Okwufulueze D.O

Bio: Okwufulueze D.O is an academic researcher. The author has contributed to research in topics: Spatial query & Query language. The author has an hindex of 1, co-authored 1 publications receiving 8 citations.

An Algorithm for Solving Natural Language Query Execution Problems on Relational Databases

Abstract: There continues to be an increased need for non-experts interaction with databases. This is essential in their quest to make appropriate business decisions. Researchers have, over the years, continued to find a methodology that bridges the gap that exist between information need and users satisfaction. This has been the core in studies related to natural language information retrieval. In this paper, we understudy the existing methodology and develop a model to extend the proposition of (a) Bhardwaj et al where a MAPPER was developed and implemented on student database and (b) Nihalani et al. where an integrated interface was used on relational databases. We present a time saving executable algorithm that satisfies needed conditions required to retrieve results of natural language based queries from relational databases. Results of the experiment shows that the performance index of the algorithm is satisfactory and can be improved upon increasing the metadata table of the relational database. This is a sharp diversion from the keyword based search that has dominated most commercial databases in use today. The implementation was deployed in PHP and the retrieval time has compared favorably with earlier deployed models. We further propose the extension of this work in the areas of inculcating some fuzzy constraints to handle uncertainty and ambiguity which are inherent in human natural language.

Design and evaluation of a conversational agent for the touristic domain

Abstract: This paper focuses on the design and evaluation of SARA, a conversational agent for the touristic domain featuring a high number of different, unique characteristics: spoken dialogue interaction, dialogue orchestration, context dependent information, an animated avatar and support for different kind of dialogue types, i.e. chat, specific and general question-answering, task oriented dialogues. The agent has currently two implementations: as web client and as mobile phone application. The paper describes the modules and resources required for running the agent on both interfaces, as well as the evaluation results obtained from two assessment studies concerning the interaction design of these two agent interfaces. The feedback gathered from the studies will enable us to improve the applications in terms of service, performance and usability.

Automatic SQL Query Formation from Natural Language Query

Abstract: Natural language processing is a field of computer science concerned with the interactions between computers and human (natural) languages. It is becoming one of the most active areas in the interaction between human and computer. These include spoken language systems that integrate speech and natural language. It is an interdisciplinary research area at the border between linguistics and artificial intelligence, aiming at developing computer programs capable of human-like activities like understanding or producing texts or speech in a natural language, such as English or conversion of natural language in text or speech form to languages like SQL. The most important applications of natural language processing include information retrieval and information organization, machine translation. The goal of NLP is to enable communication between people and computers without resorting to memorization of complex commands and procedures. General Terms Natural Language Processing; SQL Query; SL4A; Android; Python.

Automated populates and updates personalized ontology with analysis result

Abstract: To work effectively, the semantic web make explicit resources via ontologies. Semantic web is intended to make the machines to progress information themselves by understanding its significance. Ontology offers a shared collective understanding of application domain in concise and consensual manner. Ontology serves as a knowledge base domain by instantiating its concepts and semantic query language retrieves the information from the domain. The problem addressed in this paper is the challenge of automated populating and SPARQL query generator ontology given the type of input and retrieve the mandatory information from the personalized ontology. We propose personalized automated ontology for Non-Governmental Organization (NGO) system using Top-down methodology. An NGO is legally organized corporations that function independently from any form of government developed by likely or authorized persons. We propose an algorithm to populate and update ontology constantly based on user input as the requirement changes over time. Note to Practitioners — We develop Ontology by gathered information about various NGO's and built in owl language using protege tool. The personalized NGO ontology is created by Top-Down Methodology. The users' natural language queries are automatically converted into equivalent SPARQL queries to retrieve information from ontology. To convert user query into SPARQL queries we used QuriOnto and Kuntt Moris Pratt algorithms. Using populate and update algorithm the NGO ontology can be modified constantly based on user input as the requirement changes over time. We develop event-based user interface for easy usage of ontology system. The personalized NGO ontology data are evaluated and result demonstrated in graphical forms. This System offers proper information for people approaching NGOs for fund raising or for benefit.

Semantic ambiguous query formulation using statistical linguistics technique

Abstract: Natural language query systems mitigate the complexity of structured query. Usually, natural language processing is implemented to solve several problems, such as information retrieval. However, problems such as natural language ambiguity remain unsolved due to the complexity of natural language itself. This issue thus requires further research. Recent studies on semantic query formulation have attempted to resolve ambiguous natural language by proposing different disambiguation approaches. Most such processes are either implemented manually or semi-automated. In the same vein, most recent systems solve ambiguity by using an external dictionary such as WordNet or by providing suggestions manually. The present research proposes a statistical linguistic technique for solving the problem of ambiguity automatically. The proposed technique is experimentally tested on a Quran ontology with queries from the Islamic Research Foundation Website and increases the result of precision and recall by 6% and 10%, respectively.

Conversational interfaces for unconventional access to business relational data structures

Abstract: PurposeCurrent possibilities of accessing business data by regular users usually involve complicated user interfaces or require technical expertise. This results in situations when business owners are separated from their data. The aim of this research is to apply an innovative approach leveraging conversational interfaces to tackle this problem.Design/methodology/approachThe authors examine the current possibilities of accessing business data by business, users with an emphasis on conversational interfaces employing a chatbot as an alternative to traditional approaches. The authors propose a new concept relying on a guided conversation, and through experiments with a real chatbot and database, the authors demonstrate the benefits of the proposed approach.FindingsThe authors found out that the key to the success of our approach is a decomposition of complex database queries and their incremental construction in conversations. This also enables natural discovery of the domain model through constantly provided feedback. Based on the experiments with a real chatbot, the authors demonstrate that defining conversation flows and maintaining the conversation context is a crucial aspect contributing to the overall accuracy, together with keeping the conversation within the defined limits in its certain parts.Originality/valueThe authors present a novel approach using natural language interfaces for accessing data by business users. In contrast to existing approaches, the authors emphasize incremental construction of queries, predefined conversation flows and constraining the conversations, when necessary.