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Conversational BI: an Ontology-Driven Conversation System for Business Intelligence Applications

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Conversational BI: an Ontology-Driven Conversation System for Business Intelligence Applications Conversational BI: An Ontology-Driven Conversation System for Business Intelligence Applications Abdul Quamar1, Fatma Ozcan¨ 1, Dorian Miller2, Robert J Moore1, Rebecca Niehus2, Jeffrey Kreulen2 1IBM Research AI, 2IBM Watson Health 1ahquamar|fozcan|[email protected], 2millerbd|rniehus|[email protected] ABSTRACT Business intelligence (BI) applications play an important role in the enterprise to make critical business decisions. Conversational interfaces enable non-technical enterprise us- ers to explore their data, democratizing access to data signif- icantly. In this paper, we describe an ontology-based frame- Business Model work for creating a conversation system for BI applications (Cube Definition) termed as Conversational BI. We create an ontology from a business model underlying the BI application, and use this ontology to automatically generate various artifacts of the conversation system. These include the intents, entities, as well as the training samples for each intent. Our approach builds upon our earlier work, and exploits common BI ac- RDBMS cess patterns to generate intents, their training examples Figure 1: Traditional BI System Architecture and adapt the dialog structure to support typical BI op- erations. We have implemented our techniques in Health etc. Figure 1 shows a typical architecture of a BI stack. Insights (HI), an IBM Watson Healthcare offering, provid- The underlying data resides in a traditional RDBMS, and ing analysis over insurance data on claims. Our user study a business model is created in terms of an OLAP cube def- demonstrates that our system is quite intuitive for gaining inition [13] that describes the underlying data in terms of business insights from data. We also show that our ap- Measures (numeric or quantifiable values), Dimensions (cat- proach not only captures the analysis available in the fixed egorical or qualifying attributes), and the hierarchies and application dashboards, but also enables new queries and relationships between them. Then, business analysts create explorations. the BI reports and dashboards using the BI model (cube def- inition) 1. The reports and the dashboards are supported by PVLDB Reference Format: structured queries that run against the underlying database ¨ Abdul Quamar, Fatma Ozcan, Dorian Miller, Robert J Moore, to render the visualizations to the user. Rebecca Niehus and Jeffrey Kreulen. An Ontology-Based Conver- sation System for Knowledge Bases. PVLDB, 13(12): 3369-3381, To obtain answers to questions that are not contained in 2020. the existing dashboard visualizations, users need to enlist DOI: https://doi.org/10.14778/3415478.3415557 the help of technical people, and the turnaround time for such cycles can be prohibitively time-consuming and expen- 1. INTRODUCTION sive, delaying key business insights and decisions. Today's enterprises need faster access to their KPIs and faster deci- Business Intelligence(BI) tools and applications play a key sion making. role in the enterprise to derive business decisions. BI dash- Conversational interfaces enable a wide range of personas boards provide a mechanism for the line of business owners including non-technical line of business owners and exec- and executives to explore key performance metrics (KPIs) utives to explore their data, investigate various KPIs, and via visual interfaces. These dashboards are usually created derive valuable business insights without relying on external by technical people. In fact, there are many technical peo- technical expertise to create a dashboard for them. As such, ple involved in the pipeline from the data to the dashboards, conversational interfaces democratise access to data signif- including the database designers, DBAs, business analysts. icantly, and also allow dynamic and more intuitive explo- rations of data and derivation of valuable business insights. This work is licensed under the Creative Commons Attribution- Today's chatbot and voice assistant platforms (e.g., Google NonCommercial-NoDerivatives 4.0 International License. To view a copy Dialogflow, Facebook Wit.ai, Microsoft Bot Framework, IBM of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. For Watson Assistant, etc.) allow users to interact through nat- any use beyond those covered by this license, obtain permission by emailing ural language using speech or text. Using these platforms, [email protected]. Copyright is held by the owner/author(s). Publication rights developers can create many kinds of natural language in- licensed to the VLDB Endowment. Proceedings of the VLDB Endowment, Vol. 13, No. 12 1 ISSN 2150-8097. In this paper, we use the terms cube definition