Conference PaperPDF Available

An Ontology of Soil Properties and Processes


Abstract and Figures

Assessing the Underworld (ATU) is a large interdisciplinary UK research project, which addresses challenges in integrated inter-asset maintenance. As assets on the surface of the ground (e.g. roads or pavements) and those buried under it (e.g. pipes and cables) are supported by the ground, the properties and processes of soil affect the performance of these assets to a significant degree. In order to make integrated decisions, it is necessary to combine the knowledge and expertise in multiple areas, such as roads, soil, buried assets, sensing, etc. This requires an underpinning knowledge model, in the form of an ontology. Within this context, we present a new ontology for describing soil properties (e.g. soil strength) and processes (e.g. soil compaction), as well as how they affect each other. This ontology can be used to express how the ground affects and is affected by assets buried under the ground or on the ground surface. The ontology is written in OWL 2 and openly available from the University of Leeds data repository: http:// doi. org/ 10. 5518/ 54.
Content may be subject to copyright.
An Ontology of Soil Properties and Processes
Heshan Du1, Vania Dimitrova1, Derek Magee1, Ross Stirling2, Giulio Curioni3,
Helen Reeves4, Barry Clarke1, Anthony Cohn1
1University of Leeds
2Newcastle University
3University of Birmingham
4British Geological Survey
Abstract. Assessing the Underworld (ATU) is a large interdisciplinary
UK research project, which addresses challenges in integrated inter-asset
maintenance. As assets on the surface of the ground (e.g. roads or pave-
ments) and those buried under it (e.g. pipes and cables) are supported
by the ground, the properties and processes of soil affect the performance
of these assets to a significant degree. In order to make integrated deci-
sions, it is necessary to combine the knowledge and expertise in multiple
areas, such as roads, soil, buried assets, sensing, etc. This requires an
underpinning knowledge model, in the form of an ontology. Within this
context, we present a new ontology for describing soil properties (e.g.
soil strength) and processes (e.g. soil compaction), as well as how they
affect each other. This ontology can be used to express how the ground
affects and is affected by assets buried under the ground or on the ground
surface. The ontology is written in OWL 2 and openly available from the
University of Leeds data repository:
Keywords: OWL ontology, soil property/process, asset maintenance
1 Introduction
Assessing the Underworld (ATU) project is a large interdisciplinary UK research
project, which addresses challenges in asset maintenance, especially how to re-
duce the economic, social and environmental costs or impacts of streetworks
required in the maintenance of roads and buried assets (e.g. pipes and cables).
Existing asset management systems (e.g. UK Pavement Management Systems)
can help local authorities or utility companies with financial reporting or assess-
ing the economic costs of the construction, repairing and replacement of assets,
but provide less support in assessing the environmental/social impacts and the
impacts on other assets, which are also important for enabling better informed
decisions in asset maintenance. To establish the total cost (social and environ-
mental impacts along with economic costs) of asset maintenance activities, it
is essential to understand how assets affect each other and how they affect or
are affected by the natural environment and human activities. Within the ATU
project, we are developing a series of ontologies based on the knowledge and
2 H. Du et al.
expertise in multiple areas for describing how buried assets, soil, roads, the nat-
ural environment and human activities affect each other. The ontology of soil
properties and processes (OSP) is a central ontology in this series.
The condition of an asset can affect and be affected by assets close to it. For
example, a water pipe burst increases the water content of the soil (including
man-made fills) surrounding it. As a consequence, the strength of soil decreases,
which has negative effects on the function of soil for supporting buried pipes and
cables nearby, as well as the roads or pavements above. In many interactions
between assets, as shown in the example above, soil plays a role as a medium. In
other words, the conditions of roads and buried assets affect each other through
soil, and soil directly affect and is affected by the conditions of roads and buried
assets. Therefore, when assessing the impacts of decisions (e.g. whether to fix a
water pipe leakage immediately) for surface or buried asset maintenance, it is
important to have a basic knowledge of soil properties and processes.
Soil has many properties and is involved in various processes, as described in
[16, 15]. The relationships between soil properties and processes are complicated.
Whilst it is often easy to know what a process or property affects directly,
it is more difficult for people to answer questions like ‘what are the factors
affecting or affected (directly or indirectly) by a particular property or process
of soil?’. To answer such questions, it is necessary to handle information about
soil properties, processes and their relationships automatically. This requires a
proper ontological model and automated reasoning.
There exist several ontologies [17,9,20,12,7,18,8,1,2], where soil is defined.
