Histograms to bridge the semantic gap between notions of content and
similarity
The results from the previous section are used to build a sample of filtered dataset (9,899 records).
Firstly, such sample contains metadata from thousands of word-in-title terms that can capture both
concrete and abstract relationships between salient visual properties. Subsequently, histogram analysis
methods were employed to compare the semantic effort by considering the metadata weight as
generated on the base of global citation scores. This result in three referential frames, is shown in Figure
2a,b,c.
Structure (292 records) and communities (199) have a significant score in pattern analysis, indicating
that these two parameters cause a positive effect on modelling required to discriminate relevant from
non-relevant images (Figure 2a). Basin has a weak score due to the indirect value it has with the general
purpose retrieval of features constructs such as predicates, relations, conjunctions, and a specification
syntax for image content (for instance, photographic images).
The following is the histogram for data classification (see Figure 2b), which shows that mapping
(221 records) is the main technique to classify marine data, while spatial (93) data are by now less
rigidly circumscribed. The complete histogram of the attributes obtained for quantify the features in the
data recognition domain is also shown Figure 2c. The importance of data recognition for evolutionary
biologists (53 records) is enclosed within the scope of the study of species (67 records). The close
relation with classification (61 records) ensures that a visual language can use an important mechanism
for conscious control, limiting the range of possible configurations of functions that must be taken into
account when performing a visual recognition task.
3.3.1. A framework for interactive visual analysis of heterogeneous marine
data
The statistical quantized histogram metadata analysis was based on the available PA, DC & DR data,
and focused on expressing the multi-dimension spatiotemporal marine data in one workflow. Based on
the data we processed, two visualization methods are explored: ontology and machine learning (ML).
The basic idea is shown in Fig. 3.
In this way, according to the data value and methodological choice, two different data classification
and recognition scenarios in pattern analysis can be compared. Under the first scenario ontology-based
for image retrieval and annotation was used to derive marine data patterns. Owing to the substantial
positive bias in ontological feedback to the domain of visualization (Y=85.7%; Table 2), subsequent
approaches for visual events were larger than in the case of the second scenario (machine learning
(ML)), but because citations were restricted to 70% of the available ML data, the resulting lower quartile
of 0.45 reached a best score for ML than for ontologies (0.38).
Therefore, machine learning (ML) decisions based on marine data assessment outperformed
ontology-driven coding for image classification. And that, in spite of ontology mapping for underwater
IoT (IoUT) supports better interoperability protocols in the context of computer vision.
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3.3.2. PA, DC & DR identified through ontologies for marine data
The Table 2 is built based on the 42 selected ontology records. This is a general-purpose scheme
designed for filtering the typology of the data sources. As described by the PubMed database there is a
strong proximity between applied (or technical) and theory (evaluative, comparative, lessons) contents
when they are both expressed in percentage terms (47.61% vs. 52.35). This analysis is then extended
utilizing the feedback of each source to the domain of data visualization. It is overly positive (85%), as
estimated by subject headings including in MeSH for each source.
For all the sources that have been used in this study, the ontology tools are listed in Table 2.
Researchers proposed new models to cope with marine transcriptome/genome identification (80%;
Table 2). They assumed Gene ontology (GO) approaches to model knowledge on the experimentation
organisms. Some approaches focus on a data visualization package (Illumina sequencing technology)
to provide refined descriptions of the whole scenario (1,5,18,19,24,36; Table 2). In one source (39;
Table 2), the authors propose an automatic reasoning mechanism to deal with uncertainty in a quasi-
empirical model using KAAS automatic annotation server. A number of marine or environmental
ontologies (MMI, ENVO, EMPO, SWEET) are found (9,11,12; Table 2), they are used as dictionary
learning in microbial environments to find out unavailable variables. Other ontologies' tools are also
used (BRENDA Tissue Ontology (BTO)(25; Table 2), Protein Annotation through Evolutionary
Relationships (PANTHER) (17,41; Table 2).
