2
A Systematic Review of Ontologies for the Water Domain
Current water research integrates Semantic Web technologies for effective water resources management. Various semantic models have been designed to describe water resources, such as water body, water type, water pipe, water meter, reservoir, catchment, pump and sensor. It is challenging to find the water-related ontologies in one place as recent studies are primarily focused on water resources management by developing different water ontologies. However, no-one has presented a literature review to provide all information on existing ontologies. This chapter presents a systematic literature survey to discuss different existing ontologies for water resources and explore their features and applications. A three-step research methodology has been followed to conduct the detailed systematic literature review, ontology characterization and ontology selection from existing sources. As a survey finding, several resources and repositories have been deeply analyzed, and 25 related studies are found. Further more, 14 ontologies are extracted from the related studies. The main finding is that only seven of them are available online to be reused or guide the development of new water ontologies. A detailed review is presented in the chapter with different parameters of existing water-related ontologies and short descriptions of all these ontologies. This chapter also highlights the application of ontologies in different water domains to analyze the existing concepts that can be reused in any proposed water ontology.
2.1. Introduction
Water is precious for human life, in regular activities like recreation and transportation, and in different areas, such as climate, health, agriculture and energy. The information related to water features, such as water quality, water distribution, wastewater management and flooding, can be acquired by water sensors. Water quality monitoring is also an essential issue as it is time-consuming and expensive to collect water samples manually. Recently, several pieces of research have been focused on the semantic representation of the water domain by representing hydrological concepts, such as water body, water quality and wastewater. Ontologies are considered a semantic model with intense expressivity to capture the knowledge in different water domains. They play a significant role in describing the characteristics and relations among the concepts included in water domains.
Ontologies are efficient in structuring the knowledge and information with a common representation and language to provide a shared vocabulary for presenting different domains’ different classes, properties and other attributes. Ontologies are built to describe the concepts, relationships, restrictions and data properties within a domain and to provide machine readability. In general, ontologies promote accessibility, extensibility and reusability (Howell et al. 2018) and have been successfully applied in different domains, such as buildings, agriculture, energy and transport. Several water ontologies are proposed to organize and specify the concepts and terminologies applied in different water domains, generally composed of hydraulic and hydrological components as a semantic model. Semantic modeling promoted the interoperability between shared information and domain knowledge by developing ontologies. Ontologies are designed to provide interoperability between different water domains. These domains are growing daily to integrate the enormous variety of water-related data. Internet of Things (IoT) technologies play a significant role in dealing with smart water data by using sensing devices. Ontologies have been designed by acquiring the sensor and observation data collected with the help of sensing devices to organize the huge amount of water data and data formats and measurement tasks. Semantic models are developed using OWL and further extend the SSN (Haller et al. 2019) and SAREF (Daniele et al. 2015) to deal with smart water management or frameworks. The SSN ontology facilitates different knowledge management for wireless sensor networks (WSNs) (Katsiri and Makropoulos 2016), while the SAREF ontology models the concepts of sensors and control in smart applications. This chapter has presented a comprehensive literature review of existing ontologies for heterogeneous water domains. A systematic review has been conducted to analyze the existing water ontologies. This review will be helpful to extract the ontologies for reuse in various applications. There are several projects that follow ontology-based approaches to present the information of water domains, such as WatERP (Anzaldi 2015), Hydro Ontology (Brodaric and Hahmann 2014) for water data interoperability, DiHydro Ontology (Katsiri and Makropoulos 2016), SWIM (Reynolds 2014), WISDOM (Zarli et al. 2014), WDTF (Walker et al. 2009), ICT4Water (Curry et al. 2014), Waste Water Treatment Plant (WWTP) Ontology (Ceccaroni et al. 2000), CUAHSI (Kadlec et al. 2015) Hydrologic Information System and many more. To our best knowledge, no-one has covered a comprehensive literature review on water ontologies to extract the features of existing ontologies that can be reused in future ontology development. Our findings suggest reusing the existing ontologies while constructing a new ontology for water domains.
The rest of the chapter is structured as follows. Section 2.2 presents a literature review to conduct a detailed characterization of existing water ontologies. A three-step methodology has been followed to conduct the review. Section 2.3 discusses the different applications of ontologies in the water domain. Finally, section 2.4 presents the discussion and concludes the chapter.