Some of them [17, 7, 1, 2] are general environmental or agricultural ontologies,
whilst others are specialized for describing soil. These soil ontologies [9, 20, 12,
18, 8] are not publicly available. Most of the existing soil ontologies [9, 20, 18, 8]
describe classifications of soil or different types of soil but do not elaborate the
various soil properties and processes. The ontologies described in [12, 2] define
some soil physical/chemical properties, but limited to those relevant to farming
or agricultural applications. None of the existing ontological models provides a
systematic and comprehensive description of soil properties and processes, and
none of them defines how soil properties and processes affect each other.
The paper addresses this gap. We present a new ontology for describing soil
properties and processes, as well as how they affect each other. It reuses and
specifies high-level classes in NASA’s Semantic Web for Earth and Environ-
mental Terminology (the SWEET ontology) [17], which is widely adopted and
extended. The ontology is developed using the NeOn methodology [19]. It is
written in OWL 2 Web Ontology Language Manchester Syntax [14], which is
based on description logic (DL) [6]. The DL expressivity of the ontology is SRI,
allowing transitive relations and inverse relations5. The ontology contains 592
concepts and 2243 relation statements (OWL logical axioms), which are based
on the knowledge of soil experts6, the SWEET ontology [5, 17], English dictio-
5To avoid confusion, we call ‘OWL object properties’ relations.
6Soil experts were involved in the development and evaluation of the OSP ontology.
They checked 1407 relation statements for describing 193 main classes.
An Ontology of Soil Properties and Processes 3
naries [3, 4] and a textbook on soil physics [16]. The ontology, together with
a tutorial of viewing and querying it, a translation of its main relation state-
ments in natural language and a feedback form, is publicly available [11] from
the University of Leeds data repository:, under
the license Creative Commons Attribution 4.0 International (CC BY 4.0)7.
The rest of the paper is structured as follows. Section 2 describes how the
main concepts and relations are defined in the ontology. Section 3 explains how
to reason with and query the ontology. Section 4 illustrates the extensibility of
the ontology. Section 5 discusses its applications. Section 6 concludes the paper.
2 Defining Soil Properties and Processes
The ontology of soil properties and processes (OSP) defines two main high-level
classes or categories: SoilProperty and SoilProcess. The classes SoilProperty and
SoilProcess are specifications of the classes Property and Process in the SWEET
ontology [5, 17] for soil. A property is an attribute, quality, or characteristic of
something [4]. A soil property is a property of soil. A process refers to a series of
changes that happen naturally over time [3,4]. A soil process is a process involv-
ing soil. Following the style of the SWEET ontology, we define different kinds of
soil properties and processes as classes in the OSP ontology and classify them
into physical, chemical and spatial/biological categories. Table 2 summarizes
the number of subclasses of SoilProperty and SoilProcess in different categories.
Though the OSP ontology covers classes in all these categories, it mainly de-
scribes soil physical properties and processes.
Table 1. Number of subclasses of SoilProperty and SoilProcess in the OSP ontology
SoilProperty Subclasses SoilProcess Subclasses
SoilPhysicalProperty 176 SoilPhysicalProcess 111
SoilChemicalProperty 16 SoilChemicalProcess 29
SoilSpatialProperty 4 SoilBiologicalProcess 8
In addition to classifying soil properties and processes into different cate-
gories as the SWEET ontology does, we also define soil properties and processes
regarding how they affect each other. The main types of relations defined in the
OSP ontology are hasImpactOn and its inverse influencedBy, meaning ‘affects or
changes’ and ‘is affected or changed by’ respectively. The relations hasImpactOn
and influencedBy are both defined as transitive. A process qhasImpactOn a
property p, if qcauses a change in p. A property phasImpactOn a process q,
if a change in pchanges how the process qgoes. A property p1hasImpactOn a
property p2, if a change in p1causes a change in p2. A process q1hasImpactOn
4 H. Du et al.
a process q2, if q1changes how the process q2goes. Other relations defined in
the OSP ontology include hasPossibleCause and its inverse hasPossibleEffect,
meaning ‘has a possible reason’ and ‘has a possible consequence’ respectively.
The word ‘possible’ means the cause/effect exists in some situation, but may
exist or not exist in a particular real world situation considered.
In the OSP ontology, we define relationships between soil properties and
processes at the concept level rather than at the individual level. For example,
we express ‘soil water content hasImpactOn soil strength’ in description logic as
SoilWaterContent v ∃hasImpactOn.SoilStrength. Such relation statements are
not defined for a particular type of soil at a certain location and depth, but
generally applicable to any soil. The statement ‘soil water content hasImpactOn
soil strength’ means that for any soil, a change in its water content causes a
change in its strength8. Similarly, the statement ‘soil compaction hasImpactOn
soil air content’ means for any soil, a compaction applied to it changes the air
content of it. The statement ‘soil compaction hasImpactOn soil air movement’
means for any soil, a compaction applied to it changes how the air in it moves.
