header

Global Machine Learning for Spatial Ontology Population

31 Pages Posted: 7 Jul 2018 First Look: Accepted

Abstract

Understanding spatial language is important in many applications such as geographical information systems, human computer interaction or text-to-scene conversion. Due to the challenges of designing spatial ontologies, the extraction of spatial information from natural language still has to be placed in a well-defined framework. In this work, we propose an ontology which bridges between cognitive-linguistic spatial concepts in natural language and multiple qualitative spatial representation and reasoning models. To make a mapping between natural language and the spatial ontology, we propose a novel global machine learning framework for ontology population. In this framework we consider relational features and background knowledge which originates from both ontological relationships between the concepts and the structure of the spatial language. The advantage of the proposed global learning model is the scalability of the inference, and the flexibility for automatically describing text with arbitrary semantic labels that form a structured ontological representation of its content. The machine learning framework is evaluated with SemEval-2012 and SemEval-2013 data from the spatial role labeling task.

Keywords: Spatial information extraction, Text mining, Structured output learning, Ontology population, Natural language processing

Suggested Citation

Kordjamshidi, Parisa and Moens, Marie-Francine, Global Machine Learning for Spatial Ontology Population (2015). Journal of Web Semantics First Look. Available at SSRN: https://ssrn.com/abstract=3199172 or http://dx.doi.org/10.2139/ssrn.3199172

Parisa Kordjamshidi (Contact Author)

KU Leuven ( email )

Oude Markt 13
Leuven, Vlaams-Brabant 3000
Belgium

Marie-Francine Moens

KU Leuven

Oude Markt 13
Leuven, Vlaams-Brabant 3000
Belgium

Register to save articles to
your library

Register

Paper statistics

Abstract Views
140
Downloads
6