2.2. Literature review
In general, ontologies are reused to integrate with different ontologies and speed up the ontology construction process, thus enabling reuse of the concepts and properties with their definitions that are already evaluated, and providing robustness and consistency to new ontologies (Gómez-Pérez and Rojas-Amaya 1999). It is recommended to reuse ontologies following ontology development guidelines and methodologies by Gómez-Pérez and Rojas-Amaya (1999) as this reduces the cost of ontology construction and engineering tasks such as verification and validation. Reusing ontologies advances application interoperability on the semantic and syntactic level (Bontas et al. 2005). An upper ontology such as BFO (Arp et al. 2015) and SUMO (Pease et al. 2002) also guides the newly developed ontologies as their formalization describes the association between non-material and material concepts along with their attributes, such as Roles, Processes and Qualities.
This section will discuss existing work to show the importance of semantic representation in the water domain. Several studies have been presented to deal with water management by applying Semantic Web technologies and IoT to promote the water domains. This section will cover the comprehensive literature of existing water ontologies and features of water that need to be modeled along with the semantic models for water domains.
2.2.1. Features in the water domain
A feature represents entities (building, building space, rivers and aquifers), qualities (weight, shape, color, dimension, etc.) and amount of materials (Sanfilippo and Borgo 2016). Water features play an important role in several individual activities that are mostly related to climate, health, energy, agriculture, transportation and recreation. Various sensors are used to collect the information of water features by measurements and provide a considerable amount of dynamic data. Brodaric and Hahmann (2014) have presented significant water feature aspects, such as container, supporter, water object, water matter amount and flow. These features are considered under the physical entities category that contains water and other objects. They can be found above, below or on the planetary surface with representative examples, such as puddles, rivers, aquifers and clouds, reservoirs, canals and catchments. Different properties always express these features with an estimated value captured by the sensing devices. For example, a river can have the properties level, velocity, pH, temperature and turbidity with an estimated value, and a lake can have the properties volume, level and temperature (Taylor 2014).
2.2.2. Semantic models in the water domain
Recently, semantic representation in the water domain has been recognized as a significant challenge. With the incremental growth of the IoT in several domains, such as agriculture, building and transportation, attention is being paid to the water domain with ICT techniques. Several semantic models are presented to show the importance of semantics in specific domains. Semantic models promote accessibility, extensibility and reusability (IAB 2015) in the form of ontologies to share and exchange the data in different formats and provide machine understanding to model domain knowledge. Several water resources are presented as a semantic model to design the concepts of water resources and water-related devices, such as actuators, sensors, pumps, valves and reservoirs. Several existing water ontologies that have been discussed in the literature are presented with their different parameters in Table 2.2.
2.2.3. A comprehensive review of ontologies in the water domain
The information about water availability and water consumption will make a solid impact to enhance the quality aspects of water management as it is not easy to find the solution to water resources problems (Blodgett et al. 2016). This section presents a comprehensive review of existing work in ontology-based water management. A three-step methodology (Kitchenham and Charters 2007) has been followed to conduct the systematic literature review of existing water-related ontologies. Deep analysis has been done to analyze the related research deeply and the authors have applied a selection criterion to select relevant papers. Several sources, such as Google Scholar, Scopus, Web of Science, Semantic Web Journal and Vocabularies, are analyzed for collecting related research as discussed in Table 2.1. As a finding, 25 relevant papers are considered to present the comprehensive review in Table 2.2.
The selection of relevant sources is essential for preparing a systematic literature review. This chapter has analyzed several sources to prepare a comprehensive review of any specific domain. Water ontologies are selected after conducting a complete review by following the prepared methodology (Kitchenham and Charters 2007, Espinoza-Arias et al. 2019). Figure 2.1 shows the methodology to conduct the review of existing ontologies in water domains.
This methodology has been categorized in three steps: a systematic review is conducted to acquire the water ontologies related papers in step 1; step 2 characterizes water ontologies with their different features. Finally, ontology selection is discussed in step 3. A comprehensive review is presented in Table 2.2, ontology characterization is discussed in Table 2.3 and finally selected ontologies are presented in Table 2.4.
