Andrea Fossati
email: andrea.fossati@epfl.ch

Élise Arnaud
email: elise.arnaud@inrialpes.fr

Radu Horaud
email: radu.horaud@inrialpes.fr

Pascal Fua
email: pascal.fua@epfl.ch

A Fossati Cvlab

E Arnaud

P Fua Cvlab

Tracking Articulated Bodies using Generalized Expectation Maximization *

come

Introduction

Tracking objects in 3D using as input a video sequence captured using a single camera has been known to be a very under-contstrained problem. This is especially valid if the target to be tracked is a human body. Persons usually perform fast motions, wear loose clothing and generate lots of self-occlusions and visual ambiguities. Other difficulties may be caused by cluttered backgrounds and poor image resolution. The problem is particularly acute when using a single video captured with a moving camera to recover the 3D motion and existing approaches remain fairly brittle. To cope with the under-constrained characteristic of the problem, incorporating motion models as prior into the algorithms has been shown to be a reasonable and effective assumption to obtain good results [START_REF] Moeslund | A survey of advances in vision-based human motion capture and analysis[END_REF]. The models can be physics-based [START_REF] Brubaker | Physics-based person tracking using simplified lower-body dynamics[END_REF] or learned from training data [START_REF] Rosenhahn | Scaled motion dynamics for markerless motion capture[END_REF][START_REF] Urtasun | 3D People Tracking with Gaussian Process Dynamical Models[END_REF][START_REF] Ormoneit | Learning and tracking cyclic human motion[END_REF][START_REF] Agarwal | Tracking articulated motion with piecewise learned dynamical models[END_REF][START_REF] Taycher | Conditional Random People: Tracking Humans with CRFs and Grid Filters[END_REF][START_REF] Sidenbladh | Implicit Probabilistic Models of Human Motion for Synthesis and Tracking[END_REF]. Furthermore, an efficient algo-Figure 1. The full pipeline of the approach, from the input image to the 3D reprojection.

rithm should be able (i) to obtain reliable image observations from the person of interest, these observations should not be corrupted by the moving background, and (ii) to fit a learned body configuration to these observations in a robust manner.

In this paper, we build on a recent work [START_REF] Fossati | Bridging the Gap between Detection and Tracking for 3D Monocular Video-Based Motion Capture[END_REF] that combines detection and tracking techniques to achieve 3D motion recovery of people seen from arbitrary viewpoints by a single camera. This algorithm helps us in having a first estimate of the body configuration for each image of the sequence, obtained by interpolating between detected key postures. Hence, the central idea of our work is to propose a robust framework to refine this first estimate that maximizes a novel image likelihood based on moving edge pixels. We first process the input sequence in order to obtain reliable edge information even if the camera is moving and thus no background subtraction is possible. We then use a Generalized Expectation Maximization (GEM) algorithm to iteratively assign the edge pixels to the correct body part and find the body configuration that maximizes the likelihood of these assignments. This is done by fitting a mixture distribution to the set of observations. The proposed mixture is composed of a uniform law to cope with corrupted observations, and Gaussian laws, each one associated to one side of each body limb. Expectation-Maximization (EM) [START_REF] Dempster | Maximum Likelihood from Incomplete Data via the EM algorithm[END_REF][START_REF] Celeux | Em procedures using mean field-like approximations for markov model-based image segmentation[END_REF] is a well established clustering technique and has been widely used in the Computer Vision community. It algorithm has been applied to the problem of articulated shape alignement with 2D image observations [START_REF] Rowley | Analyzing articulated motion using expectation-maximization[END_REF] or 3D data [START_REF] Dewaele | The alignment between 3-d data and articulated shapes with bending surfaces[END_REF][START_REF] Ménier | 3d skeleton-based body pose recovery[END_REF]. GEM [START_REF] Dempster | Maximum Likelihood from Incomplete Data via the EM algorithm[END_REF] is a variant of EM, that relaxes the maximization step into an optimization step.

In this paper, we propose to apply GEM to solve the problem of fitting the 2D projection of a 3D body cofiguration to a set of moving edge pixels. In our case the optimization is performed over parameters that define the 3D state of the person. We are therefore able to retrieve the full body pose and position in 3D even starting from single images. The exploration over the state space is constrained to a search over plausible configurations learned for a particular activity. This learning phase is performed using Principal Component Analysis, leading to embed the set of possible poses in a latent space of low dimensionality. The optimization is therefore performed over latent variables whose few dimensions keep the optimization problem tractable.

We demonstrate the effectiveness of the described approach on different sequences. The case of people walking along arbitrary trajectories is chosen. Persons who are not present in our motion database walk at different speeds and are seen from varying viewpoints, but are nonetheless accurately tracked in 3D. The results are also shown using a stick skeleton to demonstrate that the obtained results are fully 3D and can be reprojected to any viewpoint. The full pipeline of the approach is depicted in Fig. 1.

The Tracking Framework

The approach we have designed is structured as follows: first of all we obtain a reliable initial estimate of the 3D configuration of the person, using a key-pose detection technique together with the corresponding motion model, as suggested in [START_REF] Fossati | Bridging the Gap between Detection and Tracking for 3D Monocular Video-Based Motion Capture[END_REF]. Then we pre-process the video sequence we use as input in order to obtain a pretty clean edge image even if the camera is moving. Finally we use GEM to refine the initial pose estimation. This is done by matching the image edges to the edges obtained by projecting a 3D model of the person where limbs are considered as cylinders. We will explain in detail the 3 phases in the following subsections.

Pose Initialization

To obtain an initial estimate of the 3D pose of the person in each frame we adopt a techique presented in [START_REF] Fossati | Bridging the Gap between Detection and Tracking for 3D Monocular Video-Based Motion Capture[END_REF]. Basically it consists in detecting key-poses corresponding to a particular activity in few images of the video sequence. Since we consider walking in our case, the key-pose is chosen to be the particular pose when the legs are furthest apart, with the left leg in front of the right one. A rough estimate of the pose of the person in all the frames between two consecutive detections is then obtained: By interpolating the low-dimensional embedding of the activity through an appropriate motion model, an estimate value of the state S = {P, Q} of the person is retrieved. P is a 3-dimensional vector which represents the position and orientation of the body on a planar reference system coherent with the ground plane. Q is the set of the N joint angles in the body representation chain. In our experiments N = 78. In the case of the walking activity Q can be embedded in an ndimensional PCA space. A body configuration is thus represented by the vector λ = {P, φ} where φ is the latent variable of dimension n, with n << N, and a linear transformation relates φ to Q. In practice, usually, n = 3.

Sequence Pre-Processing

To cope also with sequences shot by a moving camera, we decided to elaborate the input images in order to retrieve the edges corresponding to the moving objects. These are assumed to be the objects that move in the image at a different velocity than the background. This phase is composed of two main parts:

• Motion Detection: This step is taken from [START_REF] Odobez | Separation of moving regions from background in an image sequence acquired with a mobile camera[END_REF] and simply retrieves, using optical flow, which pixels in the image are used to estimate the global motion of the camera. It also retrieves which pixels are considered as outliers for this estimation, and these are the pixels on which we will focus our attention since they are the ones that move at a different velocity than the background.

• Background matching: To obtain a more robust estimate of the edges belonging to the foreground, we also adopt a homography-based technique. Assuming that the motion of the camera is not too fast and not too close to the scene, we can consider the background to be planar. We then can simply take a window of N frames centered around the current one I t and match them to I t using a standard approach based on robust estimation of homographies using keypoints. Then we extract the edges, using a Canny-style edge detector, from all the frames in the window. Finally we warp all the obtained edge images to match I t , using the previously computed homographies. For all the pixels we will now have a set of N observations, which correspond to the same pixel being edge [START_REF] Agarwal | Tracking articulated motion with piecewise learned dynamical models[END_REF] or not (0) in the warped images. Now simply taking the median of these values for each pixel will tell us which edge pixels belong to the foreground (if the median is 0) and which to the background (if the median is 1). At this point we have an estimate of the edge pixels belonging to the background at frame I t , and simply subtracting this estimate from the edge extraction performed at I t will give us an estimate of the edge pixels that belong to the foreground.

