A Semantic Question Answering Framework for Large Data Sets

Traditionally, the task of answering natural language questions has involved a keyword-based document retrieval step, followed by in-depth processing of candidate answer documents and paragraphs. This post-processing uses semantics to various degrees. In this article, we describe a purely semantic question answering (QA) framework for large document collections. Our high-precision approach transforms the semantic knowledge extracted from natural language texts into a language-agnostic RDF representation and indexes it into a scalable triplestore. In order to facilitate easy access to the information stored in the RDF semantic index, a user's natural language questions are translated into SPARQL queries that return precise answers back to the user. The robustness of this framework is ensured by the natural language reasoning performed on the RDF store, by the query relaxation procedures, and the answer ranking techniques. The improvements in performance over a regular free text search index-based question answering engine prove that QA systems can benefit greatly from the addition and consumption of deep semantic information

Ten Ways of Leveraging Ontologies for Rapid Natural Language Processing Customization for Multiple Use Cases in Disjoint Domains

With the ever-growing adoption of AI technologies by large enterprises, purely data-driven approaches have dominated the field in the recent years. For a single use case, a development process looks simple: agreeing on an annotation schema, labeling the data, and training the models. As the number of use cases and their complexity increases, the development teams face issues with collective governance of the models, scalability and reusablity of data and models. These issues are widely addressed on the engineering side, but not so much on the knowledge side. Ontologies have been a well-researched approach for capturing knowledge and can be used to augment a data-driven methodology. In this paper, we discuss 10 ways of leveraging ontologies for Natural Language Processing (NLP) and its applications. We use ontologies for rapid customization of a NLP pipeline, ontologyrelated standards to power a rule engine and provide standard output format. We also discuss various use cases for medical, enterprise, financial, legal, and security domains, centered around three NLP-based applications: semantic search, question answering and natural language querying