Table 2.1. Sources to collect the related research
| Source | Type | URL |
|---|---|---|
| Google Scholar (GS) | Database | https://scholar.google.com/ |
| Scopus | Database | https://www.scopus.com/ |
| Web of Science (WoS) | Database | https://apps.webofknowledge.com/ |
| Semantic Web Journal (SWJ) | Journal | https://content.iospress.com/journals/ |
| Linked Open Vocabularies (LOVs) | Ontology Index | https://lov.linkeddata.es |
| Linked Open Vocabularies for Internet of Things (LOV4IoT) | Ontology Catalog | http://lov4iot.appspot.com/ |
Figure 2.1. Three-step methodologies to conduct systematic review
(source: Espinoza-Arias et al. (2019))
Table 2.2. A comprehensive review of existing research
| Ref. | Major techniques | Reasoning type | Representation language | Informative | Features covered | Reused ontologies | Ontology available | Ontology evaluation | Status |
|---|---|---|---|---|---|---|---|---|---|
| García-Castro (2020) | Ontologies, Sensor, Actuator | SPARQL | OWL, RDFs | Yes | Modeling, generating, publishing and exploiting datasets | S4CITY,SAREF, Time, GeoSparql | Yes | Protégé, OOPs, SPARQL | Consider |
| Escobar et al. (2020) | Ontologies, Linked Data | SPARQL | OWL, RDF | No | Modeling, generating, publishing and exploiting datasets | GeoNames | No | SPARQL | Reject |
| Wang et al. (2020) | Ontologies, Sensor, Actuator | SWRL | OWL | Yes | Modeling, generating | SSN, DOLCE | No | Protégé | Reject |
| Corchero (2019) | Ontologies, Sensor | NA | OWL, RDFs | Yes | Modeling, generating, publishing and exploiting datasets | SAREF, time, GeoSparql,time, om | Yes | Protégé | Consider |
| Howell et al. (2018) | Ontologies, Sensor | SPARQL, SWRL | OWL | Yes | Modeling, generating | GIS schema | No | SPARQL | Reject |
| Hahmann and Stephen (2018) | Ontologies | NA | FOL | Yes | Extending HyFO with groundwater concepts | HyFO, DOLCE | No | NA | Reject |
| Poveda-Villalon et al. (2018) | Ontologies | SPARQL | OWL, RDF | Yes | Modeling | SOSA, SAREF | No | NA | Reject |
| Goel et al. (2017) | Ontologies, Sensors | Dynamic Bayesian network based probabilistic reasoning | OWL, RDF | No | NA | NA | No | NA | Reject |
| Howell(2017)et al. | Ontologies,Sensors | SPARQL | OWL, RDF | Yes | generatingModeling, | SSN | No | By domain expert with SPARQL | Reject |
| Stephen and Hahmann (2017) | Ontologies | SWRL | NA | No | Taxonomy | HyFO | NA | NA | Reject |
| Sánchez-de Rivera et al. (2017) | Ontologies,Sensors | SPARQL | NA | No | NA | NA | NA | NA | Reject |
| Wang et al. (2017) | Ontologies, Sensors | SPARQL, SWRL | OWL | Yes | Modeling | SSN/SOSA, geo, DUL, time, space | NA | Protégé, SPARQL | Reject |
| Xiaomin et al. (2016) | Ontologies | SPARQL | OWL, RDFs | No | Modeling | NA | NA | Protégé, SPARQL | Reject |
| Kontopouloset al. (2016) | Ontologies | SPARQL | OWL | Yes | Modeling | 1405430314Ontology | DSHWSYes | Protégé, SPARQL, OOPs | Consider |
| Rahman (2015) | Ontologies | NA | OWL, RDFs | No | Modeling | NA | Yes EUWEF | Protégé | Consider |
| Varanka and Usery (2015) | Ontologies | NA | OWL, RDFs | Yes | Modeling | Geo | Surface water | Protégé | Consider |
| Sinha et al. (2014) | Ontologies | SPARQL | OWL, RDFs | Yes | Modeling | GeoSparql | NA | Protégé | Reject |
| Agresta et al. (2014) | Ontologies, Sensors | SWRL | OWL, RDFs | Yes | Modeling | SSN | NA | Protégé | Reject |
| Kämpgen et al. (2014) | Ontologies | NA | OWL, RDF | No | Modeling | NA | NA | NA | Reject |
| Brodaric and Hahmann (2014) | Ontologies | NA | NA | No | Modeling | NA | NA | NA | Reject |
