domain: sde sdv

text:
Thierry Gaudin

Dominique Lacroix

Marie-Christine Maurel

Chapitre 13 The Living Species is Not a Natural Kind but an Intellectual Construction

Introduction

In biology, the species is probably the most often and widely used notion. All scientific work on life refers to a species' name, as well as many other domains of civil society (nutrition, health, cosmetics, environmental management, gardening, etc.) However, the notion of species is also that on which opinions differ the most at the conceptual level. To give a measure of these divergences, we quote Mayden [MAY 97] who identified no less than 24 different concepts of species. Uncountable pages and numerous books have been written on the subject (recently, e.g., [STA 03, WHE 00]). Much work of philosophy of science puts an embarrassing emphasis on the contradictory and spirited discussions on the nature of a species [MAH 97, RUS 87, SOB 93]. The relatively confusing sum of all these works makes it difficult for scientists to fully embrace the question and adopt a reflective approach. This paradox -omnipresence of the notion and absence of a consensual concept -is a problem. In the largest majority, the uses of the notion of species are purely operational and do not mention any concept, which could limit the validity of the results or the data, or even the comparison of studies. Clearly, scientific communication concerning life can be problematic due to the issue about the notion of species. This apparent cacophony is not the result of a lack of interest or perspicacity on the part of biologists but rather derives from the accumulation of concepts forged at different times by different disciplines whose objectives and tools vary or evolve. Some people have attempted to combine these different concepts by adopting a sympathetic progressive attitude: the species is like a complex volume in the dark that can be lit up from different angles to ascertain its shape. Later, we will see that this attitude can create problems as it overestimates the reality of species and because the concepts refer to different situations whose compatibility varies greatly. Finally, the problem of the species is hidden behind a name, the one from the binomial Linnean nomenclature that gives it substance, authority and often prevents deeper awareness. A same name, especially if given cleverly in Latin with the authority of a descriptor taxonomist [DUB 08a], proclaims a sort of absolute equivalence that would erase the differences between concepts of species and would be far too easy to accept. This chapter reviews the main study methods used for evolution, the three main families of concepts of species that arise from these and finally the two great philosophical concepts on the nature of a species. To finish, we look at how to treat a species and the consequences of its use within the scientific community and its communication within society.

Two ways to study evolution: genealogy versus phylogeny

The main concepts of species are defined in the context of evolutionary biology. To understand these concepts, it is therefore necessary to first clarify the two main methods used to study the evolution, each addressing a different level and study method (Figures 13.1 and 13.2). The level of genealogical study identifies and describes the relations between ancestors and descendants. The observations inevitably refer to individuals (Figure 13.1). They are either direct (the observation of reproduction and the birth of descendants) or indirect (inferences produced by population genetics methods). The diversity of genealogical relationships among living species is often underestimated due to our anthropocentrism: we consider a priori that life is only represented by pluricellular organisms with bisexual reproduction. In reality, bacteria, archaea and protists represent a substantial part of the living and even among the pluricellular, the modalities of reproduction can vary with parthenogenesis, the number of sexes (often greater than two), gender changes through development, etc. The genealogical level of study is the oldest and the most easily understood with anthropocentrism. Man has applied it to himself for millennia. Breeders construct the genealogy of many animal species and geneticists have formalized them since the beginning of the 20th Century [FIS 30]. Studies at the phylogenetic level describe evolutionary relationships (called sistergroups) between individuals or groups of individuals, at an observable level where genealogical information is often completely missing and unknowable. Evolutionary relations between individuals, known as phylogenies, are inferred through the shared presence of original characteristics (apomorphies) and are described in a relative manner: an individual is a closer relative to another individual than to a third one (Figure 13.2). These relative evolutionary relationships are hierarchically nested: they are represented by what are known as acyclic connected graphs -the phylogenetic trees or cladograms. Building these trees is therefore based on many original characteristics carried by the individuals and on the minimization of their changes of states. For example, horses are closer relatives to humans than fishes, which is attested by their shared possession of mammary glands and specific skeletal characteristics (jaw, inner ear, etc.), among others. Rejecting this inference is only possible if supported by many other characteristics that prove to be contradictory to those of mammary glands and the specific skeletal characteristics (between humans and fishes, for example). The process of minimization (the principle of parsimony) of changes in characteristics is now often carried out in a frequentist or Bayesian probabilistic context, notably for molecular data. A phylogenetic tree is therefore simply a graph, a representation of the results from calculations carried out on a matrix [individuals × characteristics]. Many misleading conceptions and language shortcuts try to give more significance to this graph than it is due, considering it as a natural kind, or as an image representation of evolution; thus, some people refer to ancestors at tree nodes or to the speciation of dichotomies. Nothing could be less true: potentially, ancestral states of characteristics at nodes can be reconstructed and it may be possible to study speciation using a phylogenetic tree, but these are particular uses that rely on the tree topology rather than on parts of or on the substance of the tree. The metaphor of genealogy most often used to explain phylogeny is particularly fallacious: in phylogeny, there are neither ancestors nor descendants; an ancestor at a given level is unknowable in phylogeny because it does not carry a single original characteristic [ENG 77,NEL 70]. Study at the phylogenetic level is very recent in its modern methodological dimension; phylogenetic analysis was formalized by Hennig [HEN 66], Kluge and Farris [KLU 69] and Farris [FAR 70]. This level of study still requires many methodological clarifications with an aim of eradicating all genealogical, essentialist or gradistic connotation [GRA 14].