The statement ‘soil air movement is influencedBy soil porosity’ means for any
soil, a change in its porosity changes how the air in it moves. Note that inverse
relations are not always reciprocally asserted in the manually-written ontology.
We rank subclasses of SoilProperty and SoilProcess based on their usages or
how many statements a class is involved. If a class is involved in a statement,
then any class equivalent to it is also considered to be involved in the same
statement. Table 2 lists the top 20 classes in this ranking, which illustrates the
main soil properties and processes defined in the OSP ontology well. For each set
of equivalent classes, only one class in it is included in the list to represent the
whole set. When naming a soil property/process class, ‘Soil’ is added to the front
to avoid potential name conflicts. A class name indicates its intended meaning.
For instance, the class name SoilWaterInfiltration is intended to represent the
concept of ‘infiltration of water into soil’.
3 Querying the OSP ontology
Description logic (DL) reasoners can be used to reason about statements in the
OSP ontology. A basic question a reasoner can answer is whether a statement
(e.g. ‘SoilWaterEvaporation hasImpactOn SoilStrength’) can be inferred from
statements in a given ontology. A justification based explanation framework [13]
can be used together with a DL reasoner to explain how a new statement is
inferred from existing statements. By reasoning with the OSP ontology, more
advanced questions can be answered, such as, for given a class C, list all the
classes that ChasImpactOn or influencedBy.
8According to DL/OWL semantics, it means ‘every soil water content hasImpactOn
some soil strength’. The interpretation adopted here is more strict, however, this
makes more sense for soil experts and the standard DL inference rules are still
sound. In practice, we only expect the model to be applied to a local spatial context.
An Ontology of Soil Properties and Processes 5
Table 2. Ranking the subclasses of SoilProperty and SoilProcess in the OSP ontology
by the number of statements involved (usages) — the Top 20
SoilProperty Usages SoilProcess Usages
1 SoilMoistureContent 93 SoilAeration 70
2 SoilStructure 93 SoilCompaction 59
3 SoilTexture 67 SoilWaterInfiltration 40
4 SoilPorosity 62 SoilCrusting 40
5 SoilTemperature 59 SoilWaterEvaporation 39
6 SoilStrength 52 SoilAggregation 39
7 SoilClayMineral 49 SoilWaterMovement 32
8 SoilBulkDensity 40 SoilErosion 29
9 SoilOrganicMatterContent 39 SoilDrying 29
10 SoilHydraulicConductivity 36 SoilShrinkage 28
11 SoilParticleSpecificSurfaceArea 31 SoilWaterRetention 28
12 SoilClayContent 30 SoilSoluteTransport 26
13 SoilPoreSizeDistribution 28 SoilMicrobialActivity 21
14 SoilCohesion 23 SoilHardsetting 20
15 SoilPlasticity 22 SoilWaterTransmission 19
16 SoilMoisturePotential 18 SoilFreezing 19
17 SoilParticleArrangement 18 SoilDeformation 18
18 SoilBearingCapacity 18 SoilSwelling 18
19 SoilOxygenConcentration 18 SoilDispersion 18
20 SoilAirContent 17 SoilGaseousEmission 17
A simple way to reason with and query the OSP ontology is to use an on-
tology editor Prot´eg´e9and its reasoner plugins. Within the DL query tab of
Prot´eg´e, we can execute queries like getting all subclasses of the class expression
‘(hasImpactOn some SoilStrength) and SoilProperty’. For each class listed in the
query results, its explanations can be obtained easily. We provide a tutorial [10]
illustrating (with figures and examples) how to query the OSP ontology. It is
available at:, the same DOI of the OSP ontology.
4 Extensibility of the OSP ontology
The OSP ontology extends three other top-level classes, Phenomena,
HumanActivity and Substance, of the SWEET ontology [5, 17]. A phenomenon
refers to a fact/situation that exists and can be observed [3, 4]. A human ac-
tivity refers to a thing that a person/group does or has done [4]. The con-
cept Substance [5] covers living entities and non-living entities, such as animals,
plants, material things, etc. By including these concepts, the OSP ontology can
express how soil properties and processes affect/are affected by other environ-
mental factors (such as water, air, weather and trees) and human activities.
6 H. Du et al.
For each of the five top-level classes, its next-level classes in the OSP ontology
are shown in Fig. 1. Some of the next-level classes (e.g. PlanetaryPhenomena,
and MaterialThing) are inherited from the SWEET ontology, whilst others (e.g.