| Elag and Goodall (2013) | Ontologies | NA | OWL, RDFs | No | Modeling | NA | NA | Protégé | Reject |
| Scheuer et al. (2013) | Ontologies | NA | OWL, RDFs | No | Modeling | NA | NA | NA | Reject |
| Ahmedi et al. (2013) | Ontologies, Sensor | SWRL | OWL, RDFs | Yes | Modeling | SSN/SOSA, geo, DUL, time | Yes InWaterSense | Protégé | Consider |
| Ceccaroni et al. (2000) | Ontologies | Case-Based, Rule-Based | OWL, RDFs | No | Modeling | NA | Yes OntoWAWO | Protégé | Consider |
| Kinceler et al. (2011) | Ontologies | SPARQL | OWL, RDFs | Yes | Modeling | NA | NA | Protégé | Reject |
Table 2.3. Selected ontologies for the water domain
| Ontologies | Reused ontologies | Concept | Year | Online | Published |
|---|---|---|---|---|---|
| SAREF4WATR | SAREF, S4CITY, Time, Geosp | 71 | 2020 | Yes | Yes |
| WaterNexus Ontology | SAREF (Measurements, UnitOfMeasure, Property), GeoSparql, time, om | 30 | 2019 | Yes | Yes |
| DSHWS | Ontology1405430314 | 25 | 2016 | Yes | No |
| EUWEFNexus | No | 157 | 2015 | Yes | No |
| SurfaceWater | Geo | 93 | 2015 | Yes | No |
| OntoWAWO | No | 223 | 2013 | Yes | No |
| xLMINWS.owl | dbpedia, time, SSN | 31 | 2013 | Yes | No |
2.2.3.1. Step 1: A systematic review
A systematic approach has been followed to conduct a complete review. We have analyzed different sources to collect the relevant papers and prepared a descriptive review of 25 related studies in Table 2.1. Different keywords are used to search water ontology-related research, such as “water ontologies”, “semantics in smart water”, “flood ontologies” and “ontology in hydrology”. In total, 11 parameters are examined for all collected sources that explore existing papers’ findings. These parameters extracted primary techniques, reasoning, representation language, metadata, coverage of features, reused and imported ontologies, online availability of ontologies, ontology evaluation and status of ontologies in existing studies. Only sources from the 10 years from 2010 to 2020 are taken into account to conduct the review.
In total, 25 relevant studies are taken into account after conducting a systematic review of existing literature from different sources. Table 2.1 analyzes several features of existing work, such as Major Techniques, Reasoning Type, Representation Language, Informative, Features Covered, Reused Ontologies, Ontology Available, Ontology Evaluation and Status. It is found that 25 studies have used ontologies as major techniques and 22 studies used OWL, RDFs and FOL as representation language. For reasoning, SPARQL is used in 11 studies, six used SWRL, one study used case-based and rule-based reasoning while one study used probabilistic reasoning. In the Informative parameter, 14 studies have complete metadata information of proposed ontologies. In Features Covered, 21 studies have completely covered the modeling feature. It is also analyzed that 15 studies have reused existing ontologies, such as SSN/SOSA, SAREF, om, time, geo and GeoSparql; 7 ontologies are available online; 17 studies show that ontologies are evaluated with different evaluation tools; and the last parameter shows that only 7 studies are further considered as only 7 ontologies are available online for reuse in future development.
Figure 2.2. Parameters covered by conducted studies
Figure 2.2 shows the analysis of parameters covered by studies. According to the status of all existing studies from Table 2.2, a total of 13 studies are considered for ontology characterization due to solid coverage of parameters such as their Description, Owner, Concepts, Year, Published, Online and Sources in Table 2.3.
