Abstract
In the recent years, IT (Information Technology) offers may have represented a barrier for a customer who does not share the same technical knowledge with providers. Therefore, it would be useful to let a customer express his thoughts and intentions. For this reason, customer’s intentions analysis has become a major contemporary challenge with the relentless growth of the IT market. With an approach for automatically detecting customer’s intention from a free text, it would be possible for a provider to understand the client’s needs and, consequently, the detected intention which may serve as a useful input for recommendation engines. This paper describes an automatic approach that populates an ontology of intentions from client’s textual request in the IT market space. This approach is based on an ontology structure that models the clients’ intentions. It takes an English written request as input and produces an intention ontology instance as output by the means of many combined NLP (Natural Language Processing) techniques. The population process is mainly based on a set of linguistic rules. Moreover, a certainty factor is assigned to each rule serving later as a degree of membership to the concept instantiated with the rule. The empirical evaluation confirms the interesting performance when evaluated on the customers’ requests through a database (available at this link: https://sites.google.com/view/customer-request-dataset) specialized in an IT domain.
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Labidi, N., Chaari, T., Bouaziz, R. (2018). Linguistic Rules for Ontology Population from Customer Request. In: Thanh Nguyen, N., Kowalczyk, R. (eds) Transactions on Computational Collective Intelligence XXX. Lecture Notes in Computer Science(), vol 11120. Springer, Cham. https://doi.org/10.1007/978-3-319-99810-7_4
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