PlantProcess and SoilSubstance) are created by specifying high-level concepts in
the SWEET ontology for a particular type of objects. All the high-level categories
can be extended and enriched further easily by reusing concepts in the SWEET
ontology and following similar ways as defining soil properties and processes in
Section 2. In addition, more high-level categories can be added easily and linked
to the existing classes. We are developing ontologies for describing pipe/road
properties, processes and phenomena (e.g. defects or failures) in a similar way
and linking them to the OSP ontology.
Fig. 1. Class hierarchies of the five top-level classes in the OSP ontology
Note that the OSP ontology mainly describes soil properties and processes
from a soil science perspective, but also captures main terminologies (e.g. soil
compaction) used in engineering applications. We are aware that terminological
differences exist between soil scientists and geotechnical engineers. We follow the
concepts defined in soil science mainly because they are more understandable by
non-specialists. The OSP ontology can be extended and enriched with parame-
ters10 (e.g. soil shear strength) and calculations defined in soil mechanics [15].
5 Application
The OSP ontology is intended to be used in a software system that supports
inter-asset management/maintenance decisions (ATU decision support system).
10 The OSP ontology describes several parameters used for assessing soil structure,
under a class called SoilStructureIndex.
An Ontology of Soil Properties and Processes 7
When making decisions for maintaining a single asset, people (e.g. local author-
ities and utility companies) often consider the economic cost but may not be
aware or take into account the impacts of their decisions or actions on other
assets, the natural environment and our daily life. The ATU decision support
system will prompt people into asking a series of questions, the answers to which
will lead to a complete understanding of the economic, social and economic im-
pacts of different options for dealing with an asset defect/failure. By developing
the OSP ontology, we establish a general framework, which extends the SWEET
ontology, for expressing how properties, processes, phenomena, human activities
and substances affect each other. In this way, many environmental and social
impacts can be represented in an ontology or inferred from it using automatic
reasoning. The OSP ontology can help users understand the environmental im-
pacts of a process or a human activity related to soil. For example, knowing that
a water pipe leakage affects soil water content, we may ask the OSP ontology
about the impacts of a change in soil water content. By executing a DL query
of getting subclasses of ‘influencedBy some SoilWaterContent’ in Prot´eg´e, a list
of 264 classes is returned11, including various soil properties and processes, as
well as factors like building stability, plant growth, air quality, water quality, etc.
For classes listed, explanations are provided, which are useful for identifying and
understanding indirect or hidden impacts.
The soil properties and processes described in the OSP ontology have many
agricultural, engineering and environmental applications (see the subclasses of
SoilFunction). For example, the OSP ontology describes how the growth of a
plant is affected by soil and other environmental factors. The OSP ontology can
be applied to a range of contexts that require descriptions of soil properties or
processes. For example, the EU project NeTTUN12 utilises soil properties and
processes to provide contextual factors for tunnel construction and maintenance.
By extending the OSP ontology and linking it to ontologies for describing the
environment, infrastructure assets, human activities and economic cost models,
we envisage that the OSP ontology will make a useful contribution to ontological
models for environmental, social and economic sustainability.
6 Conclusion
We present a new ontology OSP for describing soil properties, processes and how
they affect each other. It is created using reliable knowledge sources and extends
the SWEET ontology. It can be reasoned with automatically using DL reasoners
and queried using Prot´eg´e. The OSP ontology helps people to understand indi-
rect and complicated relationships between soil properties and processes, as well
as many environmental impacts of a process or a human activity related to soil.
The OSP ontology is easy to (re)use, easily extensible, generally applicable, pub-
licly available and findable. As the ATU decision support system develops, we
will maintain and enhance this ontology, making new versions publicly available.
11 Different types of factors can be filtered out easily using DL query.
8 H. Du et al.
Acknowledgments. This research is supported by EPSRC under grant no.
EP/K021699/1 which we gratefully acknowledge. The final publication is avail-
able at Springer via 0_4.
1. Eionet GEMET Thesaurus., 2015.
2. AGROVOC Multilingual agricultural thesaurus., 2016.
3. Cambridge Dictionaries Online., 2016.
4. Oxford Dictionaries., 2016.
5. SWEET: Semantic Web for Earth and Environmental Terminology., 2016.
6. F. Baader, D. Calvanese, D. L. McGuinness, D. Nardi, and P. F. Patel-Schneider,
editors. The Description Logic Handbook. Cambridge University Press, 2007.
7. P. L. Buttigieg, N. Morrison, B. Smith, C. J. Mungall, and S. E. Lewis. The
environment ontology: contextualising biological and biomedical entities. Journal
of Biomedical Semantics, 4:43, 2013.
8. C. Deb, S. Marwaha, P. Malhotra, S. Wahi, and R. Pandey. Strengthening soil tax-
onomy ontology software for description and classification of USDA soil taxonomy
up to soil series. In Proceedings of the 2nd International Conference on Computing
for Sustainable Global Development, pages 1180–1184, 2015.