2.2.3.2. Step 2: Characterization of water ontologies
Ontology characterization is a basic idea to provide the characteristics (Howell et al. 2017, 2020) of existing ontologies. In this section, 13 ontologies are characterized to explore their different features, such as Name attribute to recognize the name of the ontology; the Description of Domain attribute presents the description of the ontology; Owner presents the name of the responsible organization; Entities presents the number of ontology classes and properties; Year presents the creation year of the ontology; and the Published attribute presents that the ontology is published on its web page with ontology documentation. Ontology publication provides (1) a human-interpretable HTML page for navigation and browsing with the help of embedded links; (2) different formats of the ontology, such as Turtle, JSON, RDF/XML and N-Triples. On the other hand, the Online attribute tells us that the ontology is available to download from different sources like GitHub or others but not published. The Sources attribute presents the ontology source to download and reuse in future applications.
2.2.3.3. Step 3: Ontology selection for water management
Step 3 performed the ontology selection approach from the conducted review of existing work. This section focuses on available online ontologies for the water domain. As per our findings, there are seven ontologies online, but only two ontologies are published, and the remaining five are unpublished. Table 2.4 presents those available online with their different dimensions and also explores the reused ontologies.
Table 2.4 shows that only seven ontologies are available online and can be reused in any new proposed water ontology. These ontologies are significant for water domains as they are designed for different purposes and analyzed with their features. The SAREF4WATR ontology, as an extension of the SAREF series, is designed to manage the flow, storage and characteristics of water with different concepts, such as WaterAsset, WaterInfraStructure, WaterMeter, Tariff and their subclasses. The WaterNexus ontology is designed to harmonize the policies and game data to deal with different concepts, such as the NexusComponent concept which represents nexus elements (water, element, food, energy, land-use) as subclasses.
The DSHWS ontology is designed to represent the Domestic Hot Solar Water System that dealt with different concepts, such as AverageDailyUse, AverageOccupancy manages the consumption of water, while System manages Domestic Solar Hot Water system (DSHWS) and SystemComponent manages different aspects like Aperture, Collector, Hydraulics, Installation and Tank. The DSHWS ontology has the Capacity and Size concepts as a subclass of the PhysicalProperty concept to measure the capacity and size of SystemComponent. The EUFNexus ontology presents the legal ontology for nexus and deals with different activities, such as ConsumptionActivity, ProductionActivity and DistributionOfWater, to monitor the activities.
The SurfaceWater ontology presents classes and properties based on attributes of the National Hydrography Dataset, such as TerrainFeatureType to support body of water as an earth feature type, and WaterBody to manage different water resources such as Bay, Lake, Sea and Waterfall. This ontology also presents spatial features such as SpatialExtent, SpatialMeasurement, SpatialMeasurementUnit and SpatialRelation to manage the qualities of continuants. The WAWO ontology presents wastewater management features, such as WaterComposition to manage the CleanWaterComposition and WWComposition; WaterFeatures to handle the ChemicalFeatures, IndicatorParameters, Microbiological, Radioactivity features of water; WaterMass to manage Flow_water_mass and Static_water_mass of water; WaterProducer, WaterQualityParameters, WWMass for Wastewater, WWTP for Waste Water Treatment Plant, WWTreatmentUnit. The InWaterSense (xLMINWS.owl) ontology provides rule-based reasoning over sensor data with different concepts, such as ConductivitySensor, TemperatureSensor, pHSensor and DissolvedOxygenSensor.
This ontology also represents different situations, such as Calibration, Evaluation, Maintenance, Monitoring, Observation and Reporting. This review analyzed the existing water ontologies with their features that can reuse and also analyze reused concepts in existing water ontologies. Table 2.5 discusses the analysis of reused concepts in existing water ontologies. It is observed in Table 2.5 that the top four ontologies, WaterNexus, xLMINWS, SAREF4WATR and DSHWS have reused maximum concepts as recommended by the methodologies and guidelines. However, the next three, SurfaceWater, EUWEFNexus and OntoWAWO ontologies, did not reuse existing ontology, and all concepts are defined in themselves. The SurfaceWater ontology reuses only one concept point from the geo ontology, it has created several existing concepts, such as FeatureObjects and Waterbody while these concepts are already available in the OntoWAWO ontology. The EUWEFNexus ontology has created 157 new concepts, such as Value, Waste, Animal, Human, while these concepts are also already available in the OntoWAWO ontology.