Three main families of concepts of species

Rather than enumerating them exhaustively, it is possible to consider that the many different concepts [MAY 97] can be reassembled into three main groups [DUB 11, MAL 08a].

The first group -diagnostic concepts -is heterogeneous. It includes the first essentialist concepts from Aristotle to those that followed (Linné, Buffon, etc.) as well as all the operational groups made with existing individuals in the absence of more detailed concepts. It involves considering that two groups of individuals constitute just as many species if it is possible to find diagnostic characteristics for each of them. This group of concepts is limited by its simplicity: the delimitation of groups is arbitrary; it incorporates no or little theory of evolution and therefore does not allow for the testing of hypotheses in evolution biology. The second group -biological concepts -is often attributed to the biologist Ernst Mayr [MAY 42]. It is based on the rupture from the principle of common reproduction and the adaptive or neutral genetic divergence and the reproductive isolation that should result from it. Two populations having undergone this type of event are deemed to de facto become two different species. Most geneticists or population biologists subscribe to this group of concepts that make the most sense in terms of the scale of their studies. However, this group of concepts remains very limited by two problems, one theoretical and the other operational. Theoretically, the ability to reproduce together is an ancestral characteristic [BRE 79, ROS 78] and two very distant species are frequently able to reproduce, often with success. Think of ligers, walphins, camas and other zonkeys, not to mention one-quarter of the European flowering plant species or one-tenth of bird species hybrids [MAL 08a]. The ultimate paradox disfavoring this concept is that hybridization between what are considered as different species is often an engine of speciation, notably in plants. The criteria for reproduction that would define a species is therefore too idealistic to be pertinent. At the operational level, we must remember that this criterion only concerns a few organisms among all of the living: those that are plurisexual, especially those whose reproduction or genetic structure is observable, therefore only existing organisms excluding fossils (that represent 99.9% of the species that have existed!). For example, let us remember that the first discovery of fossil traces of copulation in vertebrates is very recent [JOY 12]. The third group -phylogenetic concepts -is the most recent [BAU 92, CRA 83]. It consists of anchoring diagnostic concepts in a phylogenetic context: the diagnostic characteristics of each species become the apomorphic characteristics supporting monophyletic phylogenetic groups (including all descendants from a common ancestor). These concepts are coherent with the historical and evolutionary context; they are applicable to all individuals without restriction (existing vs. fossil, for example). They also have the advantage of being predictors of genetic or phenotypic diversity, with the possible use of the phylogenetic diversity (PD) metric [FAI 92,PEL 16]. They can also be viewed as a modern formalization of Simpson's concept of evolving species [SIM 61] that has remained defective since its formulation, due to a lack of phylogenetic methodologies. A single concern remains: where do we put the limit between species?

Reconciling the different concepts: pragmatism or essentialism?

What is the theoretical compatibility between these groups of concepts? It is easy to see that it is not complete. Phylogenetic species have often proven to show traces of genetic introgression, implying that they do not correspond to closed communities of reproduction. For example, Nattier et al. [NAT 12] led a study on the origins of restricted distribution (micro-endemism) in groups of insects in New Caledonia. For more than 20 million years, this group diversified through allopatric speciation; the distribution area of ancient species -much reduced in the beginning -increased in size, creating secondary sympatries that gave rise to interspecies reproduction whose traces are found in certain molecular markers. Conversely, it is commonly found that biological species are not monophyletic [FUN 03].

To compensate for this lack of complete compatibility and the clear disadvantage of recognizing pluralism in the notion of species, several scientists have conceived another kind of reconciliation between concepts: a composite concept called the lineage-species concept [DE 98, SAM 06]. These authors think that in time, a specific lineage successively encompasses first the phylogenetic species, then the biological species, once reproductive isolation is secondarily acquired. The incompatibilities raised at a time t (see above) are only noted because they are observed at different moments in a situation that involves several steps. This elegant proposition actually involves a very particular model of evolution in which reproductive isolation is acquired last, that is, far from universal. In some cases, reproductive isolation can be acquired first or secondarily lost in cases of extrinsic (geographic) isolation, also intervening secondarily. In addition, the phylogenetic species does not necessarily evolve fully into a biological species. The proposal of a composite lineage-species concept that can be considered as holistic or consensual therefore also has the disadvantage of leading to the thought that a species "truly exists" or will "truly" exist in an ulterior stage of evolution (once reproductive isolation is acquired): it falls back to a somewhat essentialist way of thinking. If the lineage-species concept is adopted, in fact only the observed stage of a given situation is adopted. In its entirety, the concept remains virtual and only holds sense through the syncretism of the different situations studied. It also hides the difficulty of considering all observations or inferences carried out at various study levels, populational and phylogenetic, and that ultimately do not refer to the same realities.