9. A. dos Santos Apar´ıcio, O. L. M. de Farias, and N. dos Santos. Integration of
Heterogeneous Databases and Ontologies. Cadernos do IME-S´erie Inform´atica,
21:4–10, 2006.
10. H. Du and A. Cohn. A Tutorial of Viewing and Querying the Ontology of Soil
Properties and Processes. Technical report, University of Leeds, 2016.
11. H. Du and A. Cohn. An Ontology of Soil Properties and Processes. University of
Leeds. [Dataset]., 2016.
12. T. Heeptaisong and A. Shivihok. Soil Knowledge-based Systems Using Ontology.
In Proceedings of the International MultiConference of Engineers and Computer
Scientists, pages 1–5, 2012.
13. M. Horridge. Justification Based Explanation in Ontologies. PhD thesis, University
of Manchester, 2011.
14. M. Horridge and P. F. Patel-Schneider. OWL 2 Web Ontology Language Manch-
ester Syntax., 2012.
15. J. Knappett and R. Craig. Craig’s Soil Mechanics. CRC Press, 2012.
16. R. Lal and M. K. Shukla. Principles of Soil Physics. CRC Press, 2004.
17. R. G. Raskin and M. J. Pan. Knowledge representation in the semantic web
for Earth and environmental terminology (SWEET). Computers & Geosciences,
31(9):1119–1125, 2005.
18. P. Shivananda and P. S. Kumar. Building Rules Based Soil Classification Ontol-
ogy. International Journal of Computer Science and Information Technology &
Security, 3(2), 2013.
19. M. C. Su´arez-Figueroa, A. G´omez-P´erez, and M. Fern´andez-L´opez. The NeOn
Methodology for Ontology Engineering. In Ontology Engineering in a Networked
World, pages 9–34. 2012.
20. M. Zhao, Q. Zhao, D. Tian, P. Qian, and X. Zhang. Ontology-based intelligent
retrieval system for soil knowledge. WSEAS Transactions on Information Science
and Applications, 6(7):1196–1205, 2009.
... THE OntoPIE represent the essential terms of the pile integrity evaluation system which includes the degree, length, depth, and integrity category of the pile defects. In [14], authors proposed an ontology of soil properties and processes for representing soil properties, processing and their interaction in order to make integrated decisions, and to combine the knowledge and expertise in multiple areas, such as roads, soil, buried assets, sensing, etc. [15,16] proposed an ontology-based simulation environment in agriculture and natural resources for constructing models and representing equations and symbols in a formal ontology language, the utility of the approach by building moderately complex models of soil-water and nutrient management. This approach has the advantage of making models more explicit and better defining the meaning of symbols used in a model. ...
... We can reuse all or part of these ontologies after having adapted them to our needs. Some concepts proposed by [12][13][14]17] are reused. ...
... Identification and structuring of ontology concepts: To identify the main concepts of ontology, the concepts proposed by [12][13][14]17] are extracted and enriched them with the concepts analyzed in the Soil-Structure Interaction model proposed in literature and the concepts proposed by civil engineering experts. These concepts are organized and structured in an ontology called "OSSIWAE". ...
Full-text available
Decisions within the company, managers in countries or regions are made by one or more groups of decision-makers. The management of natural disasters involves several specialized decision-makers (experts, firefighters, police officers, drivers,). The aim of the chapter is to improve decision making in the context of natural disasters situation. Zero risk in the world does not exist due to natural phenomena that occur randomly and appear suddenly. It is essential to manage the risks in the situation of natural disasters and also to confront the influence of natural disasters on the phenomenon of Soil-Structure Interaction. For this, this chapter presents a conceptual architecture of a knowledge base to manage the risks of natural disasters remotely by a Geographic Information system (GIS) and embedded systems. This architecture is based on the integration of data via different sources of information (GIS, satellites, electronic sensors and comments from experts). To properly manage this information, this chapter uses the ontology of Soil-Structure Interaction With Agents External (OSSIWAE) in the context of the Internet of Things (IoT). A case study is conducted on a prototype of a model for building a structure three-story for testing the usability of the proposed architecture.
... Some studies have implemented KG technology to solve environmental problems. Du et al. (2016) constructed an ontology database for describing soil properties such as soil strength, and soil processes such as soil compaction, to address challenges in combining knowledge and expertise in multiple areas while assessing the value of soil environment in the UK. Sermet and Demir (2018) designed an intelligent system, aimed at improving public preparedness for natural disasters, by building a knowledge engine containing voice recognition and natural language processing based on a generalized ontology that extracts data from environmental observations, forecast models, and disaster knowledge databases. ...
