<figure><img src="image_5.png" /><figcaption>**Fig. 1** Middle-out approach for building CriMOnto</figcaption></figure>

### 5.5.1 Criminal modular domain ontology

After applying the proposed middle-out approach, a criminal modular domain ontology, named CriMOnto is obtained. The proposed approach is defined by developing the modules independently and then combining them together to compose the whole CriMOnto (see Fig. 1) (Ghosh et al. 2016). From this perspective, the different modules can be considered different subjects since they are at different conceptual levels. Therefore, an integration process is performed to combine them (Bontas et al. 2005).

**Upper ontology module** The UOM consists of abstract concepts and relations which are effectively independent of any specific domain (see Fig. 2). For a principled development of this module, the unified foundational ontology UFO, proposed by Guizzardi and Wagner (2010), is partially reused to facilitate and speed up the ontology development process by avoiding reinvention of the wheel concerning basic categories (Rosa et al. 2012). Therefore, UFO permits the building of an ontology reusing some generic concepts such as *Category*, *Kind*, *Subkind*, *Relator*, *Role*, *Role_Mixin* and *Event*, where the ontologist does not need to rebuild these concepts. In order to make possible the activity of conceptual modeling via UFO, a conceptual modeling language, named OntoUML (Guerson et al. 2015), is used. OntoUML uses the ontological constraints of UFO as modeling primitives and is specified above the UML 2.0 meta-model.<fnref n="20" />

**Core ontology module** The COM consists of concepts and relations that are common across the domains of law and can provide the basis for specialization into domain and domain-specific concepts. The same perspective is applied, as for the *upper* module, for reusing partially the legal core ontology, LKIF-Core,<fnref n="21" /> to build this module (see Fig. 3).

<footnote n="20">²⁰ http://code.google.com/p/ontouml-lightweight-editor/, last accessed 19/4/2017.</footnote>

<footnote n="21">²¹ https://github.com/RinkeHoekstra/lkif-core.</footnote>
<figure><img src="image_6.png" /><figcaption>Fig. 2 Fragment of the upper module in OntoUML</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 3 Excerpt of the Core Module in Protégé</figcaption></figure>

**Domain ontology module** The DOM is composed of categories that are related mainly to the criminal domain in general such as *Criminal_Act*, *Penalty*, *Misdemeanor*, *Violation*, etc. In order to build this module, two main strategies are applied: (1) specialize the concepts and relations of the core module (Fig. 4a); (2) extract the knowledge from textual resources using ontology learning and NLP techniques (El Ghosh et al. 2017) (Fig. 4b).

**Domain-specific ontology module** The DSOM consists of concepts and relations of a specific subject domain such as the Lebanese criminal system (see Fig. 5). The bottom-up strategy helped to generate semi-automatically the *domain-specific* ontology. Unfortunately, the generated results were inexpressive and thus
<figure><img src="image_6.png" /><figcaption>Fig. 2 Fragment of the upper module in OntoUML</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 3 Excerpt of the Core Module in Protégé</figcaption></figure>

**Domain ontology module** The DOM is composed of categories that are related mainly to the criminal domain in general such as *Criminal_Act*, *Penalty*, *Misdemeanor*, *Violation*, etc. In order to build this module, two main strategies are applied: (1) specialize the concepts and relations of the core module (Fig. 4a); (2) extract the knowledge from textual resources using ontology learning and NLP techniques (El Ghosh et al. 2017) (Fig. 4b).

**Domain-specific ontology module** The DSOM consists of concepts and relations of a specific subject domain such as the Lebanese criminal system (see Fig. 5). The bottom-up strategy helped to generate semi-automatically the *domain-specific* ontology. Unfortunately, the generated results were inexpressive and thus
<figure><img src="image_8.png" /><figcaption>Fig. 4 Excerpt of the Domain Module in Protégé</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 5 Excerpt of the Domain-specific Module in Protégé</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Fig. 6 Hierarchical Mapping in Protégé</figcaption></figure>

insufficient for practical use. For this reason, a re-engineering process, inspired by the work of Caldarola et al. (2015), was applied to correct, prune and enrich the extracted ontology and make it more expressive by transforming it to heavyweight or axiom-based ontology.
<figure><img src="image_8.png" /><figcaption>**Fig. 4** Excerpt of the Domain Module in Protégé</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 5** Excerpt of the Domain-specific Module in Protégé</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>**Fig. 6** Hierarchical Mapping in Protégé</figcaption></figure>

insufficient for practical use. For this reason, a re-engineering process, inspired by the work of Caldarola et al. (2015), was applied to correct, prune and enrich the extracted ontology and make it more expressive by transforming it to heavyweight or axiom-based ontology.
*Integration process of ontology modules* The integration phase is the final step after building the independent ontology modules to form the resulting ontology CriMOnto. The source ontologies or modules (*upper*, *core*, *domain* and *domain-specific*) are combined and assembled together. In this context, a simple vertical mapping strategy is applied between the concepts of the different modules. In our experiments, since ontology modules are located on vertical conceptual levels from general (*upper* module) to specific (*domain-specific* module), the mappings will be based mainly on a parent–child hierarchical relationship (Legat et al. 2014). Finally, all the modules are imported into the final ontology using the OWL:imports formalisms (see Fig. 6).

### 5.5.2 Rule-base of the legal reasoning model

The Rule base of the legal reasoning model of the decision support system stores the knowledge in form of rules. In the legal domain, a legal norm is represented by an obligation rule that denotes that the conclusion of the rule will be treated as an obligation in the following form:

IF condition (*operativefacts*) THEN conclusion (legal effect).

For representing the norms of the Lebanese criminal code, a modeling process is needed, as well as a rule language. The modeling process is based on a homogeneous integration of the obtained criminal domain ontology and SWRL selected as a rule language. In SWRL, rules are of the form of an implication between an antecedent (body) conjunction and a consequent (head) conjunction in the following form (Antoniou et al. 2005):

<table><caption>**Table 1** Legal norms and rules expressed in SWRL</caption><tr><td>Article<br/>547</td><td>Anyone who intentionally kills another person shall be punishable by hard labour for a term of between 15 and 20 years</td><td>Intentional_Homicide(killing),<br/>committed_towards(killing,?y),<br/>committed_by(killing, ?x)<br/>⇒<br/>is_punished_by(?x, hard_labour),<br/>imposed_for,maximum(hard_labour,max_d_2),<br/>imposed_forMinimum(hard_labour, min_d_2),<br/>term_value(max_d_2, 20),<br/>term_value(min_d_2, 15),<br/>term_type(max_d_2, "years"),<br/>term_type(min_d_2, "years")</td></tr><tr><td>Article<br/>213</td><td>An accomplice to an offence shall be liable to the penalty prescribed by law for the offence</td><td>Accomplice(?x),<br/>commit(?x, ?y),<br/>is_punishable_by(?y, ?z)<br/>⇒<br/>is_liable_to_punished_by(?x, ?z)</td></tr></table>
*Integration process of ontology modules* The integration phase is the final step after building the independent ontology modules to form the resulting ontology CriMOnto. The source ontologies or modules (*upper*, *core*, *domain* and *domain-specific*) are combined and assembled together. In this context, a simple vertical mapping strategy is applied between the concepts of the different modules. In our experiments, since ontology modules are located on vertical conceptual levels from general (*upper* module) to specific (*domain-specific* module), the mappings will be based mainly on a parent–child hierarchical relationship (Legat et al. 2014). Finally, all the modules are imported into the final ontology using the OWL:imports formalisms (see Fig. 6).

