Hima P. Karanam

Creation and interaction with large-scale domain-specific knowledge bases

The ability to create and interact with large-scale domain-specific knowledge bases from unstructured/semi-structured data is the foundation for many industry-focused cognitive systems. We will demonstrate the Content Services system that provides cloud services for creating and querying high-quality domain-specific knowledge bases by analyzing and integrating multiple (un/semi)structured content sources. We will showcase an instantiation of the system for a financial domain. We will also demonstrate both cross-lingual natural language queries and programmatic API calls for interacting with this knowledge base.

Visual Exploration of Unstructured Regulatory Documents

Governmental authorities publish rules and directives that govern the operations of an industry. These documents, called regulations, are meant to safeguard the interests of consumers. With increasing number, size and complexity of such documents, companies face an uphill task to comply with them. We present a cognitive system, called Cogpliance, for exploring and understanding regulatory documents with the goal of assisting compliance officers in attaining regulatory compliance. Cogpliance automatically reads natural language regulatory documents, extracts key concepts and presents an interactive information exploration user interface for answering compliance officers queries.

Tooling framework for instantiating natural language querying system

Recent times have seen a growing demand for natural language querying (NLQ) interfaces to retrieve information from the structured data sources such as knowledge bases. Using this interface, business users can directly interact with a database without the knowledge of the query language or the data schema. Our earlier work describes a natural language query engine called ATHENA which has several shortcoming around ease of use and compatibility with data stores, formats and flows. In this demonstration paper, we present a tooling framework to address these challenges so that one can instantiate an NLQ system with utmost ease. Our framework makes it easy and practically applicable to all NLIDB scenarios involving different sources of structured data, file formats, and ontologies to enable natural language querying on top of them with minimal human configuration. We present the tool design and the solution to the challenges towards building such a system and demonstrate its applicability in the medical domain. PVLDB Reference Format: Manasa Jammi, Jaydeep Sen, Ashish Mittal, Sagar Verma, Vardaan Pahuja, Rema Ananthanarayanan, Pranay Lohia, Hima Karanam, Diptikalyan Saha, Karthik Sankaranarayanan. Tooling Framework for Instantiating Natural Language Querying System. PVLDB, 11 (12): 2014-2017, 2018. DOI: https://doi.org/10.14778/3229863.3236248

Cognitive Compliance for Financial Regulations

Regulations are rules and directives published by authorities to safeguard consumer interest in an industry. Compliance with such regulations is getting increasingly hard due both to the complexity of these documents, which require experts to read, understand, and interpret them manually, and to the sheer volume of regulatory change. Many CFOs rate this as their top challenge. The authors’ Cogpliance platform uses a cognitive approach to achieve regulatory compliance. Here, they describe key compliance-related tasks and demonstrate how Cogpliance helps compliance officers to handle those tasks effectively.