Automatic Taxonomy Classification by Pretrained Language Model

In recent years, automatic ontology generation has received significant attention in information science as a means of systemizing vast amounts of online data. As our initial attempt of ontology generation with a neural network, we proposed a recurrent neural network-based method. However, updating the architecture is possible because of the development in natural language processing (NLP). By contrast, the transfer learning of language models trained by a large, unlabeled corpus has yielded a breakthrough in NLP. Inspired by these achievements, we propose a novel workflow for ontology generation comprising two-stage learning. Our results showed that our best method improved accuracy by over 12.5%. As an application example, we applied our model to the Stanford Question Answering Dataset to show ontology generation in a real field. The results showed that our model can generate a good ontology, with some exceptions in the real field, indicating future research directions to improve the quality.

TOWARDS THE AUTOMATIC ONTOLOGY GENERATION AND ALIGNMENT OF BIM AND GIS DATA FORMATS

Establishing semantic interoperability between BIM and GIS is vital for geospatial information exchange. Semantic web have a natural ability to provide seamless semantic representation and integration among the heterogeneous domains like BIM and GIS through employing ontology. Ontology models can be defined (or generated) using domain-data representations and further aligned across other ontologies by the semantic similarity of their entities - introducing cross-domain ontologies to achieve interoperability of heterogeneous information. However, due to extensive semantic features and complex alignment (mapping) relations between BIM and GIS data formats, many approaches are far from generating semantically-rich ontologies and perform effective alignment to address geospatial interoperability. This study highlights the fundamental perspectives to be addressed for BIM and GIS interoperability and proposes a comprehensive conceptual framework for automatic ontology generation followed by ontology alignment of open-standards for BIM and GIS data formats. It presents an approach based on transformation patterns to automatically generate ontology models, and semantic-based and structure-based alignment techniques to form cross-domain ontology. Proposed two-phase framework provides ontology model generation for input XML schemas (i.e. of IFC and CityGML formats), and illustrates alignment technique to potentially develop a cross-domain ontology. The study concludes anticipated results of cross-domain ontology can provides future perspectives in knowledge-discovery applications and seamless information exchange for BIM and GIS.

Dynamic semantic ontology generation: a proposal for social robots

During a human-robot interaction by dialogue/voice, the robot cannot extract semantic meaning from the words used, limiting the intervention itself. Semantic knowledge could be a solution by structuring information according to its meaning and its semantic associations. Applied to social robotics, it could lead to a natural and fluid human-robot interaction. Ontologies are useful representations of semantic knowledge, as they capture the relationships between objects and entities. This paper presents new ideas for ontology generation using already generated ontologies as feedback in an iterative way to do it dynamically. This paper also collects and describes the concepts applied in the proposed methodology and discusses the challenges to be overcome.

AUTOMATIC ONTOLOGY GENERATION OF BIM AND GIS DATA

Data represented in the form of geospatial context and detailed building information are prominently nurturing infrastructure development and smart city applications. Bringing open-formats from data acquisition level to information engineering accelerates geospatial technologies towards urban sustainability and knowledge-based systems. BIM and GIS technologies are known to excel in this domain. However, fundamental level differences lie among their data-formats, which developed integration methods to bridge the gap between these distinct domains. Several studies have conducted data, process, and application-level integration, considering the significance of collaboration among these information systems. Although integration methods have narrowed the gap of geometric dissimilarity, semantic inconsistency, and information loss yet add constraints towards achieving interoperability. Integration using semantic web technology is more flexible and enables process-level integration without changing data format and structure. However, due to its developing nature and complex BIM-GIS data-formats, most approaches adapted requires human intervention. This paper presents a method, named OGGD (Ontology Generation for Geospatial Data), that implements a formal method for automatic ontology generation from XSD documents using transformation patterns following three extensive processes; first, formalization of XSD elements and transformation patterns; the second process identifies corresponding patterns explicitly, and the last process generates ontology for XSD schema. XSD elements from open-standard data models of BIM and GIS, ifcXML and CityGML, are manipulated and transformed into a semantically rich OWL model. The ontology models created can be applicable for information-based integration systems that will nurture knowledge-discovery and urban applications.

Automatic Ontology Construction

World wide web has information resources even on unthinkable subjects. This information may be available instantly to anyone having Internet connection. This web is growing exponentially, and it is becoming difficult to locate useful information in such a sheer volume of information. Semantic web extends the current web by emphasizing on interoperable ontologies which are capable of processing high quality information so that the agents placed on top of semantic web can automate the work or curate the content for the user. In this chapter, an extensive research in the area of ontology construction is presented, and after having a critical look over the work done in this field and considering the limitation of each, it has been observed that constructing ontology automatically is a challenging task as this task faces difficulties due to unstructured text and ambiguities in English text. In this work an ontology generation technique is devised covering all important aspects missing in the existing works giving better performance as compared to another system.