Keywords

1 Introduction

With the continuous development of three-dimensional ocean exploration technology, mankind had acquired an unprecedented ocean big data set. Because the ocean flow field is an important carrier of material and energy exchange in the ocean, the semantic information sharing and migration rule analysis of ocean flow field based on those heterogeneous ocean data is of great significance to human deep understanding of the ocean.

To solve the problem of semantic heterogeneity in Marine domain knowledge, many scholars had carried out a series of researches with the help of ontology. It was Findler and Malyankar who first established the ontology of coastal entities (such as shoreline, tidal surface, etc.) when hosting the project of expression and distribution of spatial knowledge. Bermudez et al. [1] constructed 28 ocean ontologies including most of the key concepts in Marine science in the Marine metadata interoperability project. Based on thesaurus, Zhou [2] constructed the Marine domain ontology covering Marine physics, Marine biology, Marine chemistry and other contents. Xiong [3], Yun et al. [4] conducted ontology modeling research in the field of Marine ecology, and proposed the Marine ecological knowledge organization model. Zhang et al. [5] studied the characteristics of Marine culture and used OWL_DL description language to realize the ontology model description of Marine culture domain. Jia Haipeng et al. [6] proposed an ontology model based on Hozo’s role theory and established the ocean carbon cycle ontology. Abidi et al. [7] conducted ontology modeling for Marine biological knowledge and data management. Li [8] and Zhang [9] established the Marine knowledge ontology based on ocean remote sensing data sources by applying hybrid ontology. Based on offshore, shore-based and remote sensing observation data, Li [10] established ocean observation ontology and constructed a set of data standardization service supporting NetCDF format. Lu [11] took ontology as the semantic basis of the multidimensional model of data warehouse and built the multidimensional data model based on the ocean hydrological ontology. For the problem of data organization in the polar Marine environment monitoring system, Zhang [12] adopted the idea of ontology to construct an ontology model framework of Marine environment data including spatial relations.

Inspired by the above knowledge ontology modeling in the Marine domain. To realize knowledge sharing and cognitive consistency in the field of ocean flow field. The paper based on the semantic analysis of the properties and spatio-temporal relations of ocean flow field, with the idea of ontology, proposed the ontology structure of ocean flow field based on six-tuple, and gave the Ontology modeling and formal expression of ocean flow field with OWL. The paper’s research could provide some references for the expression and mining of knowledge system of ocean flow field.

2 Application of Ontology in the Field of Ocean

2.1 Ontology and Geographical Ontology

Ontology originated from the field of philosophy, and was first proposed by Goclenius, a German scholar in the 17th century. For the reason that there was no unified definition and fixed application field, in 1995 Gruber from Stanford university came up with a widely accepted definition of “Ontology is an accurate description of concepts and is used to describe the essence of things”. Ontology can be divided into domain ontology, general ontology, applied ontology and representation ontology. Subsequently, ontology was introduced into artificial intelligence, information system, knowledge system and other fields by the information academia, and many scholars also gave a series of definitions. In 1998, Studer et al. argued that ontology was a clear formal specification of Shared conceptual model, and formed a unified view of ontology. Ontology construction unifies the description of concepts, attributes and relational sets, making it possible to share domain knowledge.

For the reason that geographical objects have the obvious spatio-temporal characteristics, people had gradually introduced ontology into the field of geographic information science, and created a series of concepts and definitions of geographical ontology. In 1998, Mark et al. [13] believed that the core of geographic ontology was the semantic theory of spatial information. Bishr et al. [14], 1998, held that geographic ontology was a geographic spatial model that could build semantic consistency. In 2005, Jing [15] also proposed the definition of semantic field of geographic ontology. In 2007, Li [16] proposed a three-layer overall framework of geographical ontology.

2.2 Marine Domain Ontology

As a part of the world of geography information, ocean has both geographical spatial characteristics and physical features, so ontology also had been gradually introduced into the ocean. In order to solve the integration and sharing of heterogeneous Marine data, from 2004 to 2008, Su Fenzhen, Du Yunyan, Yang Yiaomei, Xue Cunjin and other scholars studied the organization and modeling of various types of Marine data, and proposed the framework method for the integration and sharing of Marine big data. In 2008, Zhang Feng et al. established the mapping relationship between ontology and data sources by studying the semantic heterogeneity of ocean multi-source data. In 2008, Du [17] took Liaodong Bay as an example and established the gulf knowledge ontology. In 2017, Zhang et al. [18] formulated a series of rules of ocean concepts knowledge. In 2009, Zhu [19] established a Shared Marine disaster ontology. In 2010, Xiong Jing developed an optimized information retrieval system based on the ocean ecological ontology. In 2010, Shao [20] constructed the maritime accident ontology. In 2014, Jia Haipeng established the knowledge sharing system of Marine carbon cycle based on ontology. In 2018, Li et al. [21] carried out the construction and formal expression of the local ontology of the ocean flow field phenomenon and the ontology of the ocean space-time process based on the semantic analysis of the spatial and temporal features of the ocean flow field.

