This dataset is comprised of data gathered for and created in the process of the paper [Deriving a Representative Vector for Ontology Classes with Instance Word Vector Embeddings][1]. Other than the files provided here, it uses the large legal corpus [created by an earlier study][2] out of which it takes a set of raw cases. In addition, this dataset contains the created ontology, a gazetteer list, and the result vectors. 1. **Legal Ontology:** This is the limited legal ontology built for the purpose of this study. 2. **Case Files:** This corpus contains X cases extracted from a [large corpus of legal cases from the United States supreme court][3]. 3. **Legal Domain gazetteer lists:** A set of gazetteer lists built by a legal professional and by data collection are included. 4. **Results:** Finally the result vectors obtained by the [above paper][1] are included. ---------- For the purpose of convenience, given below is the abstract of the paper [Deriving a Representative Vector for Ontology Classes with Instance Word Vector Embeddings][1]. *Selecting a representative vector for a set of vectors is a very common requirement in many algorithmic tasks. Traditionally, the mean or median vector is selected. Ontology classes are sets of homogeneous instance objects that can be converted to a vector space by word vector embeddings. This study proposes a methodology to derive a representative vector for ontology classes whose instances were converted to the vector space. We start by deriving five candidate vectors which are then used to train a machine learning model that would calculate a representative vector for the class. We show that our methodology out-performs the traditional mean and median vector representations.* [1]: https://goo.gl/TZPDpr [2]: https://osf.io/qvg8s/ [3]: https://osf.io/qvg8s/ [4]: https://goo.gl/ahZFF8 [5]: https://goo.gl/ahZFF8