By making a simple intersection of the outputs of these two parts, for each input frame, we will obtain a pretty robust estimate of which pixels belong to the foreground and are at the same time part of some edges. All the parts of this pre-processing algorithm are summarized in Fig. 2. We will use the output of this procedure as input for the following phase. Note that this phase can easily be replaced by a standard background subtraction algorithm if the camera is not moving.

Pose refinement through GEM

Definitions

Before explaining how we plugged the GEM algorithm into our framework, some definitions are provided. The observations points x = {x 1 , . . . , x M } are the points belonging to the contours obtained from the previous phase. Our goal is then to fit a body configuration to these observation points. To do so, we suppose that x is sampled from a 2D mixture distribution of K components (Gaussian laws) and an outlier component (uniform law). Each Gaussian is associated to one limb's side of the projected body pose. The parameters the k th Gaussian, i.e. its mean and covariance, are denoted as θ k . Let us note that θ k is a function of the the state S of the body, and therefore a function of λ. This parameterization is straightforward and is done as follows: From a given value of λ, the state S, defined by the 3-dimensional body pose P and by the set of joint angles Q, is used to generate a 3D representation of the human body. This representation has limbs which are considered as cylinders of different radius and length, dependending on the body part. Then this 3D model is projected onto the image and generates two segments for each cylinder, which should represent the 2 sides of the limb. Finally these segments are converted into Gaussian distributions, using the midpoint as representation of the mean and their length and a constant width to model an appropriate covariance matrix.

We then formalized the problem of fitting the projected body pose, now described as a Gaussian mixture, to the observed 2D cues as a classification task that could be carried out by the GEM algorithm. This problem boils down to the problem of finding an optimal value of λ such as the mixture components explain the image observation. The algorithm performs in 2 steps: First, each edge pixel is assigned to one of the components of the mixture. Let us note that a uniform component is added to the mixture to account for the corrupted observations. Second, the body configuration, i.e the mixture distribution, is fitted to the edge pixels by finding a new value of the parameter λ that decreases a distance function.

The assignement variables are denoted z = {z 1 , . . ., z M }. The event z m = k, m = 1, . . . , M , k = 0, . . . , K means that the observation x m is generated from the k th component of the mixture. The case k = 0 corresponds to the oulier case. By assuming conditional independence of the observations, we have:

p(x|z, λ) = M m=1 p(x m |z m , λ).
As explained before, the likelihood of an edge point being generated by the k th limb's side is modeled as a Gaussian distribution of parameters θ k (λ) = (µ k (λ), Σ k (λ)):

p(x m |z m = k, λ) = N (x m ; θ k (λ)) if (k = 0). (1)
Similarly, we define the likelihood of an observation to belong to the outlier cluster as a uniform distribution:

p(x m |z m = 0, λ) = U [A] = 1 A , ( 2
)
where A represent the observed data area i.e the image area.

For simplicity, we assume that the assignement variables are independent. Their prior probabilities are denoted

p(z m = k|λ) = p(z m = k) = π k ∀k = 0, . . . K with K k=0 π k = 1
and therefore

π k = 1 K + 1 .
The components posterior probabilities are denoted as α mk :

α mk = p(z m = k|x m , λ).
By applying Bayes' rule, we can obtain the following expression, where the observation likelihood are given by eq.

(1-2):

α mk = π k p(x m |z m = k, λ) K j=0 π j p(x m |z m = j, λ)
.

For k = 1, . . . , K, we have:

α mk = π k |Σ k (λ)| -1 2 exp -1 2 xm -µ k (λ) 2 Σ k (λ) 2ππ 0 A + K j=1 π j |Σ j (λ)| -1 2 exp -1 2 xm -µ j (λ) 2 Σ j (λ) , (3)
where the notation ab 2 Σ = (ab) T Σ -1 (ab) accounts for for the Mahalanobis distance. For k = 0, we have:

α m0 = 1 - K k=1 α mk . ( 4
) (a) (b) (c) (d) (e) (f)

GEM framework

Given the probabilistic model defined above, the goal is to determine the value of λ whose associated mixture distribution better explains the observations x. Treating assignements as the hidden variables, the GEM algorithm helps in achieving this goal by maximizing the joint probability p(x, z|λ). This probabiliy can be written as:

p(x, z|λ) = p(x|z, λ) p(z, λ) = M m=1 p(x m |z m , λ) p(z m |λ) = M m=1 K k=0 [π k p(x m |z m = k, λ)] δk(zm ) (5)
The random variables δ k (z m ) are defined as follows:

δ k (z m ) = 1 if z m = k 0 otherwise
Starting with the initial value λ (0) , the GEM algorithm proceeds iteratively and the iteration t consists in searching for the parameters λ that optimize the following expression:

Q(λ|λ (t) ) = E[log p(x, z|λ)|x, λ (t) ],
where λ (t) is the current estimate at iteration t. The expectation is calculed over all the possible assignements z. Using eq (5), we have:

log p(x, z|λ) = M m=1 K k=0 log(π k p(x m |z m = k, λ)) δ k (z m )).
Remarking that:

E[δ k (z m )|x, λ (t) ] = K k=0 δ k (z m ) p(z m = k|x, λ (t) ) = α (t) mk ,
where α

(t)
mk are the posterior likelihood calculated using eq. (3-4) with λ = λ (t) , we have:

Q(λ|λ (t) ) = M m=1 K k=0 α (t) mk log(π k p(x m |z m = k, λ)).
Replacing the likelihoods by their expression given by eq. (1-2) leads to:

Q(λ|λ t ) = M m=1 K k=1 α (t) mk - 1 2 x m -µ k (λ) 2 Σk(λ) -log π k (2π k ) -1 |Σ k (λ)| -1/2 + M m=1 α (t) m0 log(A π 0 ) (6)
We can now formulate the GEM algorithm as iterations of two steps at time t:

• E-step From the current value λ (t) , this step simply requires the computation of the posterior probabilities α (t) mk using eq. [START_REF] Celeux | Em procedures using mean field-like approximations for markov model-based image segmentation[END_REF][START_REF] Dempster | Maximum Likelihood from Incomplete Data via the EM algorithm[END_REF]. Each probability α (t) mk represents the likelihood of assigning observation point m to the k th limb's side or to the outlier class.

• M-step Provided that α (t) mk are computed, now Q(λ|λ t ) needs to be maximized over λ. Since the analytical computation would be higly non-linear, the generalized version of the EM algorithm is applied. This simply means that, instead of maximizing Q(λ|λ t ), we simply find a state λ (t+1) that increases the value of Q(λ|λ t ). In pratice, several λ i are sampled around λ (t) until this condition is reached.

We iterate this procedure a certain number of times until an improvement in Q is obtained, and than retain the corresponding body pose calculated from λ (f inal) for the current frame.

Results

We now presents some results we obtained by applying the full framework explained above to different sequences. All the sequences have been captured in non-engineered outdoor scenes and the camera has been kept in motion throughout all of them. Unfortunately, due to the intrinsic strength of the approach, we are not able to provide quantitative results but just qualitative evaluations. In fact, no techniques to collect ground truth data in such difficult conditions are available at the moment.

In Fig. 3 and in Fig. 4 there are two persons in the scene but only one is tracked. The same procedure could have been applied to the other subject to obtain distinct tracking results for both of them. In Fig. 5 the walking subject is undergoing a slight viewpoint change but this does not have inlfuence on the tracking results. For this sequence we also provide the ouput of the pre-processing phase that we used as input to obtain the shown results. In all the three cases we obtain a good reprojection of the 3D model limbs onto the limbs of the tracked subject, and also the reconstructed 3D pose looks plausible.

Conclusion

In this paper we have presented an approach to retrieve the 3D pose of a person using single viewpoint sequences, shot with a moving camera in everyday life environments. To this end we first initialize the body pose with the help of a motion model and then refine it using a novel Generalized Expectation Maximization algorithm. This algorithms has the task of assigning the contour pixels, obtained from the input images after a few pre-processing steps, to the corresponding body part. It also correctly finds the outlier pixels assigning them to a special class. The pose that gives the best match between the image measurements and the body parts is finally kept as output.