3.3.3. Machine learning levels of visualization and their temporal perspective
In this section, it is investigated whether a data visualization level can give a prediction of its
suitability for a particular machine learning task. There exists a spectrum of different steps of
visualization ranging from high abstraction levels (e.g., model-developmental tools) to lower levels
(e.g., operational aids) (see Table 3; 210 records). To enhance this theoretical framework performance,
the ML-based data visualization techniques are used based on 20 metadata, assuming that the marine
data papers are categorized from different histograms which are quite reliable.
On Table 3, the level of visualization importance for suitability prediction is shown. As the first
basic task of knowledge discovery, it proposes the use of data classification tools (33%); most visual
analytics processes reported in PA, DC & DR literature operate at this level. The analyst knows or
assumes the model to be correct only in 6% of the sources. Only in 16% human analysts need to use
visualization to observe data routinely. Human analysts are able to observe input data in conjunction
with the machine's "understanding" in many ways (33%). A line-up of model developmental tools gains
a new understanding in terms of complexity (44%).
We can further derive from Table 3 that deep learning is the main automated support of marine data
analytics using machine learning (ML) techniques, perhaps this is caused by the success of deep
learning in computer vision tasks (eg. image classification, object detection, instance segmentation). In
data visualization most of the deep learning studies focus on model developmental aids (44%), followed
by observational tools (30%), investigative (18%) and presentational (8%) aids. Analytical and model-
developmental visualization levels were shared equally among other ML techniques employed (transfer
learning, ensemble methods, clustering).
As shown in Table 4, a sequence has been used to encode the data sources as the number of papers
published by year. Years 2020 (55) and 2021 (48) are peaks. We can see clearly that 50% of the papers
were all published in the last two years. This is not strange since the idea that in machine learning (ML)
pattern analysis is gaining future, is expressed again by the importance of the two last years for the four
levels of visualization (relative importance of 69%, 70%, 44%, 43%). As expected from the results in
Table 3, there are gaps in both disseminative and observational data sets (with 6 years breaks in
between).
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4. Conclusions
This proof of principle study explores the potential uses of ontologies to encode for marine data
pattern analysis literature. This study focuses on characterizing marine ontologies to select data
visualization techniques. The underlying assumption is that the application of ontologies in marine data
poses the problem of how should marine databases be represented. Therefore, the validation against
pattern analysis in oceanography should be first put in terms of data interoperability. Using this
approach could provide experts with a tool and method where they can rate ocean technologies and how
they have been received in the communities where they have been placed. A data histogram approach
has been adopted, which draws on the analysis of literature until significant categories appear. This has
demonstrated its worth in pattern analysis, data classification and data recognition, and is regarded as
an ingredient of the new generation of theoretical frameworks for data visualization. The results of the
model to predict what the encoding for a large part of the ontologies' semantic content is going to look
like in the future show that marine ontologies specific coverage statistics are few. It is acknowledged
that the biomedical Gene Ontology (GO) currently represents the most successful implementation of
ontologies in the domain of oceanography for pattern analysis applications including data visualization.
It is recognized that, for machine learning data visualization, marine data scoring solutions were better
than ontology-based coding for image classification. This approach has led to accurate predictions of
the level of visualization importance for the example of data classification. Over the machine learning
techniques most used for computer vision tasks with marine data, the result of the study outstands for
it is clearly stated that deep learning is a promissory approach to gain new understandings in terms of
data visualization tools. The results of this study show the potential use of marine data for pattern
analysis assessment and prediction of the level of data visualization. This method shows the potential
of ontologies to support the generation of model scenarios for image retrieval and annotation, and to
aid for the empirical assessment of machine learning techniques. A single example data visualization
was used as an application for indicating the potential value of ontologies to solve the issues of pattern
analysis and taking a first step towards a theoretical model for visualization with marine data. It is
recommended for future research that marine model developers should intensify their efforts to improve
data discovery, sharing, and integration on the base of ontologies.
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A complete list of references is available from the author.
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