This work presents a standardised approach to create datasets for Science and Technology Parks (STPs), facilitating future analysis of STP characteristics, trends and performance. STPs are the most representative examples of innovation ecosystems. The ETL (extraction-transformation-load) structure was adapted to a global field study of STPs. A selection stage and quality check were incorporated, and the methodology was applied to Spanish STPs. This study applies diverse techniques such as expert labelling and information extraction which uses language technologies. A novel methodology for building quality and standardised STP datasets was designed and applied to a Spanish STP case study with 49 STPs. An updatable dataset and a list of the main features impacting STPs are presented. Twenty-one (n=21) core features were refined and selected, with fifteen of them (71.4%) being robust enough for developing further quality analysis. The methodology presented integrates different sources with heterogeneous information that is often decentralised, disaggregated and in different formats: excel files, and unstructured information in HTML or PDF format. The existence of this updatable dataset and the defined methodology will enable powerful AI tools to be applied that focus on more sophisticated analysis, such as taxonomy, monitoring, and predictive and prescriptive analytics in the innovation ecosystems field.

This ontology aims to capture the semantics of documents through a set of key aspects in texts, such as the temporal dimension, presence of named entities, detection of opinionated information, or conceptual classifications. In addition, the ontology provides a lexical dimension, where the sentence of each document, and a possible summary derived from it, are taken into account. These are determining factors for setting up our own interpretation of possible scenarios (a meta-level specification) and vocabulary. Since our ontology aims to be reused by a large community, we tried to establish basic NLP terminology that was hierarchized by experts in this research field.