3 Establishment of Knowledge Conceptual System of Ocean Flow Field

Ocean current is the main dynamic flow field in the ocean, which is the result of the comprehensive action of turbulence, fluctuation, periodic tidal current and stable “constant current”, and it has different temporal and spatial scales and periodic changes. In order to effectively share and organize knowledge in the field of ocean flow field, this paper established a conceptual hierarchical system of knowledge in the field of ocean flow field based on a large number of references to professional literatures and classification standards in the field of ocean flow field, and according to classification standards such as nature, origin, scale, state and region with the idea of ontology. The specific hierarchical conceptual system of ocean flow field was shown in Fig. 1.

Fig. 1.
figure 1

Concept layered system of ocean flow field

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  1. (1)

    Classification by nature: sea water temperature is the basic index to study the properties of water masses and describe the movement of water masses. According to the index of water temperature, ocean currents could be divided into three categories: warm current, cold current and neutral current.

  2. (2)

    Classification by cause: the causes of seawater flow mainly include the driving effect of wind over seawater(shown in Fig. 2), the horizontal pressure gradient force and coriolis force caused by the superposition of seawater movement and earth movement, the inertial force caused by the wind stress on seawater and the state of seawater itself, the compensation effect caused by seawater flowing at other depths or horizontal positions, and the tide-generating force on seawater on the earth surface. Therefore, ocean currents could be divided into wind currents, geostrophic currents, inertial currents, compensating currents and tides. Among them, the wind current was divided into blowing current and wind current according to stability, and the drift was divided into finite and infinite deep drift according to depth. Geostrophic flow was divided into inclined flow and gradient flow. The compensation flow was divided into vertical and horizontal directions, and the vertical direction includes upward flow and downward flow. Tides were classified into the tides and tidal currents depending on whether the water is moving horizontally or vertically.

    Fig. 2.
    figure 2

    The wind’s influence on ocean currents

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  3. (3)

    Classification by scale: under the influence of the distribution pattern of land and sea, the continuity of sea water, the airflow on the sea surface, and the unequal distribution of heat and salt in sea water, the phenomena of ocean flow field also present different scales. Therefore, ocean flow phenomena could be divided into three scales: large scale, medium scale and small scale. Among them, large-scale refers to thousands of meters in space and months in duration. And ocean circulation could be further subdivided into wind surface circulation, subsurface circulation and thermohaline deep circulation. Mesoscale ocean flow field phenomena could be divided into mesoscale vortices, water masses and ocean peaks. Small scale flow field phenomenon was relatively large ocean flow field and mesoscale flow field, from the superficial, the changes of various physical quantities (temperature, salinity, density and velocity) from the vertical scale of 100 meters to the molecular dissipation scale were in the category of small scale, whose spatial order is within 100 meters and time span is within a few hours. From the perspective of mechanics, the process of ocean flow field at small scale is divided into two parts: wave mode and vortex mode.

  4. (4)

    Classification by state: firstly, the flow state of seawater could be divided into circulation and general ocean current according to whether it was a loop, and circulation could be further divided into near-shore circulation and ocean circulation. secondly, according to the Reynolds number, ocean currents could be divided into laminar flow, transitional flow and turbulent flow. The smaller the Reynolds number the more significant the viscous force, the larger the inertial force. When the Reynolds number is less than 2300, laminar flow, also known as steady flow and laminar flow, is formed. When the Reynolds number is more than 4000, the inertial force has a greater influence on the flow field than the viscous force, forming turbulence. Transition flow is a transition state.

  5. (5)

    Classification by region: according to the position of ocean currents in land-sea relations. Ocean currents could be divided into three categories: nearshore currents, boundary currents and oceanic currents. Among them, the ocean current was divided into equatorial current, westerly drift, polar circulation; Inshore current could be divided into surface flow, middle layer flow and bottom flow according to depth, sea tide and residual flow according to periodicity, and sea tide could be further classified according to movement form, time and direction, while inshore current could also be classified into offshore flow, shoreward flow and coastal flow according to direction. Boundary flow could be divided into eastern boundary current and western boundary current.