### 5.5.2 Rule-base of the legal reasoning model

The Rule base of the legal reasoning model of the decision support system stores the knowledge in form of rules. In the legal domain, a legal norm is represented by an obligation rule that denotes that the conclusion of the rule will be treated as an obligation in the following form:

IF condition (*operativefacts*) THEN conclusion (legal effect).

For representing the norms of the Lebanese criminal code, a modeling process is needed, as well as a rule language. The modeling process is based on a homogeneous integration of the obtained criminal domain ontology and SWRL selected as a rule language. In SWRL, rules are of the form of an implication between an antecedent (body) conjunction and a consequent (head) conjunction in the following form (Antoniou et al. 2005):

<table><caption>**Table 1** Legal norms and rules expressed in SWRL</caption><tr><td>Article<br/>547</td><td>Anyone who intentionally kills another person shall be punishable by hard labour for a term of between 15 and 20 years</td><td>Intentional_Homicide(killing),<br/>committed_towards(killing,?y),<br/>committed_by(killing, ?x)<br/>⇒<br/>is_punished_by(?x, hard_labour),<br/>imposed_for,maximum(hard_labour,max_d_2),<br/>imposed_forMinimum(hard_labour, min_d_2),<br/>term_value(max_d_2, 20),<br/>term_value(min_d_2, 15),<br/>term_type(max_d_2, "years"),<br/>term_type(min_d_2, "years")</td></tr><tr><td>Article<br/>213</td><td>An accomplice to an offence shall be liable to the penalty prescribed by law for the offence</td><td>Accomplice(?x),<br/>commit(?x, ?y),<br/>is_punishable_by(?y, ?z)<br/>⇒<br/>is_liable_to_punished_by(?x, ?z)</td></tr></table>
$$a_1 \wedge a_2 \wedge \dotsb \wedge a_n \Rightarrow b_1 \wedge b_2 \wedge \dotsb \wedge b_n;$$

By representing legal norms using SWRL, we assume that they are conflict free and will be complied with. In Table 1, we give two examples of SWRL rules, expressed using CriMOnto elements.<fnref n="22" />

## 6 Robert van Doesburg: FLINT, a formal method for the interpretation of sources of norms

This research investigates a standardized method for expressing the interpretation of sources of norms in natural language in a formal representation. The representation formalism is machine readable and our method results in executable expressions. We developed a domain specific language (DSL), called FLINT (Formal Language for the Interpretation of sources of norms). In this section, we give an overview of the foundations of FLINT and explain the value of FLINT compared to existing methods for legal knowledge engineering.

Major concerns of the field of Artificial Intelligence (AI) and Law include legal knowledge acquisition and the use of that knowledge by agents to achieve goals. To be able to perform in this field, agents need knowledge of the semantics of sources of norms that are supposed to regulate their actions. In order to have a computational set of norms, the interpretation of sources of norms must result in formal representations that can be used by a reasoner. Because norms are made by humans and, mostly, expressed in (written) natural language, AI and Law is also about the border between essentially human tasks, and tasks that can be performed by non-human agents. In our research, we address that border.<fnref n="23" />

*Engineering challenge* Create a standardized method for expressing the interpretation of sources of norms in natural language into a formal representation in a machine readable and executable form.

*Research questions* To meet the engineering challenge, research questions are formulated, some of which are listed below:

1. Why do existing methods for legal engineering pay so little attention to the explicit interpretation of sources of norms?

2. What knowledge should a normative reasoner have to be able to determine the behavioral context of all agents in a regulated system?

3. How can the knowledge needed by a normative reasoner be systematically acquired from sources of norms in natural language?

4. How can we handle differences in the interpretation of sources of norms caused, e.g., by the inherent ambiguity of statements in natural language?

<footnote n="22">²² This work has been supported by the European Union with the European Regional Development Fund (ERDF) under Grant Agreement n°HN0002134 in the project CLASSE 2 (“Les Corridors Logistiques: Application a la Vallée de la Seine et son Environnement”), the Lebanese University and the National Support from the National Council for Scientific Research in Lebanon (CNRS).</footnote>

<footnote n="23">²³ Robert van Doesburg is a Ph.D. student at the Leibniz Center for Law, University of Amsterdam, under the supervision of Tom van Engers. The research project is part of a research program on large-scale normative reasoning at the Leibniz Center of the University of Amsterdam.</footnote>
$$a_1 \wedge a_2 \wedge \dotsb \wedge a_n \Rightarrow b_1 \wedge b_2 \wedge \dotsb \wedge b_n;$$

By representing legal norms using SWRL, we assume that they are conflict free and will be complied with. In Table 1, we give two examples of SWRL rules, expressed using CriMOnto elements.<fnref n="22" />

## 6 Robert van Doesburg: FLINT, a formal method for the interpretation of sources of norms

This research investigates a standardized method for expressing the interpretation of sources of norms in natural language in a formal representation. The representation formalism is machine readable and our method results in executable expressions. We developed a domain specific language (DSL), called FLINT (Formal Language for the INTERpretation of sources of norms). In this section, we give an overview of the foundations of FLINT and explain the value of FLINT compared to existing methods for legal knowledge engineering.

Major concerns of the field of Artificial Intelligence (AI) and Law include legal knowledge acquisition and the use of that knowledge by agents to achieve goals. To be able to perform in this field, agents need knowledge of the semantics of sources of norms that are supposed to regulate their actions. In order to have a computational set of norms, the interpretation of sources of norms must result in formal representations that can be used by a reasoner. Because norms are made by humans and, mostly, expressed in (written) natural language, AI and Law is also about the border between essentially human tasks, and tasks that can be performed by non-human agents. In our research, we address that border.<fnref n="23" />

*Engineering challenge* Create a standardized method for expressing the interpretation of sources of norms in natural language into a formal representation in a machine readable and executable form.

*Research questions* To meet the engineering challenge, research questions are formulated, some of which are listed below:

1. Why do existing methods for legal engineering pay so little attention to the explicit interpretation of sources of norms?

2. What knowledge should a normative reasoner have to be able to determine the behavioral context of all agents in a regulated system?

3. How can the knowledge needed by a normative reasoner be systematically acquired from sources of norms in natural language?

4. How can we handle differences in the interpretation of sources of norms caused, e.g., by the inherent ambiguity of statements in natural language?

<footnote n="22">²² This work has been supported by the European Union with the European Regional Development Fund (ERDF) under Grant Agreement n°HN0002134 in the project CLASSE 2 ("Les Corridors Logistiques: Application à la Vallée de la Seine et son Environnement"), the Lebanese University and the National Support from the National Council for Scientific Research in Lebanon (CNRS).</footnote>

<footnote n="23">²³ Robert van Doesburg is a Ph.D. student at the Leibniz Center for Law, University of Amsterdam, under the supervision of Tom van Engers. The research project is part of a research program on large-scale normative reasoning at the Leibniz Center of the University of Amsterdam.</footnote>
In this section, we will focus on the creation of a standardized method for the formal
interpretation of sources of norms in natural language. After a short overview of the
work that FLINT is founded upon: i.e. the construction of social reality; Hohfeld’s
fundamental legal concepts; approaches for the acquisition, representation and use
of legal knowledge; and argumentation theory, we will give a short presentation of
our work on the formal interpretation of sources of norms in natural language.
Finally we will discuss the results of our work until now and the work that needs to
be done to meet our engineering challenge.

