Co-located with
The 27th ACM International Conference on Information and Knowledge Management (CIKM) 2018

Legal data mining is the subarea of data mining applied to legal texts, such as legislation, case law, patents, and scholarly works. Legal data mining systems are keys to provide easier access to and insights about law for both common persons and legal professionals. This area is becoming increasingly important, because of the rapidly growing volume of legal cases and documents available in digital formats. In this scenario, the LeDAM 2018 workshop aims to provide a venue for academic and industrial/governmental researchers and professionals to come together, present and discuss research results, use cases, innovative ideas, challenges, and opportunities that arise from applications of data mining in the legal domain. It also encourages to foster collaboration between the Legal and the Artificial Intelligence, Data Mining, Information Retrieval, and Machine Learning communities. Additionally, it is targeted at improving the awareness among the data mining research community about the problems addressed by the legal data miners and the challenges that they face.

The broad goal of the workshop is to promote research in legal data analytics by fostering collaboration between the legal data mining practitioners and the data mining research community at large. Some of the specific goals are to develop algorithms for tasks like prior case/patent retrieval, summarization of legal documents, as well as to develop models for argumentation and legal reasoning. The workshop promotes awareness among the legal community about the state of the art models, techniques and algorithms developed by the data mining community that can potentially benefit the problems legal practitioners regularly face. The workshop is positioned in a way that can benefit the Legal and the Data Mining communities by identifying new research opportunities in data mining that arise from legal applications such as prior case/patent retrieval, summarization of legal documents, models for argumentation and legal reasoning, etc. It seeks to promote direct collaboration between the two communities towards solving legal data mining problems.