... This study defined the label, type, and relationship of entities in urban soil environment using expert experience and literature review (Schwaller et al., 2021;Zhao et al., 2015). Meanwhile, several existing ontologies of general knowledge or professional domain, such as the ontology of soil property, were used for reference; the process was constructed by Du et al. (2016) and Microsoft Concept Graph (Zhang and Yan, 2017). ...
Full-text available
Contaminated sites are a main cause of urban soil problems and have led to increasing pollution and public risk in China as a result of the rapid growth of industrial and urban land use. Because land pollution involves extensive multi-source heterogeneous information, identifying the risk of urban soil pollution efficiently and predicting pollution-related events are important for urban environmental management. Knowledge graphs (KGs) have unique advantages in dealing with massive amounts of information. This study attempts to construct a KG of contaminated sites in South China to explore its feasibility and effectiveness in urban soil environmental management. The results demonstrate that KGs have a favorable effect in information retrieval, knowledge reasoning, and visualization. Studied cases in this article demonstrate that the KG model can achieve many functions, including the display of global information of polluted sites, and discovery of regional distribution of characteristic pollutants and main pollutants of specific industries, based on special query syntax. However, this approach is limited by some technical difficulties, such as knowledge mining of natural resources, which must be overcome in future studies to improve the operability of KG technologies.
... Generally, any failure or any condition which may cause failure in geo-structures, as well as their supporting ground, could potentially result in substantial damage to the infrastructure and disruption to their serviceability, and subsequently could affect society's function , (Du, et al., 2016), (Clarke, et al., 2017). ...
Full-text available
Measuring soil water content is crucially important and can affect soil strength which is a key parameter in analysis, design and monitoring of geo-structures. In this study, an optical fibre Bragg grating (FBG) sensor inscribed in Polymer Optical Fibre (POF) was developed and for the first time its ability to measure soil water content was investigated. The sensitivity of the sensor to different values of gravimetric soil water content under different compaction conditions of loose and normal compaction was tested. The effect of soil temperature on the sensor’s performance was considered. To assess the sensor’s implementation, accuracy and reliability, a commercial soil water content probe (SM150), which measures volumetric soil water content was employed. The results indicate that the developed sensor when calibrated correctly, is able to provide detailed data on any minor variation of soil water content (e.g. 0.5%) with high precision. The outcomes of this study define an additional capability of the POFBG sensors which is significantly important for long-term performance monitoring of geo-structures.
... Researchers have developed or attempted to develop ontology related to the schematics in Table 31 (Table 32). Disaster management ontology: (Babitski et al., 2009;Bénaben et al., 2008;Elmhadhbi et al., 2020;Joshi et al., 2007;Thompson, 1995;Xu and Zlatanova, 2007) Natural disaster ontology: (Hasan et al., 2015;Ramamonjisoa, 2012;Spalazzi et al., 2014;Zechar et al., 2005) (Frank, 2003;Mark et al., 1999;Smith andVarzi, 1999, 2001;Sorokine and Bittner, 2005;Witcher, 1999) (Beck et al., 2009;Du et al., 2016;Frank, 2003;Galton, 2009) Specifically mentions the relationship between reality and concept: (Frank, 2001(Frank, , 2003Mark and Turk, 2003;Smith and Mark, 2001) In addition to all other literature listed, modeling steps using ontology: (Durbha and King, 2005;Elag and Goodall, 2013;Rutherford et al., 2015) Table 31 g) The types of relations between "part" component and "whole" components Particularly about part-whole relationships: (Bittner et al., 2004;Mark et al., 1999;Smith and Varzi, 2000) The following list summarizes the recurring themes that appeared in the data categorization schematics in this dissertation. In this section, "entity" is used to represent "anything that exists, including objects, processes, and qualities" (Arp et al., 2015). ...
... Assim, qualquer indivíduo que preencha a expressão lógica é inferido como um membro desse conceito. A ontologia pode ser representada pela linguagem OWL que possui a capacidade de suportar interoperabilidade semântica para troca e compartilhamento de conhecimento de informações entre diferentes sistemas [2,3,11,14]. ...
... The Ontology of Soil Properties and processes (OSP) [96] combines knowledge on multiple areas, such as roads, soil, buried assets, sensing to the purpose of supporting integrated decision making. The ontology describe soil properties (e.g., soil strength) and processes (e.g., soil compaction) and the mutual relationships. ...