**6.1 On the representation of norms**

6.1.1 *The construction of social reality*

Norms only exist by human agreement. If people do not agree on the meaning of a
norm, they cannot all obey it in the same way. Enforcement of a norm presupposes
that the enforcers of that norm agree on its meaning. Searle (1995) explores the
possibility of a social reality. Can we construct an objective world of money,
property, marriage, government, elections, football games, and so on and so forth?
In short: is an objective world of norms and law possible?

An objective social reality is, according to Searle, built upon the following
foundations:

1. The existence of a real world, an external reality to which facts correspond (Hume 1739; Russell 1912).

2. Brute facts (Anscombe 1958), e.g. there is snow and ice near the summit of Mount Everest.

3. Social facts (Durkheim 1894), e.g. a pack of hyena's attacking a lion.

4. Institutional facts (Austin 1975), e.g., this piece of paper counts as money.

Like external reality, brute facts and social facts exist independently of human
representations. Institutional facts, however, cannot exist without human represen-
tation. The hyena's will attack the lion without any person making a representation
of it, but the concept of money cannot exist without some kind of human
interpretation of it. At the same time institutional facts presuppose brute facts in a
real world. Without some kind of brute facts there can be no exchange of knowledge
of institutional facts between humans. Van Engers and Boer (2011) applied the idea
of constructing social reality to the world of norms and law. They addressed the
interaction between sources of norms (the brute facts a norm is based upon), the
logical representations of the meaning of these sources (the institutional represen-
tation derived from the source, i.e. the interpretation of the source), and the
application of that interpretation to an individual case, or to a set of cases, using an
implementation model (a social reality). The theory of constructing social reality
enables us to separately discuss the existence of sources of norms, the interpretation
thereof, the social reality where this interpretation is applied, and the mapping of
social facts to an institutional reality. This separation of concerns reduces the
complexity of the task of creating a formal, computational representation of norms.
In this section, we will focus on the creation of a standardized method for the formal interpretation of sources of norms in natural language. After a short overview of the work that FLINT is founded upon: i.e. the construction of social reality; Hohfeld’s fundamental legal concepts; approaches for the acquisition, representation and use of legal knowledge; and argumentation theory, we will give a short presentation of our work on the formal interpretation of sources of norms in natural language. Finally we will discuss the results of our work until now and the work that needs to be done to meet our engineering challenge.

# 6.1 On the representation of norms

## 6.1.1 *The construction of social reality*

Norms only exist by human agreement. If people do not agree on the meaning of a norm, they cannot all obey it in the same way. Enforcement of a norm presupposes that the enforcers of that norm agree on its meaning. Searle (1995) explores the possibility of a social reality. Can we construct an objective world of money, property, marriage, government, elections, football games, and so on and so forth? In short: is an objective world of norms and law possible?

An objective social reality is, according to Searle, built upon the following foundations:

1. The existence of a real world, an external reality to which facts correspond (Hume 1739; Russell 1912).

2. Brute facts (Anscombe 1958), e.g. there is snow and ice near the summit of Mount Everest.

3. Social facts (Durkheim 1894), e.g. a pack of hyena's attacking a lion.

4. Institutional facts (Austin 1975), e.g., this piece of paper counts as money.

Like external reality, brute facts and social facts exist independently of human representations. Institutional facts, however, cannot exist without human representation. The hyena's will attack the lion without any person making a representation of it, but the concept of money cannot exist without some kind of human interpretation of it. At the same time institutional facts presuppose brute facts in a real world. Without some kind of brute facts there can be no exchange of knowledge of institutional facts between humans. Van Engers and Boer (2011) applied the idea of constructing social reality to the world of norms and law. They addressed the interaction between sources of norms (the brute facts a norm is based upon), the logical representations of the meaning of these sources (the institutional representation derived from the source, i.e. the interpretation of the source), and the application of that interpretation to an individual case, or to a set of cases, using an implementation model (a social reality). The theory of constructing social reality enables us to separately discuss the existence of sources of norms, the interpretation thereof, the social reality where this interpretation is applied, and the mapping of social facts to an institutional reality. This separation of concerns reduces the complexity of the task of creating a formal, computational representation of norms.
## 6.2 Fundamental legal concepts

Hohfeld introduced his fundamental legal concepts in 1913 (Hohfeld 1917). The motive to introduce these legal concepts was Hohfeld’s opinion that one of the greatest hindrances to the clear understanding of legal problems is the explicit or tacit assumption that all legal relations may be reduced to *rights* and *duties*. Hohfeld argued this was not the case by describing the ambiguities in the meaning of these concepts and went on to introduce the smallest set of legal conceptions to which, according to him, any and all *legal quantities* could be reduced. Hohfeld distinguished four types of legal relations:

1. *Power/Liability* relations;

2. *Immunity/Disability* relations;

3. *Right/Duty* relations;

4. *Privilege/No-right* relations.

Some scholars prefer the term *Liberty* instead of *Privilege* and *Claim* instead of *Right*. We do too. The Hohfeldian legal conceptions can only exist in pairs and describe relations between two people, each holding one of the rights in a pair.

The *Claim/Duty* relation describes the situation in which *person x* has a claim on *person y* to perform *action A*, and *person y* having the duty to perform *action A*. The *Liberty/No-claim* relation describes the situation where that claim and that duty are absent. A *Claim/Duty* relation can be created or by exercising the power of *person z* to perform *action B*. *Person y* will have a liability as the result of that action. *Person x* and *person z* may be the same person. In a normative (or legal) relation *person x* and *person y* must be different persons. The *Immunity/Disability* relation is a relation in which the power of *person z* to perform *action B* is absent. *Person z* has a normative, e.g. legal, disability to perform *action B*.

Hohfeld called the *Claim/Duty* relation and *Privilege/No-claim* relation the first order relations. The *Power/Liability* relation and *Immunity/Disability* relation are called second order relations, because they can change first order relations. We call the *Claim/Duty* relation situational, because it describes a situation that will exist forever unless it is terminated by using a *Power/Liability* relation. A new situation, and thus a new *Claim/Duty* relation, can only come to existence as a result of performing an action that is part of a *Power/Liability* relation. This perspective allows for a systemic view on Hohfeldian relations. *Immunity/Disability* and *Liberty/No-claim* relations allow us to argue about the existence of *Claim/Duty* or *Power/Liability* relations in disputes, e.g. in a court of law.

Hohfeld’s fundamental legal concepts have been formalized by several scholars. An example that makes references to a lot of other formalizations is Sartor (2006), see also Sect. 3. A legal philosophical discussion of the nature of rights in relation to the fundamental legal conceptions of Hohfeld, can be found in Wenar (2005).
## 6.2 Fundamental legal concepts

Hohfeld introduced his fundamental legal concepts in 1913 (Hohfeld 1917). The motive to introduce these legal concepts was Hohfeld’s opinion that one of the greatest hindrances to the clear understanding of legal problems is the explicit or tacit assumption that all legal relations may be reduced to *rights* and *duties*. Hohfeld argued this was not the case by describing the ambiguities in the meaning of these concepts and went on to introduce the smallest set of legal conceptions to which, according to him, any and all *legal quantities* could be reduced. Hohfeld distinguished four types of legal relations:

1. *Power/Liability* relations;

2. *Immunity/Disability* relations;

3. *Right/Duty* relations;

4. *Privilege/No-right* relations.

