Authors:
Sepideh Sobhgol
1
;
Mario Thron
1
and
Giuliano Persico
2
Affiliations:
1
ifak e.V. Magdeburg, Germany
;
2
Demag Cranes & Components GmbH, Germany
Keyword(s):
Ontology, Semantic Search, Keyword Extraction, Term Relation, Similarity Measurements.
Abstract:
InnoSale project aims to improve sales processes for complex industrial equipment and services using AI technologies. The project addresses the challenges of time-consuming back-office support and interpreting customer requests using different vocabularies. As partners involved in the project, we are developing a semiautomated approach to the creation of an ontology for the material handling domain by merging existing terminology from leading companies in the industry. This ontology will serve as the basis for a semantic search engine to improve the generation of quotations and the matching of customer requirements. Through the use of historical data and advanced machine learning techniques, the search engine streamlines the sales process, reducing manual effort and improving response times. The results showcases how the utilization of machine learning and NLP techniques can aid in constructing an ontology in a semi-automatic fashion. The study demonstrates the effectiveness of extra
cting terms, identifying synonyms, and uncovering various relationships, contributing to the development of an ontology. These approaches offer potential for improving the ontology construction process and enhancing semantic search capabilities, leading to more effective information retrieval. This position paper, being concise in nature, presents our initial findings and progress in this endeavor. It’s important to note that, based on new sources of information and ongoing research in the future, the results and conclusions may evolve or differ.
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