The species and the taxon name

No matter the species concept referred to (or not!), the species-taxon name is inevitably used. This is a Latin binome followed by the name of its author (e.g. Taraxacum officinale Weber, Blattella germanica L.) and whose qualities and rules of use are defined by the respective nomenclature codes of zoologists, botanists and microbiologists [DUB 08b]. The species-taxon name finds itself in the inclusive hierarchy of taxonomic categories: species, genus, family, sub-family, order, class, etc., that form a classification. The nomenclature rules associate this species-taxon name to a specific individual, the name-bearing type or holotype, more correctly called onomatophore. There is therefore a logical correspondence between species-taxon <-> name <-> holotype. Far from a typological view of organisms, this principle aims to prevent all later errors linked to the erroneous grouping of individuals: a single organism-individual bears the name. During the subsequent identification process, this taxon name is, by extension, attributed to one or more individuals based on diagnostic characteristics put forward during description. The Latin binomial name is therefore just a convenient label but through the years it has given substance to the species in the mind's spirit and sometimes substituted the definition of a species concept. Notably, the identification process that can be applied to all concerned organism-individuals leads to the idea that the species notion is linked to a finite group of individuals, that its boundaries can therefore be perfectly known and that, therefore, a concept is involved. Certain bionominalist conceptions feed on such confusion, thinking that creating names and a classification necessitates only that the entities designated by taxa have well-defined biological natures (see, e.g., [MAH 97]). It must be understood that taxonomic work on the "description of a species" is simply a process that consists of selecting a name-bearing type and defining a list of diagnostic characteristics that either subordinate it or distinguish it from preexisting taxa. As such, taxonomy is non-ambiguous, permitting the classification and subsequent identification of taxa. In reality, classification and taxonomy are simple language conventions that do not necessitate specific conceptual choices in terms of the species. In the extreme, it is even possible to imagine a completely utilitarian classification in which that taxa are innumerable drawers in a cabinet library and do not have any particular significance. Let us therefore imagine a library organized in chronological order of work acquisition rather than by subject. Of course, a "natural" classification with an evolutionary and biological logic would be much more informative and useful, just as access to a library organized by subjects or authors would be much easier and more informative. Nevertheless, as much as it is preferable to follow an evolutionary classification, it also draws general utilitarian strength by not being directly and automatically dependent on versatile evolutionary theories (e.g. contradictory phylogenetic trees) that would continually modify it.

The nature of species: a salutatory philosophical exercise

Philosophers and biologists who pride themselves on philosophy have long discussed the nature of living species. This debate on nature is not only a point of philosophical theory, as many biologists are often happy to think. It also sheds light on the discussion on species concepts and vice versa. Three main concepts on the nature of species have emerged: the species as a natural kind, the species as an individual and the species as a class [RUS 87]. The species as a natural kind has renewed itself since Aristotle and Locke and their essentialist concepts. Today, it is defended as such in a materialist ontology (e.g. [MAH 97]) through the conjunction of several properties or laws that are linked in a constant and real manner. However, species do not show such characteristics, they are polymorphous, variable and, most notably, they evolve and do not constantly have these characteristics. The natural kind concept, whose most frequently quoted example refers to the elements of the periodic table, is not in accordance with biological reality, even if perceived through the prism of one or another species concept. To support this theory, Boyd [BOY 91] and then Griffith [GRI 99] rearranged the concept of the natural kind into "homeostatic property cluster kinds", an "elastic class" whose variability is compensated for by the continuity of homeostatic properties that guarantee its integrity. Such rearrangement kills the concept of the natural kind and renders it trivial. The species as an individual was recently claimed [GHI 74, HUL 78] as a solution to the inadequacy of the natural kind. An individual is restricted in space and time: it has a beginning and an end. An interesting consequence is that an organism-individual can then become part of the species-individual instead of being a member of a kind. Organisms are therefore more causally linked (matched?) than similar. Here again, there is a difficulty in adopting this nature of a species. In fact, organisms of a species lack cohesion: they are not part of a unified whole but are simply linked by potential reproduction relationships. In addition, the genealogical link is not, strictly speaking, causal. Identifying the beginning and end of a species falls back on limiting it precisely in space and time with all its organism-individuals, which is biologically incompatible with the different concepts. It is understood that this is more an analogy than a characterization.

We see that these different natures are forced to adjust to species concepts with which they do not fit. Their point in common is their attempt to reify species and give them a tangible biological reality, beyond the biological concepts describing the loose and close relations between individuals or the historical phylogenetic diagnoses that simply give a final score of the relations between groups of organism-individuals. Why not ultimately characterize species as a less tangible group, an extensional class (see class definition according to [MAH 97]) thus characterized by the statement of one or more properties common to a set of individuals. These properties would simply be diagnostic characteristics to which the status of law or invariant would not be attached: for example the ability to reproduce together, the list of apomorphic characteristics. This characterization that could be qualified as nominalist (in its strict sense) would, in addition, be compatible with the pluralist view of species, recognizing the validity and particularity of each family of concepts, whether biological or phylogenetic. It is therefore possible to formulate different, partially compatible classes corresponding to each family of concepts. Biologically, this solution leads to the postulate that the only reality is the organismindividual (the one that is observable from one's window, not the one whose structure is inferred) and its relationships with other organism-individuals, as many times and in as diverse ways as they can be characterized in. Historically, it is interesting to note that this solution is closer to Darwin's concepts, remarkably significant and economical in hypothesis [MAL 08a, MAL 08b, STA 13].

Consider species as a simple extensional class that has the following consequences: -the name of the species carried by the name-bearing type (onomatophore) is attributed through the process of identification to a class of organism-individuals; ideally, such an attribution would require detailing the species concept used and would remain dependent on this concept: with the identification operation, the field of classification -the species-taxon free from biological theory -is combined with that of evolutionary biology -a defined concept of species; -all judicial notions linked to a species name designate the name-bearing type and its identified conspecifics; -it is necessary to revisit all the analyses in evolutionary biology and to realize that they are only truly relevant in terms of organism-individuals (contra [WHE 00]); phylogenetic studies study the evolutionary relationships between individuals, even if they use characteristics that are quite invariable among the individuals attributed to different species-classes. The concept of speciation must be well-understood: it does not correspond to species individualization but to a change in relationships between individuals, etc.