Some scholars prefer the term *Liberty* instead of *Privilege* and *Claim* instead of *Right*. We do too. The Hohfeldian legal conceptions can only exist in pairs and describe relations between two people, each holding one of the rights in a pair.

The *Claim/Duty* relation describes the situation in which *person x* has a claim on *person y* to perform *action A*, and *person y* having the duty to perform *action A*. The *Liberty/No-claim* relation describes the situation where that claim and that duty are absent. A *Claim/Duty* relation can be created or by exercising the power of *person z* to perform *action B*. *Person y* will have a liability as the result of that action. *Person x* and *person z* may be the same person. In a normative (or legal) relation *person x* and *person y* must be different persons. The *Immunity/Disability* relation is a relation in which the power of *person z* to perform *action B* is absent. *Person z* has a normative, e.g. legal, disability to perform *action B*.

Hohfeld called the *Claim/Duty* relation and *Privilege/No-claim* relation the first order relations. The *Power/Liability* relation and *Immunity/Disability* relation are called second order relations, because they can change first order relations. We call the *Claim/Duty* relation situational, because it describes a situation that will exist forever unless it is terminated by using a *Power/Liability* relation. A new situation, and thus a new *Claim/Duty* relation, can only come to existence as a result of performing an action that is part of a *Power/Liability* relation. This perspective allows for a systemic view on Hohfeldian relations. *Immunity/Disability* and *Liberty/No-claim* relations allow us to argue about the existence of *Claim/Duty* or *Power/Liability* relations in disputes, e.g. in a court of law.

Hohfeld’s fundamental legal concepts have been formalized by several scholars. An example that makes references to a lot of other formalizations is Sartor (2006), see also Sect. 3. A legal philosophical discussion of the nature of rights in relation to the fundamental legal conceptions of Hohfeld, can be found in Wenar (2005).
## 6.3 An engineer's perspective on legal knowledge

Valente (1995) distinguished three types of approaches for legal knowledge engineering: the *rule-based approach*, the *case-based approach* and the *logic approach*, and argues for a fourth: the *modeling approach*. The main focus of his work on this modeling approach, is on functional ontologies. Also Van Kralingen (1995) introduced the *frame-based approach*, which can be seen as an example of such a modeling approach. All of the approaches described by Valente, provide the knowledge engineer with a specific perspective, that comes with an ontological and epistemological framework, on original sources of norms. Below we give a short description of the characteristics of the four approaches.

**The rule-based approach** The main assumption behind the rule-based approach is that legislation can be represented as production rules, e.g. having the form of *if A, then B* or *if A, then add B*. These production rules can be used in two ways in AI and Law: as a model of sources of norms and, when combined with a reasoning engine, as an implementation device. Production rules are a shallow representation device for law, because they do not express important epistemological distinctions in law. According to Valente (1995) the result of this rule-based approach is that a large part of the systems developed are highly pragmatic and ad-hoc. Deficiencies come from the absence of any theoretical foundation and the use of an oversimplified development strategy. Since 1995 there has been some progress on this subject, but the interpretation of sources of norms in natural language remains problematic, see also De Maat et al. (2009) and Wyner et al. (2016). The rule-based approach is suitable for developing executable models of a given set of formalized norms. The approach has no solution for extracting formalized rules from of sources of norms in natural language.

**The case-based approach** The case-based approach represents laws merely as cases, and uses case-based reasoning techniques to reason with them. This is done by analogical reasoning, or rather by solving actual problems (cases) by retrieving similar past cases and using aspects of these retrieved cases to solve the case at hand. The classical example of case-based reasoning used in law is HYPO (Ashley 1991; Bench-Capon 2017). The process of case-based reasoning mimics the behavior of trial lawyers in the USA, and has been successfully used to model cases in this context (Valente 1995). While researchers working on the case-based approach have suggested methods to extract legal knowledge from cases, no solution has yet been proposed for the extraction of formal legal knowledge from statute law, regulations and other non case-centered sources of norms.

**The logic approach** The truth-based, or logic approach, is an important and pervasive approach for Legal Knowledge Engineering. Logic and truth maintenance systems have been the first formalizations that could run on digital computers, thanks to the fundamental work of Shannon (1948). Logic has been used for reasoning (modeling inferences, implementing them and analyzing their com- putability and complexity), justification (is this reasoning valid?), and representation (domain representation and representing ontological commitments). Logic is also often used in the rule-based and case-based approaches. In the process of interpreting sources of norms in natural language, however, logic has its downsides.
## 6.3 An engineer's perspective on legal knowledge

Valente (1995) distinguished three types of approaches for legal knowledge engineering: the *rule-based approach*, the *case-based approach* and the *logic approach*, and argues for a fourth: the *modeling approach*. The main focus of his work on this modeling approach, is on functional ontologies. Also Van Kralingen (1995) introduced the *frame-based approach*, which can be seen as an example of such a modeling approach. All of the approaches described by Valente, provide the knowledge engineer with a specific perspective, that comes with an ontological and epistemological framework, on original sources of norms. Below we give a short description of the characteristics of the four approaches.

**The rule-based approach** The main assumption behind the rule-based approach is that legislation can be represented as production rules, e.g. having the form of *if A, then B* or *if A, then add B*. These production rules can be used in two ways in AI and Law: as a model of sources of norms and, when combined with a reasoning engine, as an implementation device. Production rules are a shallow representation device for law, because they do not express important epistemological distinctions in law. According to Valente (1995) the result of this rule-based approach is that a large part of the systems developed are highly pragmatic and ad-hoc. Deficiencies come from the absence of any theoretical foundation and the use of an oversimplified development strategy. Since 1995 there has been some progress on this subject, but the interpretation of sources of norms in natural language remains problematic, see also De Maat et al. (2009) and Wyner et al. (2016). The rule-based approach is suitable for developing executable models of a given set of formalized norms. The approach has no solution for extracting formalized rules from of sources of norms in natural language.

**The case-based approach** The case-based approach represents laws merely as cases, and uses case-based reasoning techniques to reason with them. This is done by analogical reasoning, or rather by solving actual problems (cases) by retrieving similar past cases and using aspects of these retrieved cases to solve the case at hand. The classical example of case-based reasoning used in law is HYPO (Ashley 1991; Bench-Capon 2017). The process of case-based reasoning mimics the behavior of trial lawyers in the USA, and has been successfully used to model cases in this context (Valente 1995). While researchers working on the case-based approach have suggested methods to extract legal knowledge from cases, no solution has yet been proposed for the extraction of formal legal knowledge from statute law, regulations and other non case-centered sources of norms.

**The logic approach** The logic-based, or logic approach, is an important and pervasive approach for Legal Knowledge Engineering. Logic and truth maintenance systems have been the first formalizations that could run on digital computers, thanks to the fundamental work of Shannon (1948). Logic has been used for reasoning (modeling inferences, implementing them and analyzing their com- putability and complexity), justification (is this reasoning valid?), and representation (domain representation and representing ontological commitments). Logic is also often used in the rule-based and case-based approaches. In the process of interpreting sources of norms in natural language, however, logic has its downsides.
The most important of these is that domain experts in the legal field are unfamiliar with logical formulas, especially with the complex non-standard approaches that are being used to represent normative systems (Valente 1995). Furthermore, deontic logic exhibits paradoxes arising from the characteristics of the language (Sileno et al. 2017).