As it is analyzed that the last three ontologies did not follow the suggested guidelines and methodologies for ontology construction, the proposed literature review of existing water ontology will help reduce the construction costs and time by reusing the existing concepts of water ontologies rather than to creating a new one. Section 2.3 will discuss the applications of these ontologies.
2.3. Applications of ontologies in the water domain
Ontologies are an important tool used to overcome the semantic aspects of several challenges by enabling digital representations of intended meanings to be associated with data and other resources. Several water ontologies are developed for different purposes, such as SAREF4WATR, WaterNexus Ontology, DSHWS, EUWEFNexus, SurfaceWater, OntoWAWO, xLMINWS, SWIM, WISDOM, WatERP, WaterML2 and WDTF. In this section, we focus on different purposes to explore the ontology usage in water domains.
Table 2.4 discusses different parameters such as purposes, coverage, advantage and the major component of the existing ontologies. These features show water ontologies’ applications for different purposes along with different features and advantages. The purpose parameter discussed the aim of the design of the ontology. The coverage parameter shows the features covered by the proposed ontology. The advantage parameter shows the primary significance of ontology. The main component presents the significant concepts that fulfill the ontology coverage. It is analyzed from Table 2.4 that reusability is the main application of all ontologies while flexibility, sustainability, extensibility, operationally, adequacy and rule-based reasoning are other applications of existing water ontologies. The SAREF4WATR, SWIM and xLMINWS ontologies are designed with sensing devices for monitoring quality and measuring water consumption dynamically. SurfaceWater is the only ontology developed to organize the geographical features, while OntoWAWO is significant for wastewater management. The DSHWS ontology can be reused to manage the domestic solar hot water system in buildings. WaterML2 and WDTF are significant in managing time-series data, while others (WaterNexus, EUWEFNexus, WISDOM and WatERP) can be used in general water management.
Table 2.4. Ontology applications in water domains
| Ontology | Purposes | Coverage | Advantages | Main component |
|---|---|---|---|---|
| SAREF4WATR | Ontology is designed to manage the water infrastructure, asset, billing and remote monitoring by smart devices | To acquire the information of Water Asset, Infrastructure, Property, Devices and Tariff | Reusability, Sustainability | Water asset, Water infrastructure, Tariff, Property, Water meter, Water device |
| WaterNexus | nexusOntologyvariablesassociatesaccordingwaterto different regions | To assimilate information from various data sources referring water, land use, energy, climate change | Reusability | Scale, PhysicalObject, Model, Region, PolicyGoal |
| DSHWS | Ontology-based decision support tool designed to promote the usage of domestic solar hot water systems in building | Computes number of occupants, need of daily hot water and location | Sustainability, Adaptability, Flexibility | Household,Tank, Collector,System,HydraulicsAperture, |
| EUWEFNexus | Nexus aspect involves dealing with water, energy and food with EU laws and policies. The legal ontology has been developed for nexus | Legal knowledge acquisition for nexus | Adequacy, Reusability, Operationality | Living Organism, Energy, Authority, Quality, Material, Resource |
| SurfaceWater | Designed to model the semantic concepts of National Hydrology Datasets with GIS information such as points, areas and lines as feature objects | Hydrographic, geographical and terrain features | Reusability, Flexibility | WaterBody, Event, Feature, SpatialExtent, SpatialMeasurement |
| OntoWAWO | WAWO harmonizes the knowledge about WWTP management | WAWO capture chemical, physical and microbiological knowledge of a WWTP | Reusability | LandUse, WaterFeatures, WWTP, WWTreatmentUnit |
| xLMINWS | Ontology developed for water quality monitoring with the help of sensing devices and making observations | Captures the pH, temperature, water-relevant contaminants, bodies of water and amount of ammonia | Reusability, Rule-based Reasoning | PersonDevice, SensorNode, pHSensor, TemperatureSensor |
| SWIM | Presented as an IoT-based semantic model for water interoperability | Provideswater thesectordescriptiondevices for | Not Applicable | Pumps, Sensors,ReservoirsValves and |