-the character postulated and conditional on the identification operation also requires that information linked to the living species be used and conserved at the organismindividual level; this is the notion of "saving information" that underpins the conservation of the organism-individual similar to so many specimens in collections [GRA 17]; -empirically, due to the fact that a large majority of species are defined operationally and in a purely taxonomic and diagnostic manner, they can be considered to issue from the phylogenetic species concept, provided that the classification is natural (that is to say that the diagnoses are established from characteristics that are known, from phylogenetic analyses, to be apomorphic at the level concerned).

There remains one thing to say with regard to the delimitation of species. We have seen that in terms of the different concepts, none of them guarantees an unequivocal delimitation. The shared criterion of reproduction is far from absolute and the criterion of monophyly does not prescribe where to cut off two species. In both cases, the method is the same: discontinuities that appear significant are used to make species statements (e.g. [MAL 08a]): defining classes is an easier task when based on relatively constant differences and particularities. Since Darwin [DAR 59], it has been understood in addition that these apparent discontinuities between groups of individuals called species are the combined result of divergences and extinctions [MAL 08a, MAL 08b]. For example, delimitation methods have been proposed based on hiatus in the overall similarity between molecular markers [PUI 12]. It goes without saying that this type of delimitation carries a strong arbitrariness but remains legitimate in the context of the definition of a simple class (neither a natural kind, nor an individual). The authors generally argued for the biological legitimacy of the characteristics used for species delimitation: such and such anatomical characteristic is supposedly less adapted (less suspected of convergence?), more adapted (more stable?), a genetic characteristic (e.g. a DNA barcode) rather than phenotypic (stronger heritability, more prone to statistical analysis?), etc. There are, of course, many assumptions in such arguments and it must simply be remembered that it is more coherent to justify characteristics used with regard to the species concept followed rather than in terms of pure operationality. This would lead to the selection of apomorphic characteristics in the case of the phylogenetic concept or linked to reproduction in the case of the biological species. In this manner, the definition of a species is the result of a specific study that consequently proposes a simple extensional class. The species is therefore not discovered as a tangible entity, it is not subject to evolution [MAL 08a, MAL 08b]: to be clear, we cannot speak of the evolution of a species and that which evolves, these are the relationships between organism-individuals. The title of Darwin's famous book -The Origin of Species -must be understood as a metaphor and not as a demonstration per se.

Figure 13

 13 Figure 13.1. Genealogical level of study with network of reproduction relations in a group of ancestors and descendants; each black dot is an individual (adapted from [SAM 06])


Lolita Lecompte

Pierre Peterlongo

Dominique Lavenier

Claire Lemaitre

SVJedi : Structural variation genotyping using long reads

Introduction

Structural variations (SVs) are DNA segments that have been moved regarding another reference genome. The number of known SVs is increasing, especially with the development of third generation sequencing technologies, which produce long reads data.

Discovery vs. Genotyping

SV discovery

Given a reference genome, discover SVs between a sequencing sample and a reference genome. SVIM (2019), Sniffles (2018), pbsv (2018), NanoSV (2017)

SV genotyping

Given a set of already known variants for a species, assess the presence or absence of each variant in a given sequencing sample.

There is no known method for SV genotyping with long reads.

Method -SVJedi

Results on a simulated dataset

Simulated dataset

■ 1,000 deletions selected on the human chromosome 1 from dbVar (50 bp -10k bp)

■ Equally distributed : 0/0, 0/1, 1/1

Conclusion

▶ New method to genotype SVs using long reads data.

▶ Importance of dedicated genotyping methods.

▶ Currently only applied to deletions.

▶ Implementation: SVJedi

Results on a real dataset Dataset

						Does SV discovery tools correctly estimate genotypes?
					SVJedi		Sniffles [2]
					0/0 0/1 1/1 ./.		0/0 0/1	1/1
				Truth	0/0 330 3 0/1 19 305 6 0 1/1 3 10 311 9 0 4	Truth	0/1 1/1	3 0 254 89	0 60
							Recall : 60.87 %
	■ Long reads simulated with SimLoRD [1] 16 % error rate, 20X sequence depth	Precision : 95.85 %		Precision : 36.70 %
						80.9 % of 1/1 deletions are wrongly genotyped as 0/1
	■ Sample: NA12878 ■ Data: Nanopore WGS	svclassify	SVJedi 0/0 0/1 1/1 ./. 0/1 194 1,289 202 28 1/1 8 75 877 4
	consortium (37X)
	■ svclassify [3]:	2,677		Identity : 81.89 %
	high confidence dele-	Predicted genotypes = 98.81 %
	tions			Runtime : 2h35 (40 CPU)