This framework is promising and gives good results in the walking case. We now plan to extend it to track persons performing different activities. Another direction we will follow is to test the algorithm also on constrained environment sequences, for which ground truth data are available, such as the HumanEva dataset [START_REF] Sigal | Humaneva: Synchronized video and motion capture dataset for evaluation of articulated human motion[END_REF]. This will provide us useful quantitative evaluations.

Figure 2 .

 2 Figure 2. Summary of the pre-processing algorithm: (a) Input image. (b) Edges extracted from the input image. (c) Background edges reconstructed through homographies. (d) Subtraction between (b) and (c). (e) Ouliers retrieved by the camera motion estimation technique. (f) Final output of the algorithm, obtained as intersection between (d) and (e).

Figure 3 .

 3 Figure 3. Pedestrian tracking and reprojected 3D model. First row: Frames from the input video. The recovered body pose has been reprojected on the input image. Second row: The 3D skeleton of the person is seen from a different viewpoint, to highlight the 3D nature of the results.

Figure 4 .

 4 Figure 4. Pedestrian tracking and reprojected 3D model. First row: Frames from the input video. The recovered body pose has been reprojected on the input image. Second row: The 3D skeleton of the person is seen from a different viewpoint, to highlight the 3D nature of the results.

Figure 5 .

 5 Figure 5. Post-processing output, pedestrian tracking and reprojected 3D model. First row: Output of the pre-processing phase, that is used as input for the following phases. Second row: Frames from the input video. The recovered body pose has been reprojected on the input image. Third row: The 3D skeleton of the person is seen from a different viewpoint, to highlight the 3D nature of the results.

* This work has been partially funded by the VISIONTRAIN RTN-CT-2004-005439 Marie Curie Action within the EC's Sixth Framework Programme. The text reflects only the authors' views and the Community is not liable for any use that may be made of the information contained therein.


Valentina Leone

Serena Villata

Taking Stock of Legal Ontologies: a Feature-based Comparative Analysis

Keywords: Legal ontologies, Semantic Web, Modelling legal knowledge

Ontologies represent the standard way to model the knowledge about specific domains. This holds also for the legal domain where several ontologies have been put forward to model specific kinds of legal knowledge. Both for standard users and for law scholars, it is often di cult to have an overall view on the existing alternatives, their main features and their interlinking with the other ontologies. To answer this need, in this paper, we address an analysis of the state-of-the-art in legal ontologies and we characterise them along with some distinctive features. This paper aims to guide generic users and law experts in selecting the legal ontology that better fits their needs and in understanding its specificity so that proper extensions to the selected model could be investigated.

Introduction

The modelling and the formalisation of legal knowledge are crucial aspects to implement in order to increase the automatic approach to the law field thus supporting the work of legal experts, enhancing legal information extraction and question answering systems and enabling automatic reasoning over legal cases.

Overlooking the first theoretical approaches to the formalisation of legal ontologies, such as the Functional Ontology of Law by [START_REF] Valente | A functional ontology of law. Towards a global expert system in law[END_REF] or the frame-based ontology proposed by van [START_REF] Van Kralingen | A conceptual frame-based ontology for the law[END_REF], in the early 2000s most of the e↵orts focused on the modelling of core ontologies and knowledge interchange formats, such as LRI-Core by [START_REF] Breuker | Epistemology and ontology in core ontologies: Folaw and lri-core, two core ontologies for law[END_REF], CLO-Core Legal Ontology by [START_REF] Gangemi | A constructive framework for legal ontologies[END_REF] and LKIF by [START_REF] Hoekstra | The lkif core ontology of basic legal concepts[END_REF].

Starting from the second decade of this century, the e↵orts concerning the legal knowledge representation moved towards the modelling of specific legal sub-fields as evidence of a greater awareness of the specificity which characterise each of them. This change of focus was accompanied by the consolidation of the Semantic Web as a reality for knowledge management and sharing. The Linked Data principles and the adoption of standardised knowledge representation formalisms as RDF and OWL are now common choices for publishing resources automatically accessible and processable through the Web.

However, despite the general acceptance of these good practices for the release of resources, the overall objective of a shared representation of legal knowledge has not been reached yet. The reuse of legal knowledge in fact requires a wide awareness of the already available resources which model the domain of interest. In order to evaluate a possible reuse, all the actors involved in the ontology building process, i.e., legal experts as well as developers, need to be constantly up-to-date about the state-of-the-art and they are expected to deepen the ontological commitment and the methodological choices adopted by each resource. If not, the risk is to create and release on the Web redundant representations of knowledge, which obstruct the economy of information promoted by the Semantic Web.

Considering that the last decade has seen a proliferation of ontologies and vocabularies which model di↵erent legal fields, it can be a good opportunity to take stock of the state-of-the-art concerning legal knowledge representation. Therefore, we propose a structured comparative analysis of the most recent legal ontologies and vocabularies. This work is mainly addressed to developers as well legal experts involved in the ontology building process. Our aim is to provide them with a practical source of information to consult in order to make an informed and conscious choice about the already modelled and reusable pieces of knowledge provided by other ontologies.

The paper is organised as follows: Section 2 describes the ontologies we analysed, Section 3 provides a description of the main features we used to study and classify them, and Section 4 discusses some insights resulting from our classification. In Section 5, future work directions end the paper.

Selected legal ontologies

In the past years, studies aiming at analysing and classifying legal ontologies have already been published. [START_REF] Casellas | Legal ontology engineering: Methodologies, modelling trends, and the ontology of professional judicial knowledge[END_REF] proposed a comprehensive survey about legal ontologies spanning a fifteen-years time range approximately, from early 90s to 2011. The ontologies features she considered in her analysis mainly concern the intended use of the ontology, the level of generality (core or domain), the degree of formalisation, the methodology used to build and evaluate the ontology, and its availability for reuse. Recently, de Oliveira Rodrigues et al. ( 2019) enlarged the time-frame of their literature review and they analysed the legal ontologies proposed from late 90s to 2017. Their work presents di↵erent classification studies aimed at grouping ontologies among di↵erent dimensions, some of them similar to those already proposed by [START_REF] Casellas | Legal ontology engineering: Methodologies, modelling trends, and the ontology of professional judicial knowledge[END_REF]. The new categorisation dimensions introduced by the authors concern the country and the venue where the literature about an ontology was published, its underlying legal theory, the syntactic and semantic peculiarities of legal texts that were addressed while producing the ontology (e.g., the dynamism of normative texts or the overlap of jurisdictions) and the legal subdomain it models.

If, on the one hand, the work of [START_REF] Casellas | Legal ontology engineering: Methodologies, modelling trends, and the ontology of professional judicial knowledge[END_REF] seems now out of date due to the lack of many recently developed ontologies, in [START_REF] Mário De Oliveira Rodrigues | Legal ontologies over time: A systematic mapping study[END_REF] literature review it is di cult to identify the current emerging trends in the field due to the wide temporal interval their study focuses on. Moreover, information used to organise the ontologies in di↵erent types of classification were only collected from the scientific papers published to describe them. The ontologies documentation and the actual implementation, when available, seems not to have been taken into consideration. This methodology limits the analysis to a theoretical level which leaves out more technical details and deeper modelling choices.

Nowadays, the reuse of knowledge promoted by the Semantic Web principles require ontologists to exploit, as much as possible, the legal knowledge already made available through vocabularies, ontologies and knowledge graphs. To do so, experts who are involved in the ontology building task and who are planning to reuse an existing resource need to consider a wide set of details. Usually, those details are not limited to the theoretical features of an ontology, but also include more practical information, e.g. the on-line availability of the ontology source file or the presence of a specific class inside the ontology.