*The frame-based approach* One of the founding fathers of AI, Marvin Minsky, introduced the term *frame* as a concept essential for knowledge representation.

Van Kralingen (1995) suggested to use a frame-based approach for legal knowledge engineering. He focused on knowledge capture and claims that frames are inherently suited for making interpretations of sources in natural language. How these frames can be used for reasoning was left to a companion thesis by Visser (1995). Van Kralingen explicitly writes in his thesis that he is not paying attention to problems associated with the interpretation of legal knowledge (Van Kralingen 1995), since this was Visser's role in the project. Visser's contribution with respect to the interpretation of sources however, remains limited to a few remarks on the interpretation of vague concepts, e.g. *suitable employment*. He suggests to consult court decisions to clarify vague concepts, using a method that can be characterized as mapping the vague concept to a multi-dimensional space, and to use a calculation method to decide if an instance fits the category borders. This looks very much like the HYPO model (Ashley 1991), but then only to reason on the semantics of vague terms. We conclude that both Visser and Van Kralingen left underexposed the issue of how to make the transcription of the source of a norm in natural language into a normative frame. Breaux (2009) made his own frame for analyzing of sources of norms, without referring to the work of Van Kralingen. With his approach, Breaux aimed to support knowledge engineers in building legal reasoning systems and consequently named his approach a Frame-Based Requirements Analysis Method. The frame-based approaches of Van Kralingen and Breaux use their frames to assign text fragments from sources of norms to slots in the frames. If a specific frame element cannot be found in a sentence, or set of sentences, then that frame element remains empty. Also, both approaches have the problem that actions do not necessarily result in a state-transition. Van Kralingen's frames allows for actions that lack a final state. These actions are classified as acts of the type *state-of-affairs*. Breaux' frame-base lacks a slot for a final state or postcondition. We consider it essential that every action results in a next well-defined final state, or postcondition. If the result of a normative action cannot be found explicitly in a source, the source sentence is not considered to contain a valid FLINT act type frame. The only possibility to repair the deficit of the missing result is by making an explicit statement that:

1. the source sentence implies a specific result,

2. the result of the action can be found in a separate sentence which should be interpreted in conjunction with it.

By making these statements explicit, the interpretations are open to be challenged by alternatives.
The most important of these is that domain experts in the legal field are unfamiliar with logical formulas, especially with the complex non-standard approaches that are being used to represent normative systems (Valente 1995). Furthermore, deontic logic exhibits paradoxes arising from the characteristics of the language (Sileno et al. 2017).

*The frame-based approach* One of the founding fathers of AI, Marvin Minsky, introduced the term *frame* as a concept essential for knowledge representation.

Van Kralingen (1995) suggested to use a frame-based approach for legal knowledge engineering. He focused on knowledge capture and claims that frames are inherently suited for making interpretations of sources in natural language. How these frames can be used for reasoning was left to a companion thesis by Visser (1995). Van Kralingen explicitly writes in his thesis that he is not paying attention to problems associated with the interpretation of legal knowledge (Van Kralingen 1995), since this was Visser's role in the project. Visser's contribution with respect to the interpretation of sources however, remains limited to a few remarks on the interpretation of vague concepts, e.g. *suitable employment*. He suggests to consult court decisions to clarify vague concepts, using a method that can be characterized as mapping the vague concept to a multi-dimensional space, and to use a calculation method to decide if an instance fits the category borders. This looks very much like the HYPO model (Ashley 1991), but then only to reason on the semantics of vague terms. We conclude that both Visser and Van Kralingen left underexposed the issue of how to make the transcription of the source of a norm in natural language into a normative frame. Breaux (2009) made his own frame for analyzing of sources of norms, without referring to the work of Van Kralingen. With his approach, Breaux aimed to support knowledge engineers in building legal reasoning systems and consequently named his approach a Frame-Based Requirements Analysis Method. The frame-based approaches of Van Kralingen and Breaux use their frames to assign text fragments from sources of norms to slots in the frames. If a specific frame element cannot be found in a sentence, or set of sentences, then that frame element remains empty. Also, both approaches have the problem that actions do not necessarily result in a state-transition. Van Kralingen's frames allows for actions that lack a final state. These actions are classified as acts of the type *state-of-affairs*. Breaux' frame-base lacks a slot for a final state or postcondition. We consider it essential that every action results in a next well-defined final state, or postcondition. If the result of a normative action cannot be found explicitly in a source, the source sentence is not considered to contain a valid FLINT act type frame. The only possibility to repair the deficit of the missing result is by making an explicit statement that:

1. the source sentence implies a specific result,

2. the result of the action can be found in a separate sentence which should be interpreted in conjunction with it.

By making these statements explicit, the interpretations are open to be challenged by alternatives.
Other authors, like De Maat et al. (2009), have used frame-based approaches to capture knowledge from sources of norms using natural language processing (NLP) techniques. In our research, we use a frame-based approach for making an explicit representation of the interpretations of sources of norms.

## 6.4 Norms and arguments

Since different interpretations of sources of norms may lead to arguments as to which interpretation is preferred, we also study how formal models of interpretation can be discussed using formal methods for argumentation. For this we use argument schemes based on Toulmin's model of argumentation (Toulmin 1958). Statements are considered to be claims, based on reasons. If a claim is attacked successfully, the attack will defeat the claim. This type of attack is called a rebuttal. If the relation between a claim and the reasons that support that claim is successfully attacked, that is called a undercutter (Pollock 1987). So, a successful rebuttal results in the claim being false, and a successful undercutter results in uncertainty whether the claim is true or false. Formalization of argument schemes, using tools such as ARAUCARIA (Reed and Rowe 2004) and ArguMed (Van Eemeren et al. 2014), lies outside the scope of this section. Some work has been done on the classification and formalization of argument schemes concerning the problem of statutory interpretation (Walton et al. 2016; Araszkiewicz 2015). Typically the classification of argument schemes concerns the classification of arguments, e.g. arguments from ordinary meaning, arguments of technical meaning, arguments from legal concepts. There are no classifications of argument schemes aimed for making a type of claim, e.g. interpretation of a norm respectively the application of a norm to a case.

## 6.5 A method for the interpretation of sources of norms in natural language

Our perspective on the interpretation of sources of norms follows from the goals we want to achieve by using these models:

1. To understand the consequences of sources of norms for all addressees.

2. To allow for a discussion on the interpretation of sources of norms expressed in natural language in a precise way.

3. To provide a large-scale applicable method supporting knowledge engineers in translating these sources of norms into a formal, machine readable and executable representation, thus overcoming ambiguity issues of natural language and allowing for IT-support systems for various tasks.

To be able to achieve these goals we aim for a maximum separation of concerns. Therefore, we separate the source of a norm, and the (formal) interpretation of that source. We separate the interpretation of a norm, and the factual representation of a specific case. And we make a separate representation for the mapping of the factual representation of a specific case onto an institutional model consisting of (formal) interpretations of sources of norms.
Other authors, like De Maat et al. (2009), have used frame-based approaches to capture knowledge from sources of norms using natural language processing (NLP) techniques. In our research, we use a frame-based approach for making an explicit representation of the interpretations of sources of norms.