G Touya

S Balley

C Duchêne

K Jaara

N Regnauld

N Gould

Towards an Ontology of Spatial Relations and Relational Constraints

The Need for Constraints and Spatial Relations Ontology

Research in automated generalisation has suffered from the lack of availability of tools developed for past projects, preventing researchers from reusing what their colleagues have already done and concentrating on novel aspects [START_REF] Edwardes | Map Generalisation Technology: Addressing the need for a common research platform[END_REF]. A solution was proposed and developed in Zurich, using Web services technology to share processes between distant platforms (Neun and Burghart 2005), [START_REF] Foerster | Towards an interoperable web generalisation services frameworkcurrent work in progress[END_REF]. This paves the way for much more complex and powerful systems to be designed. This has been demonstrated at the Cogit laboratory, when [START_REF] Touya | Collaborative generalisation: Formalisation of generalisation knowledge to orchestrate different cartographic generalisation processes[END_REF] showed how a full map could be generalised using different processes used in different geographic contexts (urban, rural, mountainous etc). While this was done with all the processes available on the same system, the Web services allow us to do this with processes available on different platforms. The interoperability of systems has been identified as an important component of an on-demand generalisation system (Regnauld 2007, Gould and[START_REF] Gould | Generation and validation of workflows for On-Demand mapping[END_REF], but the interoperability of the knowledge used by generalisation systems is also an issue (Balley and Regnauld 2011). In particular, we want to propose a common way of describing and representing spatial relationships between geographic features. This would make it much easier to link the map requirements based on such relationships, with generalisation knowledge relying on them, and tools to compute them.

Moreover, [START_REF] Stoter | State-of-theart of automated generalisation in commercial software[END_REF] showed that defining generalisation constraints was a very challenging task for users with no expertise in the generalisation process, even for cartographers. Helping non-expert users to express their specifications as constraints is, thus, an urgent need to develop the use of automatic processes. An ontology containing classical generalisation constraints, among which relational constraints i.e. constraints on spatial relations [START_REF] Duchêne | Automated Map Generalisation using Communicating Agents[END_REF], would be the starting point for developing tools to help non-expert users.

In this paper, we try to make a step towards an ontology of spatial relations and relational constraints between geographic features in the context of generalisation.

The next part deals with the modelling and the classification of spatial relations to enable the creation of a spatial relations ontology. The third part briefly describes a proposition to model generalisation constraints as an ontology. Finally, the fourth part proposes methods to use the ontology in automatic processes.

Modelling Spatial Relations

Spatial Relation Types: Quantitative vs Binary Relations

We distinguish two ways of describing a relation between a pair of objects. The first way consists in characterising the relation by means of a quantitative measure, e.g. the proximity between two buildings is characterised by a distance, a relation of relative orientation is characterised by an angle. This way is valid for relations classified as "metric relations" by [START_REF] Egenhofer | Point-set topological spatial relations[END_REF]. The second way consists of associating the relation to a predicate like is parallel to, is included in, goes along, etc. In this case, the status of the pair of objects with respect to the predicate is binary: it meets the predicate or not. In some cases, a family of predicates can be defined so that a given pair of objects only meets one predicate of the family, e.g. for topological relations, the predicates of the 4-intersection model (4IM) (included, includes, covered by, covers, overlaps, equals, meets, disjoint) or of the 9-intersection model (9IM) [START_REF] Egenhofer | Point-set topological spatial relations[END_REF]. Sometimes, this family of predicates covers all possible situations (like in 9IM), i.e. the family of predicates is an enumeration. Otherwise, only a few particular configurations are identified and described by a predicate while the other ones are just not covered (e.g. is parallel to and is orthogonal to are two predicates that describe particular relative orientations).

Whether we consider one relation (e.g. relative orientation) and associated predicates (parallel, orthogonal), or several relations (parallelism, orthogonality) is an arbitrary choice. We choose the second, therefore we distinguish between quantitative relations (relations described by a quantitative measure) and binary relations (relations described by a predicate, which are present or not between a pair of objects); some of the binary relations form families or enumerations. Now, a particular situation with respect to binary relations is the situation where the relation is not completely present, but almost (e.g. the building is not strictly parallel to the road but almost, it is not clear if it is parallel to the road or not, cf. [START_REF] Duchêne | The CARTACOM model: transforming cartographic features into communicating agents for cartographic generalisation[END_REF]). This situation is remarkable in the context of generalisation, which seeks to avoid the fuzziness and replace it with a sharp relation through caricature. Therefore, for each sharp binary relation we propose to add an associated fuzzy relation in our ontology corresponding to the case where the sharp relation is almost present, e.g. near parallelism. A particular case is when a family of sharp relations exists, like the topological relations corresponding to the predicates of the 4IM or the standard 9IM. In such a case, the fuzzy relations are not linked to one sharp relation, but at least two: a fuzzy relation can be added on each edge of the conceptual neighbourhood graph linking the sharp relations, as illustrated in Figure 1. The same applies to 9IM. Fuzzy topological relationships have been studied by [START_REF] Winter | Uncertain topological relations between imprecise regions[END_REF] and [START_REF] Bejaoui | Qualified topological relations between spatial objects with possible vague shape[END_REF] in the context of object with fuzzy limits. Here, in the context of cartographic generalisation, the fuzziness is a "perceived fuziness" due to the perception limits.

To summarize, we distinguish between quantitative and binary relations, and among binary relations, we further distinguish between sharp and fuzzy relations.