Starting from the Semantic Web principle of knowledge reuse, we take the classification of legal ontologies one step further by analysing the details of their implementation and including practical information concerning their actual availability for reuse. As both the aforementioned state-of-the-art literature reviews already analysed the resources produced in 90s and in the first decade of this century, we focused out attention on the most recently released legal ontologies. Thus, as an ideal continuation and extension of [START_REF] Casellas | Legal ontology engineering: Methodologies, modelling trends, and the ontology of professional judicial knowledge[END_REF] analysis, we considered the ontologies released from 2012, by the addition of two older ontologies which are still well known and used as it will be explained later. We excluded from our study the ontologies whose source files are not available for download. We make this decision to maintain consistency with our purpose to enable readers to analyse just the ontologies actually available to reuse. As it will be noticed in the following sections, only two ontologies do not accomplish this requirement. This is because they are very recent (less than two years old) and we believe that there is a possibility that they will be released later. Moreover, we decide to focus our attention on the resource that model a legal domain referring to some European or globally applicable legal framework. The ontologies that focus on a national jurisdiction were thus excluded from our analysis.

According to our selection criteria, we analysed a set of ten ontologies belonging to five domains related to di↵erent legal field, as shown in Fig. 1: 1. Policies: it refers to the ontologies which model the permitted, mandatory and prohibited actions that can be made on a digital or material asset; 2. Licences: it includes the ontologies modelling the actions allowed on a resource protected by the intellectual property rights; 3. Tenders and procurements: this domain includes the ontologies modelling the processes used by public administrations and authorities to find contractors to entrust with services or supplies; 4. Privacy: the ontologies model the concepts concerning the protection of personal data.

Each domain is characterised by the di↵erent sources of law it refers to and by a distinctive jargon usually reflected in the classes and properties names of each related ontology.

In addition to the aforementioned domains, as showed in Fig. 1, we analysed another set of four "cross-domains" ontologies which are di cult to associate to a specific legal field because they were proposed as a more generic model for expressing deontic operators (Normative Requirement Vocabulary), represent-ing the content of legal texts in a machine-readable format (LegalRuleML) and indexing documents for search (Eurovoc and European Legislation Identifier).

In the following part of this Section, we provide a short description of each ontology.

Policies

Open Digital Rights Language

Open Digital Rights Language1 (ODRL) is a language promoted by the ODRL Community Group2 in order to model policies for digital content and media [START_REF] Steyskal | Defining expressive access policies for linked data using the odrl ontology 2.0[END_REF]). To do so, ODRL o↵ers a Core Vocabulary to specify the minimum set of terms suitable to model the policies and a Common Vocabulary of general terms to model, for example, actions regulated by the obligations, permission and prohibitions expressed in the policies.

It models di↵erent types of policies, making a distinction between (i) a policy which is an agreement between an assigner and an assignee, (ii) a policy which is an o↵er from an assigner to an undefined wide audience and (iii) a policy which is a generic set of rules with no specified assigner and assignee.

Concerning the deontic logic, ODRL allows the expression of the e↵ects associated to the non-compliance of an obligation, the e↵ects of the noncompliance of some preliminary duties to obtain a permission and the duties to be accomplished for remedying to a violated prohibition. Finally, it is possible to associate a policy with some meta-information concerning, for example, its creator, its coverage (i.e., the jurisdiction applied upon the policy) and the reference to older versions of the policy.

Linked Data Rights ontology

The Linked Data Rights (LDR) ontology3 was developed by the Ontology Engineering Group4 and it is specifically designed to model the rights which can be exercised on a Linked Data resource. LDR ontology is based on ODRL from which it extends the classes Action, Asset, Policy and Rule in order to model the conditions of use of the Linked Data resources.

In detail, LDR defines three subsets of the ODRL Action class in order to represent the actions permitted on a resource protected by the intellectual property right, to use a database of Linked Data and to access a resource via the REST and SPARQL services. Moreover it defines which are the types of Linked Data resources (data-sets, link-sets, ontologies, resources and statements) and which are the types of policy that can be concluded (contract or licence).

As in this ontology there is also a reference to the intellectual property rights, but this is not the main focus, we included this ontology in the policy domain. However, it can be useful to take into account this ontology for the intellectual property field when the other models do not fit the needs of the users.

Licences

Creative Commons Rights Expression Language

The Creative Commons Rights Expression Language (ccREL)5 is the standard promoted by Creative Commons6 (CC) to express the copyright licensing terms in a machine readable way. This ontology is more than six years old, but we decided to include it in this survey because of the wide dissemination of the Creative Commons licensing terms to regulate the use of resources protected by copyright.

The ccREL ontology models all the relevant actions provided by the Creative Commons standard, distinguishing among permissions, requirements and prohibitions. All of them are further specialised by the actions which allow the sharing of a work with third parties while maintaining the copyright. Moreover, the ontology allows the specification of the legal jurisdiction which applies on the modelled licence to be represented.

L4LOD

The Licence for Linked Open Data (L4LOD)7 vocabulary uses a light ontological structure to organise the terms concerning licensing in the Web of Data. The deontic operators (permission, prohibition, obligation) are further specified in order to detail which actions can be necessarily or possibly made and avoided on Linked Open Data sources.

Tenders and procurements

LOTED2

LOTED28 , by [START_REF] Distinto | Loted2: An ontology of european public procurement notices[END_REF], is a legal ontology which aims to represent the knowledge concerning the public procurements domain in the European Union. This ontology exploits the terminology contained in TED 9 , the reference online platform where all the public institutions of European and EEA countries publish their procurement notices. Starting from this website, LOTED2 enriches the TED lexicon with an ontological structure legally rooted on two European Union directives about the public contracts field: the Directive 2004/17/EC and the Directive 2004/17/EC. LOTED2 uses these two directives in order to model the legal concepts involved in the process of awarding a public contract, among which there are: the roles that an agent can play in the process, the di↵erent types of competition, the di↵erent types of documents used for the publication of a notice, the legal resources that regulate the field and the o↵ers submitted for awarding a public contract.

The aforementioned aspects are all contained in the core version of LOTED2. An extended version of the ontology in which the concepts modelled in LOTED2 are integrated with some concepts and properties of the Good Relations is also available.

PPROC

The Public Procurement Ontology10 (PPROC), by [START_REF] Félix Muñoz-Soro | Pproc, an ontology for transparency in public procurement[END_REF] aims to semantically represent the information published in o cial procurement documents, focusing on the Spanish law and in the EU law in general. Besides representing the usual information about tenders, PPROC objective is to represent the whole process of execution of tenders, starting from the publication of the contract until its termination.

Among its distinctive features, PPROC provides a classification of contracts according to di↵erent criteria, e.g. their administrative type or their subdivision in lots. Moreover it allows the specification of the criteria used for the evaluation of a tender, distinguishing them between subjective and objective criteria. The agents involved in a contract are expressed in the form of roles played during its execution and some hierarchies of roles are modelled. PPROC also represents the aspects which do not belong strictly to the set of properties of a tender or a contract, but which could be of interest for the suppliers (e.g., the kind of procedure followed during the execution of the procurement or its urgency).

It is important to remark that, in its attempt to model the public procurements and tenders domain, PPROC makes a big e↵ort to try to reuse information already modelled in other existing ontologies, limiting the introduction of new classes and properties to very specific modelling requirements.

Privacy

Data Protection Ontology

The Data Protection Ontology11 by [START_REF] Bartolini | Using ontologies to model data protection requirements in workflows[END_REF] concerns the data protection field, as it is modelled in the GDPR (General Data Protection Regulation 2016/679). The Regulation came into force in May 2018, three years after the ontology published by [START_REF] Bartolini | Using ontologies to model data protection requirements in workflows[END_REF] . However, even if the ontology is not based on the final version of the GDPR text, we decided to include this ontology to enable the interested reader to compare it with other two ontologies modelling the same field, that is GDPRtEXT (see Subsection 2.4.2) and PrOnto (see Subsection 2.4.4). This ontology is part of a more complex system where it plays the role of a knowledge base used to express data protection requirements as annotations inside a workflow model (e.g. a business process). The Data Protection Ontology was developed manually, extracting the terms of the domain of competence from a corpus of o cial normative sources. The main concepts modelled by the ontology concern the data protection principles, the rules of data processing and the rights of the data subject. In particular, the data protection principles are the glue that relates and justifies the duties of the data controller as well as the rights of the data subjects, making explicit the relation between a data subject right and the corresponding obligation for a data controller to guarantee this right.

