Knowledge Discovery and Process Mining for Law (KDPM4LAW)

The Knowledge Discovery and process mining for Law (KDPM4LAW) track intends to be a forum to focus on legal informatics from a broad perspective. Machine Learning (ML), Data Mining (DM), Knowledge Discovery (KD) and Information Extraction (IE) methods are increasingly important for many sub-domains of legal informatics. A recent research line is engaged with the analysis of legal event-logs, through Process Mining (PM) techniques. 

The advanced developments in ML over recent years have meant that seemingly insurmountable problems in Law are beginning to be addressed. Accordingly, it is necessary to identify the limits of automated systems and how such systems can handle the remaining unsolved intentional and unintentional ambiguities and conflicts that require legal interpretation. 

Therefore, research works on the limits and unexplored opportunities offered by ML, DM, and KD in the legal domain constitute worthwhile contributions for this workshop. 

In addition, PM techniques concern practical applications to temporal data, developing a wide set of algorithms and tools for regulatory compliance checking, mining legal event logs for process discovery, and applying predictive process monitoring on legal cases.

Typical goals may include: classification of legal sources, clustering and similarity among legal decisions, process mining for legal compliance, mining legal event logs for process discovery, prediction and support during judicial decision making, legal interpretation support, identification of evolution of legal concepts and definitions over time, information extraction and classification, detection of patterns in legal sources, multilingual alignments of concepts on domestic and international legal sources, identification of legal references and network analysis.

Topics of interest

Potential topics and areas to be addressed in the workshop are:

• Machine Learning on legal dataset

• Knowledge discovery in legal databases

• Automated knowledge extraction from legal text corpora

• Identification of legal semantic roles and extraction of named entities

• Information retrieval and multimedia search for legal documents

• Entity Recognition and Disambiguation

• Classification or clustering of law

• Training and Using Embeddings for legal text

• Process mining for legal compliance

• Mining legal event logs for process discovery

• Predictive process monitoring on legal cases

• Natural language processing techniques and learning systems for legal documents

• Linked data and knowledge graphs in the legal domain

• Link Analysis, Relation and Event Extraction

• Legal Text Summarization and Generation

• Emerging applications in legal data & knowledge engineering

Keynote speakers

To be announced.

Program comittee (to be updated)

• Kolawole ADEBAYO (Dublin City University, Ireland / ADAPT Centre, Ireland)

• Davide AUDRITO  (University of Bologna, Italy)

• Valerio BASILE (University of Turin, Italy)

• Guido BOELLA (University of Turin, Italy)

• Chiara DI FRANCESCOMARINO (University of Trento, Italy)

• Beatriz ESTEVES (Universidad Politécnica de Madrid, Spain)

• Alfio FERRARA (University of Milan, Italy)

• Laura GENGA (Technical University of Eindhoven, Netherlands)

• Chiara GHIDINI (FBK, Trento, Italy)

• Stefano MONTANELLI (University of Milan, Italy)

• Rohan NANDA (University of Maastricht, Netherlands)

• María NAVAS LORO (Universidad Politécnica de Madrid, Spain)

• Livio ROBALDO (Swansea University, Wales)

• Víctor RODRÍGUEZ-DONCEL (Universidad Politécnica de Madrid, Spain)

• Massimiliano RONZANI (FBK, Trento, Italy)

• Giulia RUFFINI (University of Turin, Italy)

• Giovanni SIRAGUSA (University of Turin, Italy)

• Andrea TAGARELLI (University of Calabria, Italy)

Call for Papers

See also: Call for Papers 

NOTE: when submitting, please choose the track 'Knowledge Discovery and Process Mining for Law (KDPM4LAW)'. For any questions, pleas contact roberto.nai@unito.it.