    The establishment of the conceptual hierarchy system of ocean flow field phenomena clarified the hierarchical relations, inclusion relations, partial and overall relations among concepts, and provided the framework foundation for the deep semantic analysis of ocean flow field.

4 Semantic Analysis of Ocean Flow Field

4.1 Analysis of Ontology Properties of Ocean Flow Field

There was no obvious boundary between attribute and concept [22]. Some elements could be used as both concepts and attributes. In this paper, concepts that describe the nature or relationship of a certain phenomenon or thing were summarized as category of attribute. Since the ocean flow field has both vector and field characteristics, there is no clear space-time boundary. Before the attributive analysis of ocean flow field domain ontology, attribute types of ocean flow field domain concept were classified into six categories based on the hierarchy system of ocean flow field domain concept established in the previous section. The set of properties of the specific ocean flow field was shown in Fig. 3.

  1. (1)

    Genetic properties: in physical oceanography, most definitions of various ocean current phenomena were based on their genetic differences, and the main factors affecting the flow of sea water were wind, sea temperature, sea density, sea salinity, etc.

  2. (2)

    Force properties: force is the source of the motion of the seawater, including horizontal pressure gradient force, wind stress, gravity, tide generating force, inertia force, friction force and coriolis force, etc.

  3. (3)

    Spatial properties: it mainly focused on the scale, distribution, location and shape of ocean currents.

  4. (4)

    Time attributes: it described the variation of the duration current phenomenon, from the beginning, formation, development and extinction, and paid attention to the lifecycle of the phenomenon.

  5. (5)

    Motion attributes: it was divided into horizontal motion state and vertical motion state,with the equation of current motion, analyzing the motion state of ocean current in three-dimensional space. Motion properties included velocity, flow direction, amplitude and axis.

  6. (6)

    Functional attributes: It described the influence of ocean current on the environmental and economic development of adjacent regions.

Fig. 3.
figure 3

Concept layered system of ocean flow field

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4.2 Asemantic Relation Analysis of Ocean Flow Field

Concepts, attributes and instances constitute the basic elements of ocean flow field description The semantic relations of ocean flow field included inter-concept relations, inter-attribute relations, inter-instance relations, relations between concept and attribute, relations between concept and instance. The semantic description of specific relations was shown in Table 1 and Table 2.

  1. (1)

    Relations among concepts of ocean flow field

    The relations among the concepts of ocean flow field mainly included equivalence relation, hyponymy and juxtaposition relation. Equivalence relation refers to the relation, in which two or more concepts are same in nature. In the conceptual hierarchy system of ocean flow field, inertial flow was also called residual flow, and equatorial flow was also called information wind current, etc., whose concepts point to the same place in essence and their relation was equivalent. The hyponymy refers to the relation between the parts and the whole and inclusion relation. In the first relation, there was no transfer relationships between their properties, which was represented by component-of. In the second relation, there was a transitive relationship on the attribute, represented by class-of. The hyponymy in the conceptual hierarchy of ocean flow field domain knowledge belonged to this inclusion relation. The relation between the concepts of the same level was defined as juxtaposition.

  2. (2)

    Relations among ocean flow field properties

    The relations among ocean flow field properties mainly included: containment relation, correlation relation and time relation. Among them, the inclusion relation was used to represent the hierarchical relation in the attribute; Correlation means that there is an correlation between the two attributes. For example, large-scale ocean flow field phenomenon in space generally corresponds to long-period ocean flow field phenomenon in time. Therefore, there was a correlation between the spatial range attribute of ocean flow field and the periodic attribute in time. Temporal relationships were used to represent the temporal sequence between the phase properties of the lifecycle.

  3. (3)

    Relations between concepts and attributes: it is an ownership relation among numerous attributes and concepts of the ocean flow field domain ontology knowledge system.

  4. (4)

    Relation between concepts and instances: the instances of concepts represent the correspondence between concrete concepts and abstract generalizations.

  5. (5)

    Inter-instance relations: the relations between ocean flow field instances mainly included temporal relations and spatial relations. Instances of ocean flow field concepts occur in chronological order, such as La Niña, which mostly occurs in years after El Niño Phenomenon. Spatial relations represent the spatial distribution characteristics of ocean flow field instances, including topological relations, position relations, distance relations, etc. The relations among specific ocean flow field instances was described in Table 2.