Full-text available
The increasing interconnections of city services, the explosion of available urban data, and the need for multidisciplinary analysis and decision making for city sustainability require new technological solutions to cope with such complexity. Ontologies have become viable and effective tools to practitioners for developing applications requiring data and process interoperability, big data management, and automated reasoning on knowledge. We investigate how and to what extent ontologies have been used to support smart city services and we provide a comprehensive reference on what problems have been addressed and what has been achieved so far with ontology-based applications. To this purpose, we conducted a systematic literature review finalized to presenting the ontologies, and the methods and technological systems where ontologies play a relevant role in shaping current smart cities. Based on the result of the review process, we also propose a classification of the sub-domains of the city addressed by the ontologies we found, and the research issues that have been considered so far by the scientific community. We highlight those for which semantic technologies have been mostly demonstrated to be effective to enhance the smart city concept and, finally, discuss in more details about some open problems.
... Ontology-Based Knowledge Networks gradually are being applied to a vast range of disciplines, such as in soil science, by describing soil properties, processing and their interaction (Du et al., 2016;Heeptaisong & Srivihok, 2010). ...
Full-text available
The present work deals with the development of an Ontology-Based Knowledge Net-work of soil/water physicochemical & biological properties (soil/water concepts), de-rived from ASTM Standard Methods (ASTMi,n) and relevant scientific/applicable ref-erences (published papers - PPi,n) to fill up/bridge the gap of the information science between cited Standards and infiltration discipline conceptual vocabulary providing accordingly a dedicated/internal Knowledge Base (KB). This attempt constitutes an innovative approach, since it is based on externalizing domain knowledge in the form of Ontology-Based Knowledge Networks, incorporating standardized methodology in soil engineering. The ontology soil/water concepts (semantics) of the developed net-work correspond to soil/water physicochemical & biological properties, classified in seven different generations, that are distinguished/located in infiltration/percolation process of contaminated water through soil porous media. The interconnections with arcs between corresponding concepts/properties among the consecutive generations are defined by the relationship of dependent and inde-pendent variables. All these interconnections are documented according to the below three ways: (i) dependent and independent variables interconnected by using the logi-cal operator “depends on” quoting existent explicit functions and equations, (ii) de-pendent and independent variables interconnected by using the logical operator “de-pends on” quoting produced implicit functions, according to Rayleigh’s method of indices, (iii) dependent and independent variables interconnected by using the logical operator “related to” based on a logical dependence among the examined nodes-concepts-variables. The aforementioned approach provides significant ad-vantages to semantic web developers and web users by means of prompt knowledge navigation, tracking, retrieval and usage
... Soil classification plays a vital role in safety regulations, where each type of soil has its own structure and properties, which affect soil behavior under different circumstances. Soil classification can be obtained based on different values of the properties [23], which directly affect the stability of the excavation and the choice of suitable safety procedures and resources. OSHA standard number 1926 Subpart P Appendix A [24] provides examples of the guidelines that can be applied to classify the soil based on various properties. ...
... However, outside of the discipline of soils science, there is confusion over soil's properties and processes amongst experts from related fields (e.g. Du et al., 2016). Furthermore, once concepts such as soil security enter the policy realm they are likely to be subject to reinterpretation by non-disciplinary experts leading to reframing and confusion, and perhaps, dilution of the power of the narrative to drive changes in the policy agenda. ...
Soils, like other natural resources, are in crisis; a policy vacuum exists, and we are observing dedicated efforts from the scientific community to address this vacuum. At the forefront of these efforts is the concept of ‘soil security’, which to date has gained support amongst soil advocates but still lacks traction in the political arena. This calls into question the alignment of current framings of soil sustainability, such as soil security, with policy makers' perceptions of the issue. To contribute to a stronger framing of soils for political agenda setting, we offer a social science perspective. We apply Stone's causal stories framework to review conceptually how ‘soil security’ and related concepts might operate in agenda setting. From there, we proceed to analyse the jurisdictional case of soil policy development in New South Wales (NSW), Australia. We find that despite the presence of a compelling policy ‘window’ and completion of a draft policy document, a change of government in NSW has caused the implementation of a new soil policy to fail. While the causes of this failure are largely unexplored, we suggest that the application of transdisciplinary approaches to soil policy processes could help avoid such situations in the future. Transdisciplinary approaches could assist policy processes through the development of a strong soil narrative that can re-politicise soils, instigate lasting soil policies and ultimately lead to societies' sustainable soil use and management. We advocate a more explicitly articulated and implemented transdisciplinary approach, with social science insights about framing and agenda setting as a starting point.
