In recent years, investments into supporting technologies for the legal industry (also known as Legal Tech) have reached new record highs. NLP based information retrieval systems, in particular, have gained a lot of attention as the legal domain is confronted with an ever-growing amount of text-based information. The [CAROLL](https://ca-roll.github.io) research group at the University of Passau has developed a German legal citation network in order to investigate ways of leveraging NLP and network analysis tools to support common legal research tasks and reveal new information about the German legal system. In the early nineteenth century, the use of legal citation indexes started to become an important information retrieval tool in case law. Knowing which precedents are still being cited, or which new cases are gaining a lot of attention was crucial information. Legal scholars have long been theorising the benefits of using citation networks and network analysis algorithms for legal research purposes. For example, case citation networks can reveal critical information on precedents based on the characteristics of the citations towards the precedent candidates. Although this research has received some consideration from legal scholars in the past, it is still underutilised, especially for civil law systems and the Germanic law in particular. As a result, our research introduces the creation and analysis of a German legal citation network. The following steps and links shall get you up-to-speed with the data available in this repository: 1. The data for the network was taken from the [Open Legal Data](http://openlegaldata.io/) project, which consists of over 200.000 German court cases, more than 1.000 courts from all levels of appeal and jurisdiction and more than 50.000 laws. These were crawled from state and federal websites. 2. The data was pre-processed and stored as a graph in a Neo4j database. 3. Using regular expressions, references to other court cases and laws from within the decision texts of each court case were extracted and added as edges to the Neo4j graph. 4. The resulting Neo4j graph can be downloaded via the link below. 5. When importing the data into your Neo4j database, please use version 4.2.0 for best compatibility. You can import the data using the following command: > "neo4j-admin load --from=[your file Directory]\2021-04-01.dump --database=neo4j --force" _**The Neo4j database has the following schema:**_ **Nodes:** - Case - Law - Court **Relations:** - HEARING: Connects each case with the corresponding court - REF: Reference relation between courts, cases and cases to laws. **Index:** | Index Name | Type | Uniqueness | EntityType | LabelsOrTypes| Properties | State | | ------ | ------ |------ |------ |------ |------ |------ | | caseID_Index | BTREE | UNIQUE | NODE | [ "Case" ] | [ "caseID" ] | ONLINE | | courtID_Index | BTREE | UNIQUE | NODE | [ "Court" ] | [ "courtID" ] | ONLINE | | fileNumber_Index | BTREE | NONUNIQUE | NODE | [ "Case" ] | [ "fileNumber" ] | ONLINE | | lawCode_Index | BTREE | NONUNIQUE | NODE | [ "Law" ] | [ "section", "code" ] | ONLINE | | lawID_Index | BTREE | UNIQUE | NODE | [ "Law" ] | [ "lawID" ] | ONLINE | **Constraints:** - ON ( case:Case ) ASSERT (case.caseID) IS UNIQUE - ON ( court:Court ) ASSERT (court.courtID) IS UNIQUE - ON ( law:Law ) ASSERT (law.lawID) IS UNIQUE [1]: https://mfr.de-1.osf.io/export?url=https://osf.io/ub8f2/?direct&mode=render&action=download&public_file=False&initialWidth=848&childId=mfrIframe&parentTitle=OSF%20%7C%20Court-Case-Law.jpg&parentUrl=https://osf.io/ub8f2/&format=2400x2400.jpeg