Table 1. Semantic relations between properties and concepts of ocean flow fields.
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Table 2. Relationships among instances.
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The boundary of ocean flow field is fuzzy, and its properties and relations are multi-dimensional. Within a certain range, it has visual properties such as direction and velocity, as shown in Fig. 4. There are also drivers, social and environmental benefits and other potential attributes. The maintenance of ocean flow field in different regions and depths and its influence have their own characteristics, as shown in Fig. 5. The ocean flow field shows different forms in different time and space scales, but it is still interrelated. For example, many vortexes are generated in the western boundary flow of the Pacific Ocean, the strength of the east-west equatorial ocean current will lead to the state change of the western boundary flow, and the ocean front will be formed in the transition zone of water body with significantly different characteristics. Based on the complexity, diversity and overall unity of Marine flow field information, this paper conducts ontology modeling of Marine flow field.

Fig. 4.
figure 4

Symbolization of ocean flow field

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Fig. 5.
figure 5

Schematic diagram of ocean flow field

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5 Modeling and Formal Expression of Ocean Flow Field Domain Ontology

5.1 Modeling of Domain Ontology for Ocean Flow Field

After the establishment of conceptual hierarchy system of ocean flow field domain and the analysis of semantic relations, this article proposed an ontology expression model of ocean flow field in the form of six-tuple based on the ontology idea, which was in the form of O = (C, P, I, R, T, M). Where, C represented the concept set of ocean flow field; P represented the set of attribute vectors of set C; I represented the set of instances of set C; R represented the set of relation of set C, such as semantic relation among concepts and spatial and temporal relation between instances; T represented the set of evolution of an instance corresponding to a concept in set C; M represented the force driving factors for the evolution of examples in T. Considering that the ocean flow field has the characteristics of multi-dimensionality, spatio-temporal dynamics and boundary fuzziness. this paper extended ontology structures such as concepts, attributes, relationships and constructed the basic structure of ocean flow field domain ontology based on the six-tuple model, which was shown in Fig. 6. Because the ocean flow field concept corresponds to not only the instance, but also the instance corresponds to its unique evolution set and driving force elements. So the instance, its evolution set and the driving factors in the evolution process are uniformly organized when describing a collection of instances.

Fig. 6.
figure 6

Symbolization of ocean flow field

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Therefore, the model structure of ocean flow field domain ontology could be further expressed as: O = {C(C1, C2); P(Ps, Pt, Pm, Pf); R(Rt, Rs, Rp, Rse); I(I0, It, Im)}. C1 said basic concept, C2 said related concepts. Ps standed for spatial attribute, Pt for time attribute, Pm for motion attribute, Pf for function attribute. Rt referred to temporal relation, Rs to spatial relation, Rp to attribute relation and Rse to semantic relation. I0 represented a specific instance, It represented the instance evolution set, and Im represented the instance evolution drive. The ontology model could be used to express the ontology structure of the entire ocean flow field, or to represent the ontology structure of a specific subclass of the ocean flow field. The overall structure of the ocean flow field domain ontology could be expressed as:

Oocean flow filed = {{all concepts in Marine flow fields}, {{sea-land position, longitude, latitude, depth}, {formation time, end time, lifecycle}, {velocity, flow direction, flow rate, amplitude}, {the influence of climate, economy and environment}…}, {{before, after, during}, {contained, adjacent, disjoint}, {azimuth relation, distance relation, topological relation}…}, {{instance 1, evolution set of instance 1, evolution driver of instance 1}}, {instance 2, evolution set of instance 2, evolution driver of instance 2},…}}.

5.2 Formal Expression of Ocean Flow Field Domain Ontology Based on OWL

OWL, as one of the most widely used languages for semantic description of ontology, has obvious advantages in the description and expression of domain concepts and conceptual hierarchy relations. However, the ocean flow field has not only a clear conceptual hierarchy, but also a complex spatial-temporal relation. In order to effectively describe and express the ocean flow field domain ontology model, the expansion of OWL language was realized by adding spatial relationship primitives, such as touch, disjoint, after, etc. The semantic description and formal expression of OWL were illustrated as following taking western boundary current as an example.

