Artificial Intelligence has proven to be efective in streamlining processes in several domains. The Brazilian judiciary, specifically, has a very large number of cases, above the work capacity of the courts, generating urgency in the creation of methods that mainly support the access and manipulation of unstructured data. This paper presents the construction of a Knowledge Graph of the Brazilian Legislation using Semantic Web standards that allows an understanding of how Brazilian laws interact with each other. The Knowledge Graph was quantitatively evaluated using complex network analysis and it was found to be useful to support experts in understanding the Brazilian legislation by detecting special nodes, namely the “bridge-like nodes”, that play an important role in the structure of the graph.
There is a growing demand for the application of Artificial Intelligence (AI) resources in all
branches of human activity. Many of the tasks performed by people can be performed by
machines with greater speed and reliability. In this context, Knowledge graphs (KGs) have been
successfully applied in many domains as a way to encode knowledge for supporting downstream
applications [
These tools can be efectively applied in the legal domain, where accessing more structured data about legal documents, especially laws, and understanding their connections and relationships can provide useful information not only for legal scholars investigating how legal topics evolve but, also, for the emerging Legal Tech Industry. A well-established source that accurately represents the law, its topics, and relationships, could provide the path to more efective and reliable legal knowledge which could be used to support applications ranging from automatic classification of legal documents to deployment of predictive decision-making support.
In addition, there are, several approaches for encoding legislation and laws, including as KGs,
mainly in Europe [
In this paper, we present the construction of the Knowledge Graph for Brazilian Federal
Legislation through the examination of relationships between laws, including citations, changes,
and repeals. For this, we leveraged the LexML data portal, which serves as a source of
semistructured Brazilian legal data [
Semantic Web and Data Science applications for the legal domain have been very common in
the last 15 years [
In recent years, systems for processing legal domain content received a lot of attention. In
the same measure, eforts have been done on the creation of knowledge graphs for the legal
domain. An example of such eforts is the EU-funded project Lynx [
We sourced data by extracting information from the LexML portal [
Quantity
The data extraction was performed using Python’s Requests module [
The database created using this approach is composed of information about legislation, such as type, name, date of approval, date of publication, and summary. Besides that, it contains information about relationships between elements of the legislation such as changes, citations, regulations, and repeals. This amount of collected information allowed us to create a Legal Knowledge Graph capable of representing the diferent elements of the legislation and the diferent relationships between them.
In Figure 1, we show a complete visualization of the Legal Knowledge Graph. This graph has a total of 214,407 nodes, that correspond to legislation presented in Table 1, and a total of 142,734 edges where 28,451 represent changes, 17,452 citations, 17,452 regulations and 93,905 repeals. We highlight that diferent legislation types (nodes) can interact through diferent types of relationships.
Although KGs are not defined by the technology they adopt, Semantic Web standards have
proven useful when used to represent knowledge in a graph structure, including provenance [
Knowledge graphs often employ ontologies as a way to abstract graph content, supporting graph cohesion by providing standardized terminology for nodes and edges. Ontologies help during automatic KG construction (supporting link prediction) as well as to enable graph querying. We used the European Legislation Identifier (ELI)1 ontology, which provides a set of fundamental legal concepts and is reasonably capable of being reused in many legal sources. 1https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52012XG1026(01) We also developed routines for the automatic generation of legal resource metadata in RDF from the legislation database presented in the previous section. Listing 1 shows an example containing Federal Decree-Law No. 2848 of 1940 and Law No. 14110 of 2020 that changes it.
Listing 1: Example of Brazilian legal resources in RDF using the ELI ontology The ELI ontology provides the high-level LegalResource class and the type_document predicate that allows the characterization of the legal resource according to a domain-specific ontology. Other predicates, including changes, amends, and repeals, help to build the graph by allowing the proper identification of connections between laws. We provide an online version of the Legal Knowledge Graph using this ontology at https://github.com/hansidm/br-legal-kg.
The approach described in the previous sections has the advantage of allowing the application of complex network techniques to explore Brazilian legislation. Here, we perform a direct simplification of the constructed KG by defining a network where nodes represent unique legislation and edges represent any type of relationship described in the previous section. Then, we use graph theory to extract topological information from the network that allows us to build a profile of the structure of Brazilian legislation. This process resulted in a directed unweighted graph with = 214, 407 nodes and = 141, 568 edges. It is important to note that the fraction / ≈ 0.660 is too low, which is an indication of the graph’s edge density. This suggests that the network is not a connected graph and is likely split into many small fragments. To explore this property, we perform a connected components analysis of the graph. By doing this, we find a total of 98,710 weakly connected components, where the largest of them has 110,466 (51.5%) nodes and 95,484 (44.5%) of them are unconnected nodes. The remaining 8,457 nodes are divided into the other 3,225 components. This indicates that a number of legal documents do not relate with which other or are independent.
