Modeling semantic business trajectories of territories for multidisciplinary studies through controlled vocabularies

Environmental changes influence society and this impact extends to businesses across several sectors. The risks linked with environmental changes have both advantages and disadvantages for companies. Therefore, business analysts must understand how their companies should deal with the opportunities and challenges that environmental changes present. On the other hand, environmental analysts working on different aspects (e.g. atmospheric sciences, ecology, geosciences, social sciences, etc.) of an environment for human health and well-being are also interested in studying the impact of businesses (e.g. the launch of a factory) on society. Though, business and environmental analysts are from different educational backgrounds. They work on different vocabularies and data formats for analyzing data. These differences create semantic heterogeneities to understand key concepts from data. This makes understanding cross-domain data a complex and time-consuming process for them, eventually creating a knowledge barrier. To address this knowledge barrier, this work presents a proof-of-concept modeling of business trajectories using online news data. The proposed method consists of five stages: a) news data preprocessing, and topic model training for identifying the relevant thematic concepts related to business trajectories using the historical dataset of news articles, b) semantic enrichment of thematic concepts using the controlled vocabularies, c) processing latest news articles using the trained topic model to acquire the most similar thematic concepts, and obtaining the location and temporal entities using the Named-Entity Recognition model (NER) process, d) constructing a knowledge graph using the collected thematic, spatial and temporal entities, along with enrichment of the relevant environmental data, and e) visualizing the semantic trajectories from knowledge graphs for understanding insights in data. This article presents the potential for retrieving the same trajectory data using different business and environmental viewpoints for multidisciplinary data analysis.