  1. (1)

    Conceptual hierarchy description of western boundary current

    In the domain knowledge system of ocean flow field, the west boundary flow is a subclass of the boundary flow divided by the regional location of sea current in the land and sea relation, and the western boundary current is paratactic to the eastern boundary flow. The formal expression of its conceptual hierarchy was as follows:

    <owl:ObjectProperty rdf:ID=“Land and sea area location”> <rdfs:domain rdf:resource=“#BoundaryCurrent”/> … <owl:Description rdf:about=“# WesternBoundaryCurrent”/> <owl:Description rdf:about=“# EasternBoundaryCurrent”/> … </owl:ObjectProperty>

  2. (2)

    Attribute description of western boundary current

    The western boundary current has motion properties, function properties, time properties and spatial properties, so the property description of the west boundary flow includes the property structure description and the property constraint description. According to attribute types, attribute constraints could be divided into numerical constraints, object constraints, etc. For example, velocity in motion attributes is numerical constraints, while direction needed to be defined with some references other than numerical values, which could be defined as object class attribute constraints. For example, the flow direction of the western boundary current was from low latitude to high latitude, and its key expression was as follows:

    <owl:onProperty rdf:resource=“#direction”/> <owl:hasValue rdf:resource=“#zonal(from low latitude to high latitude)”/>

  3. (3)

    Description of the relation structure of western boundary current

    The relation structure of western boundary flow included time, space, attribute and semantic relations. At the semantic level, the west boundary current was contained in the boundary flow. In the spatial relation, the west boundary flow was adjacent to equatorial current, but not connected to the eastern boundary flow. In the temporal trend, the western boundary current occurred after the trade(wind) current. The specific OWL was described as follows:

    <owl:Class rdf:ID=“WesternBoundaryCurrent”> <rdfs:subClassOf rdf:resource=“#BoundaryCurrent”/> <rdfs:touch rdf:resource=“#TradeWindDrift”/> <rdfs:disjoint rdf:resource=“#EasternBoundaryCurrent”/> <rdfs:after rdf:resource=“#TradeWindDrift”/> </owl:class>

  4. (4)

    Instance description of western boundary current

    Instance description mainly included attributes, instance evolution set, instance driving force and so on. Taking kuroshio as an example, its motion attributes included direction and speed, and its spatial attributes included longitude, latitude and depth. The evolution set included the black tide in the south China sea, ryukyu current, east China sea black tide, tsushima warm current, kuroshio continuation, etc. Its evolution collection could be expressed as follows:

    <WesternBoundaryCurrent rdf:ID=“Kuroshio” > <owl:unionOf rdf:parseType=“Collection”> <owl:Class rdf:about=“#South China Sea Branch of Kuroshio”/> <owl:Class rdf:about=“#Ryukyu Current”/> … <owl:Class rdf:about=“# Kuroshio Extension”/>

The driving force of the case evolution set was essentially a special constraint, whose expression form was the same as that of the set of evolution. The driving factors of the evolution process of the instance were given by enumeration. In addition, our study expressed the specific constraint of driving force factors according to the expression form of the above attribute constraints.

The formalized expression of the ocean flow field domain ontology based on OWL stored the semantic information as standardized formalized code, and provided strong support for the ocean flow field information query and semantic sharing. To facilitate model and information management, in this paper, ocean flow field domain knowledge was constructed with Protege ontology modeling tool. The specific ontology structure was shown in Fig. 7. Through this system, the target concept and hierarchical structure could be quickly queried, located and displayed.

Fig. 7.
figure 7

Ocean flow field domain ontology constructed based on Protégé.

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6 Conclusion

Considering the importance of ocean flow field in oceanography research, semantic information sharing and deep rule analysis had also become the focus of people’s attention. Therefore, this paper, using the ontological idea, proposed the ontology structure of ocean flow field in the form of six tuples based on the domain knowledge system of ocean flow field and semantic analysis in space-time. In addition, OWL language modeling primitive was extended, taking western boundary current as an example, formal description and expression of ocean flow field domain ontology was carried out. This study could provide methodological support for the integration and sharing of spatio-temporal data of ocean flow field. The analysis of its concept, attribute, spatio-temporal and semantic relationship could be used for knowledge inference in the field of ocean and ocean current field. Combined with natural language and descriptive logic, semantic extraction could be carried out on the latent features of ocean flow field phenomena, so as to achieve the application of big data information mining of ocean flow field and trend prediction based on temporal and spatial characteristics.

Ocean flow field is the result of interaction of various factors and has typical spatio-temporal distribution characteristics. Since this paper is a knowledge system of ocean flow field domain established from the perspective of semantic analysis, the organization and analysis of spatio-temporal process data of ocean flow field are few. The relationship between time and space proposed in this paper can solve the basic expression of time and space and is a preliminary study on the spatio-temporal characteristics of ocean flow field. Future research could be focus on ontology-based spatio-temporal data organization of ocean flow field, owl-based extension of ocean flow field spatio-temporal information expression, ontology-based ocean flow field knowledge inference and spatio-temporal prediction model.