To quantify the global importance of a given node, it is often convenient to look at some
node-centrality measures. In this case, the simple and natural choice for measuring importance
in our context is certainly in-degree centrality [
In Table 2, we show some Brazilian legal codes2 and the corresponding in-degree of the nodes associated with them. In this table, the laws are sorted in descending order of . As we can see, the highest in-degree node is associated with the “Consolidation of Labor Laws”, followed by the “National Tax Code” and the “Penal Code”. Surprisingly, there are some Legal Codes that, despite their importance, have a very low in-degree. Nonetheless, in-degree still is a good proxy for the relevance of a Legal Code in the same way that it is for usual nodes. This fact just indicates that having a high in-degree is suficient but not a necessary condition for a node to be important.
Brazilian law practitioners usually have a qualitative notion of what specific laws are of interest, without specific metrics that would reveal them from a computational perspective. We have worked with domain practitioners to identify elements that would characterize a legal document as of interest. In doing so, we define the bridge-like nodes as the nodes that belong to the neighborhood of at least two nodes associated with Brazilian Legal Codes. We hypothesize that if a node is bridge-like, then this node represents a legal document that is of interest according to the domain practitioners.
Proceeding in that way, we detect 55 bridge-like nodes connected to 16 Brazilian Legal Codes shown in Table 2. There is only one code that has no bridge-like nodes connected to it, namely the "Water Code". A convenient representation of the relationship between the bridge-like nodes 2The list of Brazilian Legal Codes used for this analysis can be found at: http://www4.planalto.gov.br/legislacao/portallegis/legislacao-1/codigos-1. and the Brazilian legal codes can be done by drawing the subgraph defined by the Brazilian Legal Codes, the bridge-like nodes, and the edges between them. In Figure 3, we show this subgraph. There, the Brazilian Legal Codes are represented by the big orange circles, and the bridge-like nodes are represented by the blue circles. The subgraph defined by Brazilian Legal Codes and bridge-like nodes has a total of 76 connected nodes and is a fraction of the largest component of the network.
In Table 3, we show the five bridge-like nodes with the highest . The first bridge-like node corresponds to “Law No. 8383 of 30/12/1991”. This law changes the income tax legislation and establishes the Tax Reference Unit, a tax correction factor created in a period of quite high inflation in Brazil. The second node corresponds to “Law No. 8884 of 11/06/1994”. It provides for the prevention and repression of infractions against the economic order and transforms 45 21 18 16 15 the Administrative Council for Economic Defense (Cade) into an Autarchy, which is a type of indirect public administration entity with administrative and financial autonomy. The third node corresponds to “Law No. 13146 of 06/07/2015”. It establishes the Brazilian Inclusion Law of Persons with Disabilities. An important Brazilian law that later became known as the “Statute of Persons with Disabilities”. These three examples show that, despite the bridge-like nodes having a low in-degree, they are of great importance to Brazilian Legislation.
We presented an approach for constructing a Knowledge Graph for Brazilian Legislation. Legal data from LexMl portal has been extracted and integrated into a database that serves as input to a method that represents this data as linked data in RDF using Semantic Web standards. Then, we used graph theory to extract information from the Knowledge Graph that allowed us to make characterizations about the structure of Brazilian legislation, essentially building a graph profile. We found indications of low edge density in the graph, which was confirmed by performing a connected component analysis. We also calculated the in-degree centrality for the network nodes and observed that it is a good global-importance measure of a given node in our network since the nodes with the highest degree coincide with the Legal Codes of Brazilian Legislation.
This approach, also, allowed us to detect “bridge-like nodes” (nodes that belong to the neighborhood of at least two nodes associated with Brazilian Legal Codes). These nodes have been shown to play an important role in the structure of the network since they connect diferent important parts of Brazilian Legislation. We believe that a careful analysis of these bridge-like nodes would allow a more detailed understanding of the inner structure of the network. It could also bring insights into the processes that lead to the formation of the Legal Knowledge Graph. The information generated by the bridge-like nodes approach can be very useful for legal scholars to understand the real impact of laws as it promotes a new way to identify to what legal dimensions one particular law establishes connections.
For future work, in terms of graph applications, we intend to carry out a deeper analysis of the Knowledge Graph in order to better understand its underlying topological structure and characterize temporal correlations between laws that can help us to understand the evolution of Brazilian Legislation. In terms of Semantic Web Standards, we intend to refine the legal modeling in the graph by exploring the formal definition of the extensions needed for further characterization of legal data in the form of an ontology. We also intend to explore the inner structure of the legal documents which could shed light on the relations between parts of documents. As LexML is of a high level, we can refine the representation by providing contextualized terminology for the Brazilian scenario. 5.0.1. Acknowledgments.
We gratefully acknowledge CNPq, CAPES, FUNCAP, and the Edson Queiroz Foundation for ifnancial support.