## 6.4 Norms and arguments

Since different interpretations of sources of norms may lead to arguments as to which interpretation is preferred, we also study how formal models of interpretation can be discussed using formal methods for argumentation. For this we use argument schemes based on Toulmin's model of argumentation (Toulmin 1958). Statements are considered to be claims, based on reasons. If a claim is attacked successfully, the attack will defeat the claim. This type of attack is called a rebuttal. If the relation between a claim and the reasons that support that claim is successfully attacked, that is called a undercutter (Pollock 1987). So, a successful rebuttal results in the claim being false, and a successful undercutter results in uncertainty whether the claim is true or false. Formalization of argument schemes, using tools such as ARAUCARIA (Reed and Rowe 2004) and ArguMed (Van Eemeren et al. 2014), lies outside the scope of this section. Some work has been done on the classification and formalization of argument schemes concerning the problem of statutory interpretation (Walton et al. 2016; Araszkiewicz 2015). Typically the classification of argument schemes concerns the classification of arguments, e.g. arguments from ordinary meaning, arguments of technical meaning, arguments from legal concepts. There are no classifications of argument schemes aimed for making a type of claim, e.g. interpretation of a norm respectively the application of a norm to a case.

## 6.5 A method for the interpretation of sources of norms in natural language

Our perspective on the interpretation of sources of norms follows from the goals we want to achieve by using these models:

1. To understand the consequences of sources of norms for all addressees.

2. To allow for a discussion on the interpretation of sources of norms expressed in natural language in a precise way.

3. To provide a large-scale applicable method supporting knowledge engineers in translating these sources of norms into a formal, machine readable and executable representation, thus overcoming ambiguity issues of natural language and allowing for IT-support systems for various tasks.

To be able to achieve these goals we aim for a maximum separation of concerns. Therefore, we separate the source of a norm, and the (formal) interpretation of that source. We separate the interpretation of a norm, and the factual representation of a specific case. And we make a separate representation for the mapping of the factual representation of a specific case onto an institutional model consisting of (formal) interpretations of sources of norms.
<table><caption>**Table 2** The FLINT-language framework</caption><thead><tr><td>Act type frame</td><td>Duty type frame</td><td>Fact type frame</td></tr></thead><tbody><tr><td>Action</td><td>Description of duty</td><td>Fact type</td></tr><tr><td>Actor</td><td>Duty holder</td><td>Derivation function</td></tr><tr><td>Object</td><td>Claimant</td><td></td></tr><tr><td>Recipient</td><td>Liable agent</td><td></td></tr><tr><td>Precondition</td><td>Creating act type</td><td></td></tr><tr><td>Postcondition</td><td>Terminating act type</td><td></td></tr><tr><td>Reference to source(s)</td><td>Reference to source(s)</td><td>Reference to source(s)</td></tr></tbody></table>

For the interpretation of sources of norms we developed the *Formal Language for the Interpretation of Normative Theories*: FLINT (Van Doesburg et al. 2016a; Van Engers and Van Doesburg 2016). We use FLINT to formally describe institutional reality (Searle 1995). The Power to act is represented as an institutional *act type* that consist of an action, described in the source(s) of a norm, that an agent is allowed to perform on an object, if a precondition (consisting of institutional *fact types* connected by Boolean relations) is met. The action has a postcondition consisting of an institutional *fact types*, *act types* or *duty types* that can be either created or terminated. The result of the action is received by an agent holding the agent role *recipient*. *Act types* have a functional perspective, meaning that the performance of a given *act type* under the same conditions, always has the same result.

The *actor* is the agent holding the Hohfeldian power, the *recipient* the agent holding the liability. The performance of an institutional *act type* can create or terminate: an institutional *fact type* (e.g. a decision on an application creates a decision and terminates the application), an new institutional *act type* (e.g. an actor having the power to delegate, can delegate his power to another agent), or a institutional *duty type* (i.e. the duty of a duty holder to perform an institutional act in the future, and the corresponding claim of the claimant on the duty holder).

The precondition can be elaborated by making detailed expressions for separate institutional *fact types*. By making separate expressions for the details of individual *fact types*, the question whether a fact is true or not can be answered using first-order logic. The representation of institutional *act types* as drivers of state transitions from one world, to another, enables the description of consecutive decisions in the same case without using defeasible logic. As a result FLINT expressions can be presented without complicated logical constructions, enabling direct validation by all members of a multidisciplinary team.

The development of FLINT started in a NWO sponsored workshop,<fnref n="24" /> which has led to a continuing collaboration between academics and industry. It consists of a domain specific language (DSL) and a prototype that supports working with this language. For the three frame types of FLINT, see Table 2.

<footnote n="24">²⁴ Van Doesburg et al. (2016b).</footnote>
<table><caption>**Table 2** The FLINT-language framework</caption><thead><tr><td>Act type frame</td><td>Duty type frame</td><td>Fact type frame</td></tr></thead><tbody><tr><td>Action</td><td>Description of duty</td><td>Fact type</td></tr><tr><td>Actor</td><td>Duty holder</td><td>Derivation function</td></tr><tr><td>Object</td><td>Claimant</td><td></td></tr><tr><td>Recipient</td><td>Liable agent</td><td></td></tr><tr><td>Precondition</td><td>Creating act type</td><td></td></tr><tr><td>Postcondition</td><td>Terminating act type</td><td></td></tr><tr><td>Reference to source(s)</td><td>Reference to source(s)</td><td>Reference to source(s)</td></tr></tbody></table>

For the interpretation of sources of norms we developed the *Formal Language for the Interpretation of Normative Theories*: FLINT (Van Doesburg et al. 2016a; Van Engers and Van Doesburg 2016). We use FLINT to formally describe institutional reality (Searle 1995). The Power to act is represented as an institutional *act type* that consist of an action, described in the source(s) of a norm, that an agent is allowed to perform on an object, if a precondition (consisting of institutional *fact types* connected by Boolean relations) is met. The action has a postcondition consisting of an institutional *fact types*, *act types* or *duty types* that can be either created or terminated. The result of the action is received by an agent holding the agent role *recipient*. *Act types* have a functional perspective, meaning that the performance of a given *act type* under the same conditions, always has the same result.

The *actor* is the agent holding the Hohfeldian power, the *recipient* the agent holding the liability. The performance of an institutional *act type* can create or terminate: an institutional *fact type* (e.g. a decision on an application creates a decision and terminates the application), an new institutional *act type* (e.g. an actor having the power to delegate, can delegate his power to another agent), or a institutional *duty type* (i.e. the duty of a duty holder to perform an institutional act in the future, and the corresponding claim of the claimant on the duty holder).

The precondition can be elaborated by making detailed expressions for separate institutional *fact types*. By making separate expressions for the details of individual *fact types*, the question whether a fact is true or not can be answered using first-order logic. The representation of institutional *act types* as drivers of state transitions from one world, to another, enables the description of consecutive decisions in the same case without using defeasible logic. As a result FLINT expressions can be presented without complicated logical constructions, enabling direct validation by all members of a multidisciplinary team.

The development of FLINT started in a NWO sponsored workshop,<fnref n="24" /> which has led to a continuing collaboration between academics and industry. It consists of a domain specific language (DSL) and a prototype that supports working with this language. For the three frame types of FLINT, see Table 2.