GDPRtEXT

The GDPRtEXT12 (GDPR text extensions), by [START_REF] Harshvardhan | Gdprtext-gdpr as a linked data resource[END_REF], is one of the most recent ontologies analysed in this survey and it deals with a currently central topic in the privacy domain: the aforementioned General Data Protection Regulation (GDPR).

The aim of GDPRtEXT is to represent the GDPR as a Linked Data resource, assigning an URI to each relevant part of the text. To do this, it extends some classes and properties of the ELI ontology (presented in Subsection 2.5.3) in order to specify the di↵erent parts in which the GDPR's text is structured (such as articles, recitals, citations and so on) and the properties that hold among them.

The ontology also provides more than 200 classes suitable to represent the relevant concepts introduced by the regulation and concerning the data protection field. The concepts' macro-areas modelled by the ontology are related to the categories of personal data, the concept of consent, the agents involved in the processing of the data, the actions that can be made on data, the rights of the data subject and the obligations of each agent which deals with the data.

GDPRtEXT also introduces a special property isDefinedBy which exploits the URI scheme created according to the Linked Data principles in order to link its classes to the relevant part of the text of the GDPR explaining the concepts they represent.

PrivOnto

PrivOnto is an ontology developed by [START_REF] Oltramari | Privonto: A semantic framework for the analysis of privacy policies[END_REF] in the context of the Usable Privacy Policy project13 and its aim is to model annotated privacy policies explaining the data practices implemented by a website.

PrivOnto was built from a corpus of 115 privacy policies of websites belonging to US-based companies. This corpus was annotated by some domain experts who were asked to identify the main categories representing data practices, together with their attributes. The result was a set of ten categories of data practices represented as frames. Each frame has its set of attributes together with the corresponding values, that refer to the fragment of the privacy policy they are taken from. Indeed, PrivOnto allows the modelling, with specific classes, of di↵erent parts of the text and the annotations associated to each of them.

As an application of this resource, a set of 57 di↵erent SPARQL queries was engineered in order to browse the annotated corpus over its di↵erent dimensions (categories, attributes and values).

PrOnto

Similarly to the Data Protection Ontology and GDPRtEXT (see Subsection 2.4.1 and Subsection 2.4.2), PrOnto (Privacy Ontology), proposed by [START_REF] Palmirani | Pronto: Privacy ontology for legal reasoning[END_REF], focuses on the modelling of the knowledge concerning the GDPR. The purpose of PrOnto is not only to support information retrieval, but also to provide a model on which techniques of legal reasoning and compliance checking could be applied.

Among its distinctive features, PrOnto focuses on the distinction between agents and roles, with the former able to cover particular roles inside di↵erent contexts and for a limited interval of time. Moreover, PrOnto models the sequence of actions aimed at processing personal data. Specifically, it makes a distinction between a planned sequence of actions named workflow and the real execution of this plan, named workflow execution. A temporal reference can be associated to each action and some boolean attributes are associated to the workflow in order to represent and automatically infer its lawfulness, fairness and transparency.

Besides the traditional deontic operators, (i.e., permissions, prohibitions, obligations and duties) PrOnto explicitly models compliance with and violation of an obligation by relating the obligation class with the compliance and violation classes as well as a right with the corresponding permission.

Within the DAPRECO project by [START_REF] Bartolini | Towards legal compliance by correlating standards and laws with a semiautomated methodology[END_REF], the PrOnto ontology has been associated to fine-grained if-then rules in reified Input/Output logic [START_REF] Robaldo | Reified input/output logic: Combining input/output logic and reification to represent norms coming from existing legislation[END_REF]). Rules represent GDPR norms and are encoded in LegalRuleML(see Section 2.5.2). To date, this the biggest knowledge base in LegalRuleML freely available online14 .

Cross-domains ontologies

Eurovoc

Eurovoc15 is a multilingual and multidisciplinary thesaurus managed by the Publications O ce of the European Union. Its function is to index the documents issued by the European Union Institutions in order to ease their retrieval.

The concepts are organised in 21 sectors which in turn are composed by micro-thesauri. Each sector concerns a field of competence of the European Union and each concept can be associated with only one sector to avoid ambiguities (except for the sector Geography which allows a polihierarchy).

Each concept is lexicalised by a set of terms in which only one is the preferred term (i.e., the term used for the indexing of the concept), while the others are the non preferred terms (i.e., synonyms of the preferred term not used for the indexing of the concept they represent). All the terms associated to a concept are provided with their translations in all the 23 languages spoken inside the European Union and Macedonian, Serbian and Albanian. Nevertheless, while there is a unique correspondence between the di↵erent translations of a preferred term, the set of the non preferred terms associated to a concept can vary considering their representation in di↵erent languages in order to maintain the linguistic nuances of each national legal lexicon.

The terms in Eurovoc are also linked to each other through some semantic relations: beside the classical hierarchical one, also associative relations can be found among terms that are semantically related but are not on the same hierarchical structure.

Although the project which led to the creation of Eurovoc is more than twenty years old, its updating is constant and frequent: the thesaurus is continuously enriched with new terms concerning the topics dealt by the EU and cleaned up by removing obsolete terms.

LegalRuleML

LegalRuleML 16 , by [START_REF] Palmirani | Legalruleml: Xml-based rules and norms[END_REF] and [START_REF] Athan | Legalruleml: Design principles and foundations[END_REF], is a project promoted by the OASIS LegalRuleML Technical Committee 17 which aims to develop a standard for the legal knowledge representation and exchange. To reach this goal, LegalRuleML o↵ers a markup language which permits the harmonisation of di↵erent types of legal texts, such as norms, guidelines and policies.

Even though LegalRuleML is not properly an ontology but a markup language, we decided to include this resource inside our survey because it provides a rich set of concepts and properties which enable the management of the complexities of a formal representation of legal texts in a machine-readable way. Among its distinctive features, LegalRuleML provides some parameters to model the di↵erent interpretations that could be associated to a rule, to keep track of the author of a document or its fragments, to manage the temporal evolution of the norms and to take into account the defeasibility of the law.

Thus, the advantage and the final goal of LegalRuleML is the possibility to maintain the same expressive power independently from the way the norm is expressed, using the natural language or a formal machine-readable representation.

European Legislation Identifier ontology

The European Legislation Identifier (ELI) ontology 18 is a model which allows the publication of legal documents of di↵erent European Union countries using a shared and uniform set of metadata in order to enhance interoperability among the national administrations. Nowadays, this resource is used by 11 of the 28 EU countries and by the EU Publication O ce.

According to the information published by ELI Task Force (2018), the ELI ontology reflects many of the basic principles of FRBR (Functional Requirements for Bibliographic Records) vocabulary 19 , contextualising them into the legal field. While the FRBR provides the description of a bibliographic record in terms of work, expression, manifestation and item, the ELI ontology describes a legal document through the concepts of legal resource, legal expression and format. In detail, legal resource refers to the intellectual creation, independently from its translation in more than one language and from the format used for its publishing; it corresponds to the work property in FRBR. The legal expression concept is the realisation of a legal resource using a sequence of signs as, for examples, the alphanumeric characters and it corresponds to the expression property in FRBR. The format refers to the physical means 16 http://docs.oasis-open.org/legalruleml/legalruleml-core-spec/v1.0/ legalruleml-core-spec-v1.0.html 17 https://www.oasis-open.org/committees/tc_home.php?wg_abbrev=legalruleml 18 https://publications.europa.eu/en/web/eu-vocabularies/model/-/resource/ dataset/eli 19 https://sparontologies.github.io/frbr/current/frbr.html used to store the legal expression (could be paper or an electronic format) and it corresponds to the manifestation property on FRBR. However, the item property of FRBR does not have a correspondence in the ELI ontology.