Spatial Relations Model

To enable interoperability between the components of a generalisation system specifying, extracting and exploiting spatial relations and associated constraints, we propose to formalise these relations in the form of an ontology (Figure 2). This section describes how we propose to model the highest level of this ontology. The proposed model is made of several facets that should be considered when extending a spatial relation from the ontology. Each facet is not equally relevant for binary, quantitative and fuzzy relations distinguished in section 3.1. A spatial relation links some spatial features which can be described through different facets: the concepts they reflect in the real world, the types of geometric primitive representing them digitally, and more precisely their feature types in a dataset.

Relations are seen as predicates that hold or not between spatial features. Quantitative relations always result in a value (or measure) and therefore it can be considered they always hold (even if it might be irrelevant to consider them), while binary relations do not.

Any relation (quantitative or binary, sharp or fuzzy) can be relevant or not to consider between a given pair of objects (e.g. if two buildings are separated by a river, it can be relevant or not to consider their proximity relation, it can be relevant or not to consider their parallelism provided it holds). The relevance depends in particular on the generalisation specifications.

Assessing the achievement of a binary relation means assessing if it holds. Assessing the relevance of any relation means assessing if it is relevant to consider it Assessing a relation may require a condition to be specified. This condition ("expression" on Figure 2) may refer to a threshold (e.g. two features are near if their relative distance is less than 10m), to other relations (e.g. two features are aligned if they are near and parallel) or to other elements like an intersection matrix template.

Relation properties enable to better describe relations after they have been assessed. They may be quantitative measures (e.g. a relative distance characterising an occurrence of the relation near) or other information. Quantifying a relation means calculating the values of its properties.

Operations may be provided to guide assessment or quantification. For instance, the type of distance measure to use (minimum, maximum or average distance) depends on the geographic nature and on the geometric primitive associated with the features. Operation types can also refer to complex spatial analysis operations. As described in section 4, operation types are currently used to help identifying the right tool at processing time. However, ongoing research is looking at alternative ways to match the operations and tools [START_REF] Gould | Semantic description of generalisation web services for on-demand mapping[END_REF].

Spatial Relations Taxonomy

The proposed spatial relations ontology model includes a taxonomy derived from previous spatial relations classifications [START_REF] Egenhofer | Point-set topological spatial relations[END_REF][START_REF] Steiniger | Relations among map objects in cartographic generalization[END_REF][START_REF] Bucher | Towards a typology of spatial relations and properties for urban applications[END_REF]. It is composed of eight relation types while each relation may also be fuzzy, like the almost parallelism relation that is a fuzzy orientation relation (Figure 3). Topological relations contain the classical 9-intersection primitives (Egenhofer and Franzosa 1991) like meet (Figure 4a) as well as fuzzy topological relations [START_REF] Winter | Uncertain topological relations between imprecise regions[END_REF] like fuzzy covered by (Figure 4b, the distance between the building and the forest limit makes it a fuzzy relation between touches and covered by). Orientation relations can be binary like relative orthogonality (Figure 4c) or fuzzy like almost parallelism (Figure 4d). The position relations gather the relative position relations (Figure 4e and f) and proximity relations represent features close to each other (Figure 4g). Figure 5a and Figure 5b show classical examples of shape and size relations between buildings. The semantic/logical relation type is illustrated by the is access to relation (Figure 5c) and the is flat relation between a lake and relief (Figure 5d). Finally, movement relations gather relations that can be expressed by a movement verb [START_REF] Mathet | New paradigms in space and motion: A model and an experiment[END_REF] like "the river circles the building" (Figure 5e) or "the path passes all across the forest" (Figure 5f). Moreover, some features may be related to some part of a feature and not to the complete feature itself. For instance, the limit of the forest locally circles the building in Figure 6f, or the building is on a summit in Figure 6a which is a characteristic part of the relief. Evenly, features may be related to implicit structures of a feature, like the skeleton of a polygon, that follows a river (Figure 6e), or the thalweg in which the river flows (Figure 6c). However, we believe that such spatial relations require an extension of our model presented in the previous section, and thus need further investigation. Figure 6. spatial relations between a feature and a part (or an implicit structure) of a feature.

Use Cases of relations modelling

Modelling the follow Relation for on-demand mapping involving user's data

Let us consider an on-demand mapping use case where cycle routes captured by a user must be integrated into reference data featuring roads and cycle tracks. Before conflation (Figure 7), it is expected that cycle routes follow roads and cycle tracks, i.e. they are almost parallel and close to the referential sections. This spatial relation enables to express on-demand map specifications:

Only main roads must appear on the map, plus minor roads followed by a cycle route.

Cycle routes following a road must share its geometry in the final map.

Cycle tracks must not appear on the final map, but cycle routes following a cycle track must be symbolised as "cycle friendly". Modelling the follow relation using our ontology enables to have it assessed and exploited by the third part tools performing the filtering of roads, the conflation and the symbolisation of cycle routes.

The relation between cycle routes and roads can be described as shown in Figure 8. Both members are supposed to be network sections. A tolerated maximum distance between sections is provided as a condition of achievement. This tolerance has to be decided by the user or automatically evaluated using the advertised accuracy of data sources. The relation can typically be assessed via a network matching operation. Cycle tracks are not a network and do not necessarily share geometry with roads (notably because they may have been produced by another provider). The cycle route/cycle track relation is slightly different from the cycle route/road one. It can be modelled as shown in Figure 9. Member 2 is not supposed to be a network section. As cycle route sections can be partly cycle-friendly (Figure 7), they may have to be split prior to symbolisation. Consequently, convergence and divergence nodes are stored as a property relation. A local matching operation could assess the relation and calculate its properties.

