BINLI: An Ontology-Based Natural Language Interface for Multidimensional Data Analysis

Current technology facilitates access to the vast amount of information that is produced every day. Both individuals and companies are active consumers of data from the Web and other sources, and these data guide decision making. Due to the huge volume of data to be processed in a business context, managers rely on decision support systems to facilitate data analysis. OLAP tools are Business Intelligence solutions for multidimensional analysis of data, allowing the user to control the perspective and the degree of detail in each dimension of the analysis. A conventional OLAP system is configured to a set of analysis scenarios associated with multidimensional data cubes in the repository. To handle a more spontaneous query, not supported in these provided scenarios, one must have specialized technical skills in data analytics. This makes it very difficult for average users to be autonomous in analyzing their data, as they will always need the assistance of specialists. This article describes an ontology-based natural language interface whose goal is to simplify and make more flexible and intuitive the interaction between users and OLAP solutions. Instead of programming an MDX query, the user can freely write a question in his own human language. The system interprets this question by combining the requested information elements, and generates an answer from the OLAP repository

Semantic Networks and Spreading Activation Process for QA improvement on text answers

Question Answering (QA) systems try to find precise answers to natural language questions. QA extraction result is often an amount of text candidate answers which requires some validation and ranking criteria. This paper presents an automatic answer appreciation technique where extracted candidate answers are represented in a question dedicated associative knowledge base, a semantic network. A spreading activation algorithm looks for semantically related candidate answers, that reinforce each other. The purpose is to enhance the best answers by rising their weight. This article concludes with evaluation details for an experiment with text answers to Portuguese questions