Since the documents issued by di↵erent EU countries could be described with di↵erent metadata according to the national jurisdiction they refer to, the ELI ontology overlooks these di↵erences in order to represent only the common metadata of the national legal documents, providing the user the possibility to personalise and extend the set of metadata according to its needs. Therefore, the set of properties that can be established among the aforementioned three classes is not so large and they mainly concern the type of the represented document, the topics it deals with, the entry into force and the legal value of the document according to the format it is represented with.

Normative Requirements Vocabulary

The Normative Requirements Vocabulary 20 (NRV), by [START_REF] Gandon | Normative requirements as linked data[END_REF], is an ontology which extends LegalRuleML and whose aim is to exploit the standard frameworks o↵ered by the Semantic Web in order to represent normative requirements and rules. Di↵erently from other existing legal ontologies, NRV is not limited to the representation of the three main deontic operators (i.e. permission, obligation and prohibition), but it specifies and organises them in a hierarchical structure according to di↵erent criteria which concern: the need for compensation, the possibility to breach or fulfil a requirement and the temporal aspects involved in their validity and compliance.

NRV also uses the named graphs of RDF 1.1 in order to represent the states of a↵airs, that is the contexts on which the deontic operators can be applied. Then, given that OWL does not support the named graph structure, a SPARQL approach is tested for making complex inferences in which the formalised normative requirements are applied upon a state of a↵airs.

Features Description

This Section contains a description of each feature we used to classify the legal ontologies. We organised the overall set of features in three macro-classes according to the type of property modelled by the features they include. More specifically, we distinguish between:

general information class: it contains several features about the ontology disclosure and the purpose of its creation;

modelling information class: it refers to the methodological and technological choices followed in order to build the ontology;

semantic information class: it groups all the features concerning the way in which the ontology models the knowledge it refers to.

20 http://ns.inria.fr/nrv/v1/nrv_v1.html As mentioned before, each of these macro-classes is a set of more specific features as detailed in Table 1. In the following part of the Section, we provide a description of each feature used to classify the analysed legal ontologies.

General information class

As mentioned above, the features contained in this class refer to the generic purpose for which the ontology was built together with some practical information useful for those who are actually interested in using the resource. Eight features belong to this class. The first information concerns the extended name of the ontologies. As they are often referenced by their acronyms in literature, their full name could provide to the reader a first insight of the scope of the ontology, also helping her to memorise the acronym itself. The legal domain feature refers to one of the five domains listed in Section 2 and it corresponds to the visual information represented in Fig. 1. This feature is further specified by purpose which contains a brief description of the main scope and function of the ontology inside the specified domain. Finally, the year feature indicates the year of the ontology first release.

Together with this general information, we decided to include some more specific features in order to provide the readers with useful information con-cerning the retrieval of an ontology on the Web and its reuse. To this purpose, the current version feature refers to the most recent released version of the ontology, while licence provides the information concerning the licence under which a resource is made available for reuse. Such feature could help interested users to fairly use the ontology, respecting any limitation and constraint in its adoption. Then, to assess the frequency of updates made to an ontology, we introduced the updates frequency feature, whose possible values are: low, medium and high. In the following tables, the date of the last update is provided in brackets. This feature is important to understand if the resource already reached a stable point and to evaluate if it is kept up-to-date according to the changes of the domain that it models.

In order to provide readers with an estimate of how much an ontology is known, we also include the feature number of references. To estimate this number, we used the Google Scholar21 search engine and, for each paper describing an ontology and included in the bibliography of this study, we took the number of references from its publication date until May 2019, as returned by Google Scholar. For resources which do not have a reference paper, we searched from the number of citation starting from 2012 in order to be consistent with the year we chose two start our ontology collection (see Section 2). Moreover, we used to research keywords: the first one contained the extended name of the ontology followed by the term "ontology" (except for Eurovoc, where we used the term "thesaurus" as it is usually associated to this resource), while the second one contained the corresponding acronym (if available) followed again by the term "ontology". The two keywords were then linked by a disjunction operator (i.e., OR). For instance,for the ELI ontology we built the following string: "European Legislation Identifier ontology" OR "ELI ontology", where the quote marks were used to obtain only exact matches.

Finally, the link feature specifies the at-present active link to the Web page containing the ontology documentation. Usually, if available, this Web page also contains the link to download the ontology source file.

Table 2, Table 3

Modelling information class

The eleven features contained in this class concern all the modelling choices which are immediately reflected in methodologies and standards used to build the ontologies.

The language feature refers to the main natural language used to specify the concepts, the relations and the lexicon inside the ontology while development indicates the approach adopted in the ontology building process, that is a bottom-up approach (from lexicon to concepts), a top-down approach (from legal foundations to lexicon) or a middle-out approach, which merges the techniques of the previous two methods.

The construction feature specifies if the modelling of the ontologies' concepts and relations was manual or used some Natural Language Processing (NLP) technique to partially automatise the process of building the ontology. Linked to this aspect, two features concern the sources from which the concepts inserted in the ontology were chosen. The first one is knowledge source (KS) for terms extraction, that is legal documents or websites used to extract the relevant concepts and the corresponding ontology lexicon. In contrast, the external vocabulary (EV) reference feature refers to the existing ontologies and vocabularies which the ontology reuses specifying the URIs of some of their concepts and properties. Therefore, the di↵erence between these two last features is that the legal documents listed in correspondence of the first feature only provide the raw concepts which are relevant for the domain but which needed to be formally modelled before being inserted in the ontology, while the second feature looks at the reuse of some parts of existing ontologies in order to adopt some concepts and relations already modelled by them. Similarly, the ground ontology feature refers to the main ontology which is extended by the analysed resource. This feature can be seen as a specialisation of external vocabulary reference. The di↵erence is that an ontology which uses another one as ground ontology inherits from it the great part of its concepts and structure, while an ontology that makes some reference to external vocabularies adopts its own structure and reuses only some concepts of other existing resources.

The level of structure feature is a quantitative evaluation of the number of concepts and relations modelled by the ontology. This property can be expressed by three values that denote a growing number of classes and relations: lightly structured, moderately structured and highly structured. The knowledge representation (KR) formalism refers to the formal language used to represent the ontology in a machine readable way. At present, the two de facto standards used to represent ontologies are RDF and OWL. Connected to this feature, the axioms feature is also considered. The feature refers to the three possible level of axioms planned by OWL 2 specification: class expression axioms, object property axioms and data property axioms.

Taking into account the principle of reuse promoted by the Semantic Web, we also considered the ontology design patterns used to represent some parts of knowledge whose modelling was already codified in a standard representa-tion. Finally, the evaluation feature analyses which methods were adopted to evaluate the created knowledge model provided by the ontology.

Tables from 5 to 7 classify the analysed ontologies according to the features of this class.

Eurovoc

Semantic information class

So far, we presented a set of features which are independent from the legal domain and which could be applied potentially to analyse and compare the ontologies belonging to every domain of interest. In this Section, we analyse three features which specifically refer to the way in which the legal knowledge is modelled.

The modelling of temporal aspects feature allows to specify if an ontology models some temporal aspects concerning the legal field of interest and provides a brief description of the way in which this is done. There are a lot of di↵erent possibilities to model a temporal feature inside an ontology: it could be a simple time mark associated to the issue of a policy, or an interval of time which specifies the validity of an obligation or, again, it could be an implicit representation of time which focuses on the parameters that could vary over it, e.g., the status of a norm or the jurisdiction under which it is valid.

When an ontology permits the modelling of norms and rules, the adopted normative model feature specifies the type of rules that the ontology can represent (e.g., constitutive rules, prescriptive norms, etc.). Finally, the deontic logic model feature provides a short description of the deontic operators modelled inside the ontology (i.e., obligation, duties, permissions and rights). As for the previous feature, this one holds only if the ontology deals with norms and rules. However, since norms are one of the main focus of the legal domain, a lot of the analysed ontologies model the deontic operators. For example, some of them only represent permissions, obligations and prohibitions, others model also the violations of obligations and prohibitions, while others provide a hierarchy of deontic operators organising them according to di↵erent criteria (e.g., temporal criteria or need for compensation of a violated norm).

