Ciencias Exactas y Ciencias de la Salud

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Design and implementation of a Chatbot for answering questions on scientometric indicators
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-05-23) López Rodríguez, Víctor Iván; HERNANDEZ GRESS, NEIL; 21847; Ceballos Cancino, Héctor; puemcuervo; Hernández Gress, Neil; Alvarado Uribe, Joanna; Juárez Ibarra, Erika Alejandra; Garza Villarreal, Sara Elena; School of Engineering and Sciences; Campus Monterrey
Scientometrics is the field of study and evaluation of scientific measures such as the impact of research papers and academic journals. It is an essential field because nowadays, different rankings use key indicators for university rankings, and universities themselves use them as Key Performance Indicators (KPI). The first objective of this research work is to propose a semantic model of scientometric indicators by generating a statistical ontology that extends Statistical Data and Metadata Exchange (SDMX). We develop a case study at Tecnologico de Monterrey following the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. We evaluate the benefits of storing and querying scientometric indicators using linked data in Neo4j to provide flexible and quick access knowledge representation that supports indicator retrieval, discovery, and composition based on a self-knowledge strategy. The semantic representation can answer a simple query using dimensions, query returning values with time intervals, aggregation functions such as average and standard deviation, and calculate a new scientometric indicator with data stored in the ontology. The second objective of this research work is to integrate the proposed statistical ontology model of scientometric indicators in a chatbot. Building a chatbot requires the use of Natural Language Processing (NLP) as a capability for recognizing users' intent and extracting entities from users' questions. We proposed a method for recognizing the requested indicator and transforming the question expressed in natural language into a query to the semantic model. The chatbot and the ontology model represent a novel framework that can answer questions from the Research Office about scientometric indicators. The chatbot is evaluated in terms of Goal Completion Rate (GCR). It measures how many questions the chatbot answered correctly and correctly identifies intent and entity extraction. The second evaluation approach of the chatbot is a survey that focuses on usability, the strictness of language variations, chatbot comprehension, correlation in chatbot responses, and user satisfaction. The main contribution of this research is the structural representation of the type of question that can be performed over the indicators modeled with SDMX. We simplify the model training and interpretation of questions by defining complexity levels and extracting entities from the question. We demonstrate how a chatbot can answer questions about any indicator modeled with SDMX. The chatbot can be trained to recognize another way to formulate questions without impacting the semantic representation of the indicators. The model is scalable because we can add more indicators using RDF, and the chatbot will only require minor changes (e.g., adding new dimensions).
Use of collaborative filters to recommend information in a chatbot system: Tecnologico de Monterrey Admissions Chatbot
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-06) Vázquez Cetina, Emmanuel; Ceballos Cancino, Héctor Gibrán; puelquio, emipsanchez; Hernández Gress, Neil; Garza Villarreal, Sara Elena; Escuela de Ingeniería y Ciencias; Campus Monterrey; Alvarado Uribe, Joanna
One of the main objectives of companies is to provide customers with a good customer service experience, so that customers are satisfied. Therefore, with the emergence of natural language processing techniques, companies are looking for automated solutions that provide quality services to customers. This is possible thanks to chatbots, which are helpful because they are permanently available and respond immediately. Additionally, with the use of recommendation systems, suggestions can be provided to the user, allowing a better conversation flow and reducing the response time. This research main objective is the development of a recommendation system for a conversational chatbot of online customer service of the ITESM admission department to suggest the following question to the user. In this project, a framework for a hybrid recommendation system is proposed, considering the user connection variables in each conversation, as user features, and applying an (Latent Dirichlet Allocation) LDA in the set of options provided by the chatbot to capture the context of the conversation as item features. In state-of-the-art, a problem similar to ours was found; this consists of recommending the following question that a user of the StackExchange platform can answer, using user characteristics and question labels to create different models. The results found that using a LightFM model, a maximum precision of 0.750 was obtained. In contrast, with our data set, a maximum precision of 0.787 is obtained, indicating that this model works well in our problem.

Ciencias Exactas y Ciencias de la Salud

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