Full-text available
Single or multi-layer infiltration rate models describe water penetration into soil porous zone at a variety of comparable complexity levels. Commonly, those models are indicating a route leading from general to more specific case depending on the mathematical structure and the simulated ongoing phenomenon. For the purpose of wastewater flow quantification through porous media, an algorithmic procedure was developed which includes certain interconnected activity stages and decision nodes. Furthermore, a schematic cross shaped representation of the infiltration rate models’ ontology is presented within a route, leading from general to more specific models and evaluation criteria are introduced to determine highest ranking and thus the best model amid those placed on the horizontal axis of the cross shaped scheme, via a simple Multi-Criteria Analysis Decision Making
Full-text available
Soil classification for engineering purpose based on ASTMD2487-06 has many levels of hierarchy and there is distinct possibility of increase in hierarchy levels in future. Soil classification for engineering purpose inevitably requires evaluation of mathematical expressions. Ontology is formal explicit description of knowledge in form of concepts and their relationships [1]. It enables to share the common understanding of the domain knowledge. It also enables humans and machines to interpret the meaning and collaborate with other entities in a meaning manner depending upon the context. In this paper a step by step procedure is explained for building rules based engineering classification of soils using Protégé knowledge acquisition system and swrl. Query interface for fetching data relevant data in the ontology is explored.
Full-text available
The aim of this paper is to develop a soil knowledge-based system by using ontology (SOKS). We applied XPath algorithm and automatic term weighting of ontology to improve the performance of this system. The system provides functions to search from various sources such as HTML, databases and digital libraries on the Internet and support knowledge sharing and knowledge reuse which is the important process in knowledge management. The system can improve performance of document retrieval in term of precision value is 0.9 which outperform the traditional system.
Full-text available
As biological and biomedical research increasingly reference the environmental context of the biological entities under study, the need for formalisation and standardisation of environment descriptors is growing. The Environment Ontology (ENVO; is a community-led, open project which seeks to provide an ontology for specifying a wide range of environments relevant to multiple life science disciplines and, through an open participation model, to accommodate the terminological requirements of all those needing to annotate data using ontology classes. This paper summarises ENVO’s motivation, content, structure, adoption, and governance approach. The ontology is available from - an OBO format version is also available by switching the file suffix to “obo”.
Full-text available
Research in interoperability has been motivated by the growing heterogeneity of computing systems. Heterogeneity can occur in many levels and each level of heterogeneity requires an isolated or integrated approach for solution. In this paper, we propose the specification of a formal ontology for the information related to a specific domain of a database system, to work together with a global scheme, developed as software layer among the different databases under consideration. To test this approach we elaborated a case study, based upon hypothetical queries submitted to relational and heterogeneous databases, with data on soil domain, aiming at identifying the kinds of soil most appropriate to a certain culture. The case study demonstrated that the semantic conflicts were circumvented and the integration of the databases was easily reached.
Technical Report
OWL 2 extends the W3C OWL Web Ontology Language with a small but useful set of features that have been requested by users, for which effective reasoning algorithms are now available, and that OWL tool developers are willing to support. The new features include extra syntactic sugar, additional property and qualified cardinality constructors, extended datatype support, simple metamodeling, and extended annotations. The Manchester syntax is a user-friendly compact syntax for OWL 2; it is frame- based, as opposed to the axiom-based other syntaxes for OWL 2. The Manchester Syntax is used in the OWL 2 Primer, and this document provides the language used there. It is expected that tools will extend the Manchester Syntax for their own purposes, and tool builders may collaboratively extend the common language. It is already used in Protégé 4 and TopBraid composer.
In contrast to other approaches that provide methodological guidance for ontology engineering, the NeOn Methodology does not prescribe a rigid workflow, but instead it suggests a variety of pathways for developing ontologies. The nine scenarios proposed in the methodology cover commonly occurring situations, for example, when available ontologies need to be re-engineered, aligned, modularized, localized to support different languages and cultures, and integrated with ontology design patterns and non-ontological resources, such as folksonomies or thesauri. In addition, the NeOn Methodology framework provides (a) a glossary of processes and activities involved in the development of ontologies, (b) two ontology life cycle models, and (c) a set of methodological guidelines for different processes and activities, which are described (a) functionally, in terms of goals, inputs, outputs, and relevant constraints; (b) procedurally, by means of workflow specifications; and (c) empirically, through a set of illustrative examples.
With the development and popularization of Internet, The research focuses on how to get the requirement quickly and exactly from a large number of information. Using ontology provides a new intelligent searching method based on Web. In this paper, According to ontology theory of agriculture's characters and combining with the major of soil and agricultural chemistry, the retrieval system took the soil knowledge system as example, took native XML (eXtensible Markup Language) Database--Tamino as information navigation database. According the demands input by users, this system will display related information by tree and understand user's demands through clicks, primarily realize Web's intellective searching. This article still introduces the design and implement process of the intellective retrieval system, XML and JSP (Java Server Pages) technology in detail. The system application can be spread for other shared information resources retrieval, providing efficient and relevant services for users.