and business DOI: https://doi.org/10.14778/3415478.3415557 model interchangeably 3369 terfaces (e.g., chatbot, natural language search, etc.) for the insights from existing visualizations as well as discov- any kind of domain (e.g., weather, music, finance, travel, ering new and useful insights that are not covered by the healthcare, etc.). These custom or domain-specific natu- pre-defined dashboards through the dynamic generation of ral language assistants usually target a range of domain structured queries and integration with the underlying BI specific tasks, such as booking a flight, or finding a drug platform. dosage. Such task-oriented agents limit the scope of the in- The main contributions of this paper can be summarized teraction to accomplishing the task at hand and hence are as: more tractable to design and build. However, these task- oriented agents fail to address the challenges involved in • We propose an end-to-end ontology-based framework, iterative data exploration through conversational interfaces and tools to create a conversation service for BI appli- to gain information and derive meaningful insights. cations. Recently, several business intelligence tools, such as Ask • We create an ontology from a business model, captur- Data Tableau [2], Power BI [8] by Microsoft, Microstrat- ing all the key information for the BI application, in- egy [6], and the IBM's Cognos Assistant [3], also explored cluding measures, dimensions, dimension hierarchies, exploiting natural language interfaces. These early systems and their relationships. have many restrictions in terms of the conversational in- teraction they provide, as they rely on the user to specify • We exploit common BI access patterns and use the on- several parameters, and only offer a fixed set of patterns. tology to generate several conversation space artifacts There are several challenges in creating a conversational automatically, including intents, entities, and training interface for a BI application. The first challenge is creat- examples. ing a data model that captures the entities, and their rela- tionships and associated semantics that are relevant to the • We adapt the dialog structure to support the BI Ac- underlying data and the common set of BI queries and op- cess patterns and operations to provide an intuitive erations. We have two options: Modeling the underlying conversational interaction for BI applications. data in the RDBMS, or modeling the cube definition. We chose the latter, because a cube definition provides impor- • We implement and demonstrate the effectiveness of our tant BI specific information, such as measures, dimensions, proposed techniques for Health Insights, an IBM Wat- dimension hierarchies, and how they are related. son Healthcare offering. The second challenge is building the necessary capability of the conversation system to capture user intent, recognize The rest of the paper is organized as follows. Section 2 and interpret the different workload access patterns. We ex- provides a brief overview of our ontology-driven approach for plore three different approaches, which we explain in detail building conversational interfaces for BI applications. Sec- in Section 3.3. The first two approaches use only the infor- tion 3 describes in detail our approach for data modeling mation available in the ontology, capturing the structural and generation of conversational artifacts including intents, relationships between measures and dimensions. The third entities and dialog. We discuss the implementation of our approach also takes into account user's access patterns. proposed techniques in a healthcare use case Health Insights The third and the final challenge is the integration with in Section 4 and provide a detailed system evaluation in Sec- the underlying BI platform to issue appropriate structured tion 5. We discuss related work in Section 6 and conclude queries and render the intended visualizations. in Section 7. In this paper, we explore the use of conversational in- terfaces for BI applications. In earlier work [22], we de- 2. SYSTEM OVERVIEW veloped an ontology-based approach to developing conver- In this section we provide a brief overview of our ontology sational services to explore the underlying structured data driven approach to building a conversational BI system for sets. In particular, we developed techniques to bootstrap deriving useful insights from data in different domains. the conversation work space in terms on intents, entities, and training samples, by exploiting the semantic informa- 2.1 Ontology-driven approach tion in an ontology. In this paper, we extend that work In our prior work [22], we demonstrate the viability of us- for BI applications. In particular, we observe that users ing an ontology-based approach for building conversational follow certain BI patterns and operations when analyzing systems for exploring
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