Modelling relations between user data and reference data

Let us consider the case where user comes with his own thematic data, referenced for the localisation using some topographic data, and wants to make a map by overlapping them on another topographic data. A typical example is fire brigades referencing fire hydrants on a road network and they want to make a map using an updated version of their road network. Relations between user data and topographic data have a special importance compared to the relations among topographic data, therefore they should be preserved during data processing (e.g. migration of thematic data or the generalisation of topographic data). For instance, fire brigades want that the fire hydrant stays at the same side of the road, and also want to get the same relative position of fire hydrants among the surrounding buildings [START_REF] Balley | Towards a Prototype for Deriving Custom Maps from Multisource Data[END_REF] propose a model for referencing the thematic data by storing relations, and the model can also be used to constraint data processing based on relations. According to this model, relations are either between thematic and topographic objects, or between thematic and characteristic objects. Characteristic objects are defined as additional objects that are extracted from the initial topographic data. As examples of characteristic objects, we have river-road intersections, top of bend of a road segment. So the spatial feature of the ontology in this case can be thematic (e.g. hydrants), topographic (e.g. roads and buildings) or characteristic (e.g. top of bend). In the model, every relation has two members (or spatial features): the first is a thematic object and the second is topographic or characteristic object.

For example, a car accident located at the top of a bends has two relations: "near" and "on road", the modelling of the two relations is described in Figure 10.

Proposed hierarchical model for a Constraints Ontology

In order to fulfil the previous needs, our proposal is to build a generalisation constraints ontology to be integrated into what Balley and Regnauld (2011b) called the semantic referential [START_REF] Kuhn | Semantic Reference Systems[END_REF], and to be completed incrementally. There is a large literature on constraint modelling for generalisation [START_REF] Beard | Constraints on rule formation[END_REF][START_REF] Louwsma | Specifying and implementing constraints in GI with examples from a Geo-Virtual reality system[END_REF][START_REF] Burghardt | Investigations on cartographic constraint formalisation[END_REF]. The model of the ontology relies on the constraints modelling of [START_REF] Touya | Collaborative generalisation: Formalisation of generalisation knowledge to orchestrate different cartographic generalisation processes[END_REF] that synthesise this literature: a constraint is defined on the character (e.g. area) of an entity (e.g. building) and has an expression type (e.g. threshold type, character < threshold) (Figure 11). As suggested by [START_REF] Regnauld | Evolving from automating existing map production systems to producing maps on demand automatically[END_REF], such constraint ontology is based on geographic entity ontology that contains characters or properties defined on entities (e.g. a building has the character "area"). The paper deals with the spatial relations and relational constraints that could be added in this generalization constraints ontology. Four types of relational constraints have been identified (Figure 12): the classical relational preservation constraints that monitor the preservation of salient relations during generalisation (e.g. preserve river circles building relations); the relation caricature constraints that monitor only fuzzy relations to make them sharp (e.g. caricature almost parallelism relations into parallelism relations); the relation transformation constraints that change a relation into another during generalisation (e.g. transform road/building parallelism relations into adjacency relations); finally, non-creation constraints that prevent non-existing relations from being created by generalisation transformations (e.g. no relative prominence relation between buildings can be created by building enlargement algorithms).

How to link relations and constraints with measure algorithms to assess them

One of the goals of the spatial relations ontology is to enable the automatic selection of algorithms in a process of generalisation or on-demand mapping.

A first proposal to link relations and constraints to the measure algorithms enabling to assess and quantify them is to annotate the algorithms with the spatial relations ontology concepts [START_REF] Lemmens | Lost and found, the importance of modelling map content semantically[END_REF]. [START_REF] Touya | Collaborative generalisation: Formalisation of generalisation knowledge to orchestrate different cartographic generalisation processes[END_REF] proposed to use a registration mapping, i.e. a file that matches algorithms to ontology concepts, here algorithms being Java classes.

The "operation" proposed in the relation model allows more flexibility: depending on the map specifications and available data sources, a spatial relation may be best assessed via different, more specific operations. In the proposal of Balley and Regnauld (2011b), operations are defined in an ontology and determining the most suitable one is the duty of rules and task templates. If available tools have been annotated with operations, they can be selected by semantic matching. This approach is currently under implementation [START_REF] Balley | Towards a Prototype for Deriving Custom Maps from Multisource Data[END_REF]. [START_REF] Gould | Semantic description of generalisation web services for on-demand mapping[END_REF] proposes an even more flexible matching. His assumptions are, it is unlikely to have available tools annotated through our operation ontology, and such annotations wouldn"t be sufficient to make a choice between several implementations of the same operation. He therefore proposes to develop an additional "algorithm ontology" refining the link between operations and tools.

Conclusion

To conclude, an ontology of spatial relations and relational constraints is a valuable tool to improve map generalisation automation and on-demand mapping. This paper proposes a first model towards such an ontology, as well as a first taxonomy of spatial relations to fill the ontology model. A constraint ontology, related to the relations one is also proposed. Now, it needs to be completed and improved, and to be tested in automatic processes by the research community, in order to converge to a standard and shared generalisation knowledge.

Figure 1 .

 1 Figure1. A fuzzy topological predicate is added on every edge of the neighbourhood graph of the 4intersections model by[START_REF] Egenhofer | Reasoning about Gradual Changes of Topological Relations[END_REF]

Figure 2 .