The classification of the analysed ontologies according to these three features is provided in Tables from 8 to

Concluding remarks

The analysis of the ontologies contained in this survey and the completion of the tables included in the previous Section led us to a greater awareness about some weaknesses concerning the panorama of the existing legal ontologies. The remarks we made can be grouped according to the division in macro-classes used to organise the features described previously.

Concerning the general information about an ontology (summarised in the general information class) some lack of standardisation still exists in the graphical user interfaces (GUIs) used to make the ontology scope and content available to the final user. Currently, the LODE 22 tool is one of the most common Web services used to automatically create these GUIs. LODE processes the owl file of an ontology to create an HTML page which lists classes, properties and axioms of the ontology together with some metadata indicating the author(s), the release date, the current version and the licence of the ontology, as shown in Fig. 2.

An unified look for the GUIs exposing the content of an ontology could be helpful for users concerned with ontology building and reuse, as it could reduce the time spent to look for the information within websites.

Linked to this problem, the second issue concerns the need to make explicit all the details concerning the download and the licence of an ontology. Browsing the Web pages of the di↵erent ontologies, it was sometimes di cult for us to find this information. However, it seems clear that without them, a fair reuse of the ontologies would not be promoted.

A special case concerns the resources made available by the European Union whose orientation towards the Semantic Web and the Linked Open Data is remarkable. They are all collected in the EU vocabularies portal 23 where a tab-like GUI organises all the information about a resource as it shown in Fig. 3.

As it can be noted, this interface is very di↵erent from the GUI which can be created with LODE. Even if the download links are well visible, the type of licence which regulates the use of each resource is not specified. We found this information in the old Web sites of each resource, before their grouping inside the portal, under the heading "Legal notice". Moreover, in the current interface of the EU vocabularies portal, the title of each tab sometimes does not clarify the information associated with it, and the documentation of the di↵erent resources is not standardized. For example, the documentation of ELI is an xlsx file which must be downloaded and opened with a commercial software in order to be visualized. In contrast, the description of Eurovoc is better organized into expandable windows inside the tab.

Therefore, according to these remarks, some improvement would be desirable to harmonize the way in which the metadata on legal ontologies issued by the EU are organised inside the portal.

22 github.com/essepuntato/LODE 23 publications.europa.eu/en/web/eu-vocabularies Fig. 2: An excerpt of the NRV GUI, automatically generated using the LODE tool.

Concerning the methodological and technological choices made during the development of an ontology, this information is never displayed on the aforementioned GUIs and it could be di cult to find also reading the literature published together with the ontology. However, this information is important for several reasons: first of all, it provides a scientific foundation to the work allowing other researchers to analyse and verify it, secondly, it enables an easy and understandable interpretation of the corresponding literature in which this information is sometimes implicit, even if it is at the basis of the development of the ontology.

A positive aspect that we noticed during the analysis of the proposed resources is the trend promoted by the Semantic Web principles to reuse the concepts and the properties of other ontologies or to propose extensions of existing ontologies using them as ground ontologies. However, we noticed a lack of sensitivity to the adoption of the ontology design patterns (ODPs) in Fig. 3: Some information about ELI as displayed on the EU vocabularies portal.

the ontology building process. As outlined by [START_REF] Gangemi | Ontology design patterns[END_REF], ODPs are modelling solutions to solve recurrent ontology design problems. The ODPs di↵er from the reuse of single concepts as they are micro-ontologies which model a piece of knowledge which occurs frequently in di↵erent domains. The low use of ontology patterns could be associated to the di culty to identify, inside a complex modelling problem, the parts which could be covered by an ODP because it requires the knowledge of the full landscape of available ODPs. However, some portals ease their retrieval collecting the existing design patterns (among them we mention www.gong.manchester.ac.uk/odp/html and www.ontologydesignpatterns.org).

Finally, the most important lack that we noticed in the features involving the modelling information class is evaluation. In the literature related to the resources, we have not often found any mention to the criteria used to evaluate the proposed models. However, as shown in Table 7, the current trend is to provide SPARQL queries to test the validity of some competencies questions and the fulfilment of some objectives which the ontology should reach. This is especially done by the most recent ontologies as for example NRV and PrOnto. In contrast, older ontologies mention in they literature the fact that they are used by real users, as in the case of PPROC or the resources released by the European Union. We can consider it as a method of evaluation since the actual use of a resource is one of the best ways to test the robustness of a knowledge model.

The considerations we made concerning the semantic information class call back the aforementioned problem of the ontologies design patterns. Indeed, we noticed that each ontology models a specific legal domain and adopts its own ontological commitment, with a consequent proliferation of di↵erent knowledge models referring to similar use cases. For example, the deontic operators, being one of the main focus of di↵erent legal domains, are modelled in many ontologies but the aspects that each of them considers are di↵erent. For example, some ontologies associate a temporal reference to the validity of an operator (as LegalRuleML or ODRL do) while others do not (e.g. L4LOD). Or, again, some ontologies make a distinction between an obligation which is respected and an obligation which is violated (as NRV), while others not (e.g. LDR). Thus, even if the legal domain has plenty of recurrent use cases, few e↵orts are dedicated to find a standardized solution to design problems which recur often within the legal domain.

Future perspectives

According to the remarks proposed in the previous Section, some improvements could be done to enhance an ontology building process oriented towards the reuse of existing resources.

First of all, the creation of a new set of metadata to include inside the ontology source file should be evaluated in order to complete the information that is already showed in the graphical interfaces displaying the content of an ontology. We believe that the most needed information is both of a general and of a legal nature. In the first instance, some metadata for indicating the methodology of development followed to create the ontology and the embedded design patterns would be useful to ensure the reuse of the ontology itself. In the second instance, we think about a set of metadata able to summarise some of the purely legal aspects modelled into an ontology. Some of these metadata could recall some of the features used inside this survey to classify the ontologies, as for example the modelled deontic operators and the type of modelled norms (if this feature is applicable).

In addition to a new set of metadata for the description of the ontology features, it could be important to address the problem pointed out at the end of Section 4 concerning the need of legal design patterns to reuse inside the ontologies. Some witnesses in this direction are provided by [START_REF] Haapio | Design patterns for contracts[END_REF] and [START_REF] Haapio | Legal design patterns for privacy[END_REF]. An e↵ort to discover recurrent legal knowledge and to model it in the form of a standardises legal use case with the corresponding ontology design pattern could improve the quality of the released ontologies reducing the e↵orts required to model legal knowledge. This is especially true considering that usually the design of ontology-based systems is assigned to computer scientists who need, in addition to the technical background, a further knowledge about the legal domain which usually they do not hold.

Fig. 1 :

 1 Fig. 1: The five domains according to which the analysed ontologies were grouped.

Table 1 :

 1 The macro-classes and the corresponding features used to classify the legal ontologies

	Macro-class	Features
		extended name
		legal domain
		purpose
		year
	general information	current version licence
		updates frequency
		number of references
		link
		development
		construction
		language
		knowledge sources for terms extraction
		external vocabularies references
	modelling information	ground ontology
		level of structure
		knowledge representation formalism
		axioms
		design patterns
		evaluation
		modelling of temporal aspects
	semantic information	adopted normative model
		deontic logic model

Table 2 :

 2 and Table 4 classify the ontologies presented in Section 2 according to these features. Classification of ontologies published from 1984 to 2013 according to the general information class of features. Last revision of the information contained in the table: May 2019.