<footnote n="24">²⁴ Van Doesburg et al. (2016b).</footnote>
When you create a formal representation of a source, the first thing you are faced with is the bootstrapping problem: where to start? Different solutions have been proposed in the past, but most authors remain vague about this particular point. Experience in the POWER project (Van Engers et al. 2000) shows that it doesn't necessarily make sense to start with the first sentence of a source and then work your way to the end of it, if only for the fact that the sources are to voluminous, and contain references to equally voluminous additional sources, and so on and so forth. This leads to a task that cannot be completed. We decided to use a more pragmatic, task-oriented, approach.

We suggest the following procedure for making an interpretation model:

1. Decide which task(s) are to be supported.

2. Find an initial set of relevant sources of norms.

3. Identify an initial set of relevant fragments (sentences or constituents thereof) for the task(s) at hand.

4. Transform relevant fragments into an explicit interpretation model.

5. If necessary, find additional sources and fragments to fill missing information.

6. Make explicit expressions for all assumptions left implicit by the authors of the sources of norms.

The first step of the procedure constitutes a pragmatic starting point. The last two steps are required to make complete all elements of a frame. An institutional *act type* cannot exist without an action, actor, object, recipient and some kind of postcondition. An *act type* without any precondition constitutes an absolute power, it describes an act that is allowed regardless of the condition the actor is in. When a frame is not complete, we assume that information needed to act according to the norms, is missing or inappropriately left implicit. If fragments filling this shortcoming, are not found, we assume that the source of norm contains implicit information, and we choose an explicit expression that, we think, fits the meaning of the interpreted sources of norms best. By making the interpretation of a source explicit, the interpretation is opened up for discussion. Arguments for alternative interpretations can be presented and conflicting interpretations can be debated, resulting in consensus or a decision by an arbitrator.

Unfortunately, this comes at the cost of sacrificing completeness as the boundaries of the resulting model are impacted by the chosen task. The interpretation model itself, however, is task independent. In other words, the model may be used for a variety of tasks, although it may still be incomplete. This brings us to the stopping criteria. This will always remain a question open for debate. There may be new questions or tasks that require additional sources of norms to be included. There may be changes in sources of norms that are already in the interpretation model. And of course, there also may appear new interpretations of existing sources of norms.

The goal of making an interpretation is not to create a complete model that results in the correct answer to any question. The goal is to make a model that can be used to reason about a specific set of questions. FLINT is being used to enable experts from the legal, policy and administrative domain to explicitly formulate
When you create a formal representation of a source, the first thing you are faced with is the bootstrapping problem: where to start? Different solutions have been proposed in the past, but most authors remain vague about this particular point. Experience in the POWER project (Van Engers et al. 2000) shows that it doesn't necessarily make sense to start with the first sentence of a source and then work your way to the end of it, if only for the fact that the sources are to voluminous, and contain references to equally voluminous additional sources, and so on and so forth. This leads to a task that cannot be completed. We decided to use a more pragmatic, task-oriented, approach.

We suggest the following procedure for making an interpretation model:

1. Decide which task(s) are to be supported.

2. Find an initial set of relevant sources of norms.

3. Identify an initial set of relevant fragments (sentences or constituents thereof) for the task(s) at hand.

4. Transform relevant fragments into an explicit interpretation model.

5. If necessary, find additional sources and fragments to fill missing information.

6. Make explicit expressions for all assumptions left implicit by the authors of the sources of norms.

The first step of the procedure constitutes a pragmatic starting point. The last two steps are required to make complete all elements of a frame. An institutional *act type* cannot exist without an action, actor, object, recipient and some kind of postcondition. An *act type* without any precondition constitutes an absolute power, it describes an act that is allowed regardless of the condition the actor is in. When a frame is not complete, we assume that information needed to act according to the norms, is missing or inappropriately left implicit. If fragments filling this shortcoming, are not found, we assume that the source of norm contains implicit information, and we choose an explicit expression that, we think, fits the meaning of the interpreted sources of norms best. By making the interpretation of a source explicit, the interpretation is opened up for discussion. Arguments for alternative interpretations can be presented and conflicting interpretations can be debated, resulting in consensus or a decision by an arbitrator.

Unfortunately, this comes at the cost of sacrificing completeness as the boundaries of the resulting model are impacted by the chosen task. The interpretation model itself, however, is task independent. In other words, the model may be used for a variety of tasks, although it may still be incomplete. This brings us to the stopping criteria. This will always remain a question open for debate. There may be new questions or tasks that require additional sources of norms to be included. There may be changes in sources of norms that are already in the interpretation model. And of course, there also may appear new interpretations of existing sources of norms.

The goal of making an interpretation is not to create a complete model that results in the correct answer to any question. The goal is to make a model that can be used to reason about a specific set of questions. FLINT is being used to enable experts from the legal, policy and administrative domain to explicitly formulate
their interpretation of a source of norm, while knowledge engineers can focus on the
formal aspects of the expression.

## 6.6 Discussion and conclusion

### 6.6.1 What is new?

The following aspects are considered to be contributions to the field of AI and Law:

1. A first version of a procedure for knowledge engineers to start and stop the process of modeling sources of norms. The authors do not claim completeness of the resulting interpretation model. All interested parties are invited to argue that not all relevant fragments of sources of norms are included, and/or that the interpretation of these fragments should be changed.

2. Separating the interpretation of norms, and application to specific cases (actual or imaginary). Rule-based, case-based, logic based, and the frame-based approaches by Van Kralingen, Visser<sup>25</sup> and Breaux do not make this distinction. The FLINT approach distinguishes:

(a) the relevant sources of norms that apply to a specific (set of) task(s);

(b) the creation of an interpretation model for a set of sources of norms, consisting of *act types*, *duty types* and *fact types*;

(c) the facts and acts of a specific case;

(d) the mapping of the facts and acts in a specific case onto an interpretation model.

3. The formal interpretation models allow for applying argumentation schemes to structure differences of opinion on the interpretation of norms, and allows us to use such arguments to reason about the correct application of norms to the facts of a case. For this task we use specific argument schemes for the separate parts of the interpretation, i.e. referring to the relevant sources of norms, interpreting the sources of norms in *act types* and *duty types*, describing the facts and acts of a specific case, mapping facts and acts in a specific case onto the interpretation model.

All these contributions enhance transparency. They also substantially decrease the complexity of the validation of interpretation models. Our research thus far resulted in:

1. A procedure for making an interpretation model.

2. Frames for institutional *act types*, institutional *duty types*, and derivation functions for institutional *fact types*.

<figure><img src="image_11.png" /></figure>

<footnote n="25"><sup>25</sup> Visser does some work on the interpretation of vague concepts. In our view this work is about the mapping of facts and acts of a specific case onto the vague concept that appears in statute law. This view is supported by the case-based approach Visser uses for his interpretations.</footnote>
their interpretation of a source of norm, while knowledge engineers can focus on the
formal aspects of the expression.

**6.6 Discussion and conclusion**

6.6.1 *What is new?*

The following aspects are considered to be contributions to the field of AI and Law:

1. A first version of a procedure for knowledge engineers to start and stop the process of modeling sources of norms. The authors do not claim completeness of the resulting interpretation model. All interested parties are invited to argue that not all relevant fragments of sources of norms are included, and/or that the interpretation of these fragments should be changed.