 2 Figure 2. OWL model for spatial relations.

Figure 3 .

 3 Figure 3. Proposed taxonomy for spatial relations between two geographic entities. Fuzzy relations are also one of the other types (e.g. orientation relation).

Figure 4 .

 4 Figure 4. Examples of spatial relations.

Figure 5 .

 5 Figure 5. Other examples of spatial relations.

Figure 7 .

 7 Figure 7. Situation before conflation: (driving direction from left to right) The cycle route follows roads and cycle tracks. The first and last portions of the cycle route are cycle friendly.

Figure 8 .

 8 Figure 8. Modelling of the follow relation adapted to cycle routes and roads.

Figure 9 .

 9 Figure 9. Modelling of the follow relation adapted to cycle routes and cycle tracks. Divergence and convergence points are properties of the relations.

Figure 10 .

 10 Figure 10. Modelling "near" and "on road" relations of an accident

Figure 11 .

 11 Figure 11. Principles of the hierarchical constraints model in OWL2: the axioms restrict the variables for a user to define his constraints.

Figure 12 .

 12 Figure 12. Four types of relational constraint in the proposed ontology illustrated by existing generalisation constraints.

th ICA Generalisation Workshop, Istanbul, Turkey, 2012


What Is the Level of Detail of OpenStreetMap?

Level of Detail Problems in OpenStreetMap Maps

The cartographic representation of geographical phenomena includes a level of detail (LOD) that can be considered as the scale to display the representation or the scale at which the phenomena can be meaningfully analyzed [START_REF] Mackaness | Understanding geographic space[END_REF]. Volunteered geographic information (VGI) is the result of the contributions of ordinary citizens [START_REF] Goodchild | Citizens as voluntary sensors: spatial data infrastructure in the world of web 2.0[END_REF], therefore, each contribution follows its own level of detail standards. For instance, very precise fountain features coexist with built-up area features in the same dataset. In OpenStreetMap (OSM), the most successful VGI project, level of detail heterogeneity both results from contributor will and from capture tools. Indeed, a contribution may be added using precise GPS tracks, vectorization on scanned maps or satellite imagery, or from open existing vector data (e.g. land use data in France is extracted from CorineLandCover® European data). The sources variety leads to heterogeneous data quality [START_REF] Girres | Quality assessment of the french OpenStreetMap dataset[END_REF][START_REF] Haklay | How good is volunteered geographical information? a comparative study of OpenStreetMap and ordnance survey datasets[END_REF]. This level of detail heterogeneity has a major drawback: whatever the scale, it is very difficult to make legible maps with OSM data. When scale is small, very detailed features like roads or buildings need cartographic generalization to be legible (Figure 1A & B). Indeed, [START_REF] Gaffuri | Improving web mapping with generalization[END_REF] claims generalization is a problem for most web maps. On the other hand, when scale is large, small scale features may be inconsistent with very detailed features (Figure 1C &D).

Research Topics to Overcome OSM Limitations

VGI is very rich, sometimes richer than traditional datasets and it would be a pity not to overcome the above mentioned limitation to produce legible maps. Several research topics to tackle to overcome the limitations are described below.

The first research topic could be to define VGI-specific metadata, including LOD information, or to develop formal specifications for VGI [START_REF] Brando | Edition collaborative d'un contenu géographique : une approche fondée sur des spécifications formelles et des relations spatiales explicites[END_REF]. However, interviewed OSM users refuse to add heavy constraints to the contribution. Indeed, even simple attribute tags are theoretically foreseen but actually sparsely used in OSM [START_REF] Girres | Quality assessment of the french OpenStreetMap dataset[END_REF].

Multiple representation databases (MRDB) are a research field that tries to design systems and models to store and represent multiple representations for geographic phenomena through scale and time [START_REF] Balley | Modelling geographic data with multiple representations[END_REF]. Converting VGI into MRDB is an interesting challenge, and it could be achieved using the linked data framework [START_REF] Hahmann | Linked data -a multiple representation database at web scale?[END_REF].

Besides MRDB, mapping OpenStreetMap data at different scales requires to harmonize the level of detail of mapped features. It means that spatial analysis techniques should be developed to automatically determine the level of detail from shape, accuracy or spatial relations of features. For instance, a forest area that has geometry with few vertices and overlaps several buildings (Figure 1C) should be considered as poorly detailed. Then, map generalization techniques could be used to harmonize features to the same level of detail.

Finally, a solution to overcome the level of detail heterogeneity could be to provide reconciliation tools when data is captured to avoid the inconsistencies created by LOD differences. [START_REF] Brando | Edition collaborative d'un contenu géographique : une approche fondée sur des spécifications formelles et des relations spatiales explicites[END_REF] proposes such techniques, for inconsistencies more general than LOD differences, to allow VGI following classical integrity constraint by comparison with reference data. Figure 2 shows reconciliation example that helps overcome level of details problems.

Figure 1

 1 Figure 1 Four examples of maps (©OpenStreetMap) with level of detail problems. (a) The mountain road (orange) is too detailed compared with land use and coalesces at this scale. (b) Buildings are too small to be displayed at this scale. (c) The forest is much less detailed than buildings causing overlaps. (d) Industrial (purple) and commercial (grey) parcels overlap buildings as their level of detail is not compatible with maps with buildings at this scale.

Figure 2

 2 Figure 2 Reconciliation example: the forest should not overlap buildings so its outline is distorted.