| WISDOM | Captures domestic knowledge and integrates water data value chainmetertoandcontextualizebehavioral smartdata | Physical element types (storage, transfer, etc.) and types of actors (bulk water suppliers, consumers, regulators and water utilities) | Reusability, Extensibility | WaterPipe, WaterType, WaterTariff, WaterBill |
| WatERP | Ontology developed to present the water supply domain and demand knowledge | Provides a new way of association and improves the water resource management domain knowledge | Reusability | WaterResourceManagement, Regulators, Water Utilities, and Water Bulk Suppliers |
| WaterML2 | Developed to provide a common format for time series data and constructed on existing standards such as GML | Used to exchange several hydro-meteorological observation and measurements | Reusability | Measurement, MeasurementTimeseries |
| WDTF | Presented as precursor of WaterML2 as a transferring format for forecasting data and flood warnings | Not Applicable | Not Applicable | HydroCollection, TimeSeriesObservation |
Table 2.5. Reused concepts in existing water ontology
| Ontology | New concepts | Reused concepts | Total concepts | Reused (%) |
|---|---|---|---|---|
| WaterNexus | 19 | 11 | 30 | 37 |
| xLMINWS.owl | 21 | 10 | 31 | 36 |
| SAREF4WATR | 47 | 24 | 71 | 33 |
| DSHWS | 17 | 8 | 25 | 32 |
| SurfaceWater | 92 | 1 | 93 | 1 |
| EUWEFNexus | 157 | 0 | 157 | 0 |
| OntoWAWO | 223 | 0 | 223 | 0 |
2.4. Discussion and conclusion
The main contribution presented is to highlight the reusable ontologies for the water domain. It is always suggested that one should not reinvent the wheel and reuse existing concepts rather than design from scratch. In this chapter, a systematic literature review has been conducted to explore the knowledge from shallow to deep analysis of existing ontologies in the water domain. The main advantages of the proposed work are: (1) acquiring existing water ontologies in one place; (2) exposing the semantic richness of existing water ontologies; (3) extracting online available ontologies for reuse; and (4) applicability of existing water ontologies. A three-step methodology has been followed to conduct the systematic literature review, characterize existing ontologies and select available online ontologies.
The key contribution is to analyze the different sources such as Scopus, Google Scholar, WoS, Journals and Repositories to acquire and select related studies, hence characterizing the related ontologies with water domains and finally extracting only online available water domain ontologies. One of the significant benefits of the systematic review is exploring existing water domain ontologies for reusing with domain-specific requirements. Domain experts can easily reuse the existing ontologies to reduce the construction efforts and cost. It supports the reuse of the modeled knowledge within multiple domains. As a finding, 14 ontologies are taken into account and categorized based on different features that are covered by conducted studies, but only seven ontologies (SAREF4WATR, WaterNexus, DSHWS, EUWEFNexus, SurfaceWater, OntoWAWO and xLMINWS) are available online to reuse; hence the other seven (SWIM, WISDOM, WatERP, WaterML2, WDTF, Utility Network Schemas and Hydrologic Ontology) ontologies were discarded.
Although these ontologies are designed for real application and have significant features to reuse in new development, they are lacking reusability in general as users need approval from the owners to reuse these models. Online available ontologies have used different reasoning types, for example the DSHWS ontology used SPARQL for reasoning, the xLMINWS (InWaterSense) ontology used SWRL rules and the OntoWAWO ontology used case-based and rule-based reasoning. In contrast, reasoning information is not available in the other three ontologies (WaterNexus, EUWEFNexus and SurfaceWater). It is also analyzed that these ontologies reused several existing ontologies, such as SAREF (Measurements, UnitOfMeasure, Property), GeoSparql, time, om, dbpedia and SSN. Existing ontologies, SAREF4WATR, WaterNexus, xLMINWS and DSHWS, have reused a maximum of concepts to reduce construction cost, effort and time.
To our best knowledge, no such study has been previously conducted to present a comprehensive study of semantic water resources models. As a future scope, this review will help identify the existing work and ontologies for reuse to propose a new water ontology.
2.5. References
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Note
- Chapter written by Sanju TIWARI and Raúl GARCÍA-CASTRO.