Natural Language Interface to Relational Database (NLI-RDB) Through Object Relational Mapping (ORM)

The book is a timely report on advanced methods and applications of computational intelligence systems

Experimental study of socio chatbot usability

Máster en Investigación e Innovación en Inteligencia Computacional y Sistemas InteractivosBackground: The use of chatbots especially with ability of natural language processing (NLP) has increased considerably in recent years. These chatbots are being used in different areas and by a wide variety of users. Due to this variety of users, it is essential to incorporate usability in the development of chatbots. Objective: The objective of this research is to firstly identify the state of the art of the chatbots usability and the applied human-computer interaction (HCI) techniques and analyze how to evaluate the chatbots usability, secondly to evaluate the usability of chatbot SOCIO (which helps user to create class diagram), as well as the quality of the class diagram realized. Method: For the aim of this research, the literature was first reviewed for the purpose of specifying the state of the art in the usability of chatbot. To this end, a systematic mapping study (SMS) was be conducted through Scopus, ACM Digital Library, IEEE Xplorer, SpringerLink and ScienceDirect using a predefined search strategy. In addition, the articles and the results were analyzed. Finally, from the point of view of 18 teams of three members each, the usability of the web application CREATELY and chatbot SOCIO as well as the quality of the class diagrams obtained at using these tools were compared by executing a within-subjects cross-over design experiment. Each of 18 teams realized two class diagrams about a college and an online store by using chatbot SOCIO and CREATELY. Results: The search retrieved 170 papers and 19 are retained as primary studies. There are few papers reviewed the chatbots usability. A proposal of usability of chatbot is proposed. Compared with CREATELY, usability of the chatbot SOCIO is evaluated in aspects of efficiency, effectiveness, satisfaction and quality. The results of this experiment indicate that the chatbot SOCIO has a positive effect on the effectiveness, efficiency and satisfaction of the participants when they create the class diagrams, as well as its quality. Conclusions: We categorized according to four criteria: usability techniques, usability characteristics, research methods and type of chatbots. The chatbots usability is an emerging field of research, where the published studies were mainly survey, usability test, and experiments. The results of the experiment executed indicate that chatbot SOCIO performed better than CREATELY in aspects of effectiveness, efficiency and satisfaction when team making the class diagram as well as the quality of class diagrams

Aneesah: a novel methodology and algorithms for sustained dialogues and query refinement in natural language interfaces to databases

This thesis presents the research undertaken to develop a novel approach towards the development of a text-based Conversational Natural Language Interface to Databases, known as ANEESAH. Natural Language Interfaces to Databases (NLIDBs) are computer applications, which replace the requirement for an end user to commission a skilled programmer to query a database by using natural language. The aim of the proposed research is to investigate the use of a Natural Language Interface to Database (NLIDB) capable of conversing with users to automate the query formulation process for database information retrieval. Historical challenges and limitations have prevented the wider use of NLIDB applications in real-life environments. The challenges relevant to the scope of proposed research include the absence of flexible conversation between NLIDB applications and users, automated database query building from multiple dialogues and flexibility to sustain dialogues for information refinement. The areas of research explored include; NLIDBs, conversational agents (CAs), natural language processing (NLP) techniques, artificial intelligence (AI), knowledge engineering, and relational databases. Current NLIDBs do not have conversational abilities to sustain dialogues, especially with regards to information required for dynamic query formulation. A novel approach, ANEESAH is introduced to deal with these challenges. ANEESAH was developed to allow users to communicate using natural language to retrieve information from a relational database. ANEESAH can interact with the users conversationally and sustain dialogues to automate the query formulation and information refinement process. The research and development of ANEESAH steered the engineering of several novel NLIDB components such as a CA implemented NLIDB framework, a rule-based CA that combines pattern matching and sentence similarity techniques, algorithms to engage users in conversation and support sustained dialogues for information refinement. Additional components of the proposed framework include a novel SQL query engine for the dynamic formulation of queries to extract database information and perform querying the query operations to support the information refinement. Furthermore, a generic evaluation methodology combining subjective and objective measures was introduced to evaluate the implemented conversational NLIDB framework. Empirical end user evaluation was also used to validate the components of the implemented framework. The evaluation results demonstrated ANEESAH produced the desired database information for users over a set of test scenarios. The evaluation results also revealed that the proposed framework components can overcome the challenges of sustaining dialogues, information refinement and querying the query operations