2. Separating the interpretation of norms, and application to specific cases (actual or imaginary). Rule-based, case-based, logic based, and the frame-based approaches by Van Kralingen, Visser<sup>25</sup> and Breaux do not make this distinction. The FLINT approach distinguishes:

(a) the relevant sources of norms that apply to a specific (set of) task(s);

(b) the creation of an interpretation model for a set of sources of norms, consisting of *act types*, *duty types* and *fact types*;

(c) the facts and acts of a specific case;

(d) the mapping of the facts and acts in a specific case onto an interpretation model.

3. The formal interpretation models allow for applying argumentation schemes to structure differences of opinion on the interpretation of norms, and allows us to use such arguments to reason about the correct application of norms to the facts of a case. For this task we use specific argument schemes for the separate parts of the interpretation, i.e. referring to the relevant sources of norms, interpreting the sources of norms in *act types* and *duty types*, describing the facts and acts of a specific case, mapping facts and acts in a specific case onto the interpretation model.

All these contributions enhance transparency. They also substantially decrease the complexity of the validation of interpretation models. Our research thus far resulted in:

1. A procedure for making an interpretation model.

2. Frames for institutional *act types*, institutional *duty types*, and derivation functions for institutional *fact types*.

<footnote n="25"><sup>25</sup> Visser does some work on the interpretation of vague concepts. In our view this work is about the mapping of facts and acts of a specific case onto the vague concept that appears in statute law. This view is supported by the case-based approach Visser uses for his interpretations.</footnote>
3. A DSL for the formal interpretation of sources of norms (FLINT) (Van Doesburg et al. 2016a).

4. A prototype for generating a datalog reasoner from a FLINT representation of a simplified example in the tax domain (to be published).

5. Interpretation models for several legal domains, including Dutch immigration policy (Van Doesburg et al. 2016a), tax policy (Van Doesburg and Van Engers 2017), administrative law (Van Doesburg and Van Engers 2017) and the European General Data Protection Regulation (to be published).

6. First experiment with argumentation schemes for the interpretation of sources of norms (Van Doesburg and Van Engers 2017).

### 6.6.2 Future work

Future work will focus on the following subjects:

1. Evaluation of the method and the resulting interpretation models by experts and laymen and experts from the fields of law, policy advising, public administration, knowledge engineers.

2. Elaborating methods for using argument schemes to resolve conflicts on the interpretation of sources of norms.

3. To re-engineer the FLINT solution allowing for large-scale application. This includes the development of tools for modelers and domain experts.

**Acknowledgements** The participants of the doctoral consortium would like to thank Guido Governatori, Leon van der Torre, Bart Verheij, Enrico Francesconi, and Giovanni Sartor for volunteering as mentors and Trevor Bench-Capon for his constructive comments and editorial efforts while developing their research reports.

**Open Access** This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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3. A DSL for the formal interpretation of sources of norms (FLINT) (Van Doesburg et al. 2016a).

4. A prototype for generating a datalog reasoner from a FLINT representation of a simplified example in the tax domain (to be published).

5. Interpretation models for several legal domains, including Dutch immigration policy (Van Doesburg et al. 2016a), tax policy (Van Doesburg and Van Engers 2017), administrative law (Van Doesburg and Van Engers 2017) and the European General Data Protection Regulation (to be published).

6. First experiment with argumentation schemes for the interpretation of sources of norms (Van Doesburg and Van Engers 2017).

### 6.6.2 Future work

Future work will focus on the following subjects:

1. Evaluation of the method and the resulting interpretation models by experts and laymen and experts from the fields of law, policy advising, public administration, knowledge engineers.

2. Elaborating methods for using argument schemes to resolve conflicts on the interpretation of sources of norms.

3. To re-engineer the FLINT solution allowing for large-scale application. This includes the development of tools for modelers and domain experts.

**Acknowledgements** The participants of the doctoral consortium would like to thank Guido Governatori, Leon van der Torre, Bart Verheij, Enrico Francesconi, and Giovanni Sartor for volunteering as mentors and Trevor Bench-Capon for his constructive comments and editorial efforts while developing their research reports.

**Open Access** This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Hart HLA, Honoré T (1985) Causation in the law. OUP, Oxford

Herrestad H, Krogh C (1995) Obligations directed from bearers to counterparts. In: Proceedings of the 5th international conference on artificial intelligence and law, ACM, pp 210–218

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Hoekstra R, Breuker J, Di Bello M, Boer A, et al (2007) The LKIF core ontology of basic legal concepts. In: Proceedings of the workshop on legal ontologies and artificial intelligence techniques, pp 43–63

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Van Eemeren FH, Garssen B, Krabbe EC, Henkemans AFS, Verheij B, Wagemans JH (2014) Handbook of argumentation theory. Springer, Berlin

Van Engers T, Boer A (2011) Public agility and change in a network environment. JeDEM-eJ eDemocr Open Gov 3(1):99–117

Van Engers T, Van Doesburg R (2016) Modeling the interpretation of sources of norms. In: Proceedings of eKNOW, pp 41–50

Van Engers TM, Kordelaar PJ, Den Hartog J, Glassée E (2000) Power: programme for an ontology based working environment for modeling and use of regulations and legislation. In: Proceedings of 11th international workshop on database and expert systems applications, IEEE, pp 327–334

Van Kralingen RW (1995) Frame-based conceptual models of statute law. Kluwer Law International, Alphen aan den Rijn

Van Kralingen RW, Visser PR, Bench-Capon TJ, Van Den Herik HJ (1999) A principled approach to developing legal knowledge systems. Int J Hum Comput Stud 51(6):1127–1154

Verheij B (2003) Dialectical argumentation with argumentation schemes: an approach to legal logic. Artif Intell Law 11(2):167–195

Verheij B, Bex F, Timmer ST, Vlek CS, Meyer J-JC, Renooij S, Prakken H (2015) Arguments, scenarios and probabilities: connections between three normative frameworks for evidential reasoning. Law Prob Risk 15(1):35–70

Visser PR (1995) Knowledge specification for multiple legal tasks: a case study of the interaction problem in the legal domain. Kluwer Law International, Alphen aan den Rijn

Vlek CS, Prakken H, Renooij S, Verheij B (2014) Building bayesian networks for legal evidence with narratives: a case study evaluation. Artif Intell Law 22(4):375–421

Von Wright GH (1963) Norm and action: a logical enquiry. Routledge and Kegan Paul, Abingdon

Walton D (2005) Argumentation methods for artificial intelligence in law. Springer, Berlin

Walton D, Sartor G, Macagno F (2016) An argumentation framework for contested cases of statutory interpretation. Artif Intell Law 24(1):51–91

Wellman C (1990) Relative duties in the law. Philos Top 18(1):183–202

Wenar L (2005) The nature of rights. Philos Public Affairs 33(3):223–252

Wigmore JH (1937) The science of judicial proof, as given by logic, psychology, and general experience, and illustrated in judicial trials. Little Brown, and Co., Boston

Wright RW (1985) Causation in tort law. Calif Law Rev 73(6):1735–1828

Wright RW (2011) The NESS account of natural causation: a response to criticisms. http://scholarship.kentlaw.iit.edu/fac_school/716. Accessed 1 May 2017

Wyner A, Van Engers T, Hunter A (2016) Working on the argument pipeline: through flow issues between natural language argument, instantiated arguments, and argumentation frameworks. Argum Comput 7(1):69–89

Zieliński A (2017) Kodeks postępowania cywilnego. Komentarz, C.H. Beck, Warszawa

Ziembiński Z (1978) Teoria prawa, Poznań – Warszawa