	L4LOD	Licenses for	Linked Open	Data	licences	representation of	existing licensing	terms in the Web	of Data	2013	0.2			CC-BY-SA	low	(10 May 2013)	31	bit.ly/2m40FSn
	ODRL		Open Digital	Rights Language		policies	representation of	the conditions of	usage of digital	assets	2012	2.2		W3C Community	Contributor	License	Agreement (CLA)	high	(15 Feb 2018)	21	bit.ly/2J75JPj
	LegalRuleML	Legal Rule	Modeling	Language	cross-domains	modelling of legal	norms allowing	legal reasoning	2011	1.0		OASIS	Intellectual	Property Rights	Policy	high	(8 May 2018)	56	bit.ly/2sxpskV
	ccREL	Creative	Commons Rights	Expression	Language	licences	machine-readable	standard to	express licensing	terms	2008	unique version			CC BY 3.0	low (1 May 2008)	190	bit.ly/2Lua4gp
	Eurovoc		European	Vocabulary		cross-domains	indexing of the	documentary	information of the	EU institutions	1984	4.9	commercial or	non-commercial	use allowed	providing	appropriate	acknowledgement	high	(29 Mar 2019)	280	bit.ly/2MY0TpM
					Extended name			Legal domain				Purpose			Year	Current version				Licence	Updates	frequency	Number of	references	Link

Table 3 :

 3 Classification of ontologies published from 2014 to 2015 according to the general information class of features. Last revision of the information contained in the table: May 2019.

	PrOnto		Privacy Ontology		privacy	representation of	the GDPR	concepts for legal	reasoning and	compliance	checking	2018	not yet released	not applicable	not applicable	5	not applicable
	PrivOnto		not found		privacy		representation of	normative	requirements and	rules as LOD		2017	unique version	not found	not found	20	not found
	NRV	Normative	Requirements	Vocabulary	cross-domains		representation of	annotated privacy	policies of	websites		2017	unique version	not found	law (last update	not found)	6	bit.ly/2KFwkIC
	GDPRtEXT	GDPR text	extensions	privacy	representation of	the GDPR	concepts with a	direct link to the	Regulation text	2017	0.6	CC by 4.0	high	(31 July 2018)	10	bit.ly/2xwjTZJ
				Extended name		Legal domain						Purpose					Year	Current version	Licence	Updates	frequency	Number of	references	Link

Table 4 :

 4 Classification of ontologies published from 2017 to 2018 according to the general information class of features. Last revision of the information contained in the table: May 2019.

Table 5 :

 5 Classification of ontologies published from 1984 to 2013 according to the modelling information class of features. Last revision of the information contained in the table: May 2019.

	L4LOD	not found	manual		English			not found		not found	none		lightly structured		RDF		class level		not found	not found
	ODRL	not found	manual		English			not applicable		Dublin Core,	SKOS, FOAF	none		highly structured		RDF		class level,	property level		not found	not found
	LegalRuleML	not found	manual		English			not applicable		not found	RuleML		highly structured	RelaxNG and	XML Schema,	RDFS, XSLT	class level,	property level	container,	collection,	recursive element,	marker,	composite	not found
	ccREL	not found	manual		English			not found		not found	none		lightly structured		RDF		class level,	property level		not found	not found
		not found	manual	EUs languages,	Macedonian,	Albanian, Serbian	ECLAS thesaurus,	SCAD, EC-01,	O cial Gazette	indices	FRBR, Dublin	Core, SKOS	none		lightly structured		RDF		not found		not found	EU institutions,	Publication O ce,	national and	regional	parliaments
		Development	Construction		Language			KS for terms	extraction			EV references	Ground	ontology	Level of	structure	KR formalism			Axioms		Design patterns	Evaluation

Table 6 :

 6 Classification of ontologies published from 2014 to 2015 according to the modelling information class of features. Last revision of the information contained in the table: May 2019.

	PrOnto	bottom-up	following the	MeLOn	methodology	manual	English	GDPR, terms of	use, information,	privacy policies,	consent forms	ALLOT, FRBR,	LKIF Core,	PWO,	LegalRuleML	metamodel	none		highly structured	OWL	class level	Time-indexed	Value in Context,	Time interval	SPARQL queries
	PrivOnto		middle-out		manual	English	115 privacy	policies of	US-based	companies			not found			none		lightly structured	OWL	class level	not found	not found
	NRV		not found		manual	English				not applicable					LegalRulemML,	RuleML		LegalRuleML		moderately	structured	RDF	class property	not found	SPARQL queries
	GDPRtEXT		bottom-up		manual	English	GDPR, document	issued by o cial	sources,	industry-based	sources			ELI ontology			ELI ontology		lightly structured	OWL	class level	not found	not found
				Development		Construction	Language				KS for terms		extraction						EV references			Ground	ontology	Level of	structure	KR formalism	Axioms	Design patterns	Evaluation

Table 7 :

 7 Classification of ontologies published from 2017 to 2018 according to the modelling information class of features. Last revision of the information contained in the table: May 2019.

Table 8 :

 8 10. Classification of ontologies published from 1984 to 2013 according to the semantic information class of features. Last revision of the information contained in the table: May 2019.

	L4LOD				not found				prescriptive rules					permissions,	obligations and	prohibitions over	the licensed data
	ODRL	modelling of the	date and time a	policy is issued or	modified. Date	and time	constraint on the	validity of a	deontic operator.	prescriptive rules					permissions,	prohibitions and	obligations over a	digital or material	asset
	LegalRuleML		modelling of the	aspects of a rule	that vary over	time (e.g., status,	validity,	jurisdiction)	constitutive,	technical and	prescriptive rules	permission,	rights, obligation,	prohibition,	compliance with a	prohibition or an	obligation,	violation of a	prohibition or an	obligation,	reparation of a	violation
	ccREL				not found				prescriptive rules					requirements and	prohibitions set	by the Creative	Commons	standard
	Eurovoc				not applicable				not applicable								not applicable
					Modelling of	temporal	aspects		Adopted	normative	model							Deontic logic		model

Table 9 :

 9 Classification of ontologies published from 2014 to 2015 according to the semantic information class of features. Last revision of the information contained in the table: May 2019.

	PrOnto		temporal intervals	associated to	actions in	workflows, to	agents' roles and	to deontic	operators		constitutive and	prescriptive rules	permissions,	prohibitions,	obligations,	rights, compliance	with an obligation	and violation of	an obligation.	Some references	are modelled	between	obligations and	compli-	ance/violation	and between	rights and	permissions
	PrivOnto							not applicable							prescriptive rules										not applicable
	NRV	temporal aspects	are modelled	through the	concepts of	perdurance,	persistence,	co-occurrence and	preemptiveness of	a deontic operator	prescriptive rules			permissions,	obligations and	prohibition are	organised	according to the	principles of	compensation,	compliance,	violation,	temporal validity	and realisation
	GDPRtEXT		information about	personal data	retention and	storage period are	modelled as	ontology classes		prescriptive rules				obligations of	di↵erent agents	mentioned in the	GDPR	(Controller,	Processor, Data	Protection	O cer) and right	of the data	subject
							Modelling of		temporal		aspects					Adopted norms	model										Deontic logic	model

Table 10 :

 10 Classification of ontologies published from 2017 to 2018 according to the semantic information class of features. Last revision of the information contained in the table: May 2019.

https://www.w3.org/TR/odrl-vocab/

https://www.w3.org/community/odrl/

http://oeg-dev.dia.fi.upm.es/licensius/static/ldr/

http://www.oeg-upm.net/

https://www.w3.org/Submission/ccREL/

https://creativecommons.org/

http://ns.inria.fr/l4lod/v2/l4lod_v2.html

https://code.google.com/archive/p/loted2/source

https://ted.europa.eu/TED/main/HomePage.do

http://contsem.unizar.es/def/sector-publico/pproc.html

https://bitbucket.org/guerret/lu.uni.eclipse.bpmn2/src/ 3ca749d36cf193b9af8808c0fdf24858cdfeb21e/resources/dataprotection-rdf.owl? at=master&fileviewer=file-view-default

https://openscience.adaptcentre.ie/ontologies/GDPRtEXT/deliverables/docs/ index-en.html

https://www.usableprivacy.org/

https://github.com/dapreco/daprecokb/blob/master/gdpr/rioKB_GDPR.xml

https://publications.europa.eu/en/web/eu-vocabularies/th-dataset/-/ resource/dataset/eurovoc

https://scholar.google.it/

The authors have received funding from EU Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 690974 (MIREL).