Voice-Based Data Analysis and Automated Knowledge Generation

Posted on May 26, 2025 in Computers

Automated Fun Fact Generation from Wikipedia

Introduction: Problem Definition

  • There has been recent interest in providing fun facts.
  • In this paper, the authors demonstrate how fun facts can be mined from superlative tables in Wikipedia.
  • The content is dynamic, meaning it is updated over time.

Key Contributions

  • The authors show how to identify a large number of relational tables and attributes within each table from Wikipedia that are an excellent source for generating fun facts.
  • They propose a templated approach that, when instantiated using table data, automatically generates many fun facts from a single table. This approach is reusable for any update to the table data.
  • They propose two general classes of templates, namely rank-ordered and distributional, that lead to interesting sentences when instantiated with table data.
  • They present a semi-automated method for turning structured facts from tables into natural language templates.
  • They propose a method for dynamically maintaining (table, template) pairings over time that gracefully handles table schema changes like column header renaming and column reorderings.
  • They present experiments demonstrating that the fun facts generated by their approach are interesting to users and preferable to those generated by existing approaches.

Methodology for Fun Fact Generation

  • They propose two general template view classes that, when instantiated with a specific entity, can generate interesting facts about the entity in relation to others.
  • The Rank-ordered view class describes how exceptional an entity is compared to other entities in a given set, with respect to some given ordered attribute.
  • The Distributional view class describes how exclusive an entity is compared to other entities with respect to membership of some given unordered attribute value.

Example Template Generation

This view can be used to generate the template:

  • [$entity] is the [$rank] [$superlative] [$entity_class]

For example, the superlative could be “tallest” and the entity class “buildings in the world.” This template can then be instantiated using a single row from a table, producing statements such as: “Shanghai Tower is the 2nd tallest building in the world.”

Inference Model for Phrasal Verb Components

Details on the inference model for phrasal verb components are discussed.

Dynamic Maintenance of Facticles

Pairing with the most recent version of each table is crucial for keeping facticles up to date, especially as rows get reordered.

Voice-Based OLAP: Query Evaluation and Result Vocalization

Problem Definition

  • The authors focus on the problem of answering OLAP (Online Analytical Processing) queries via voice output.
  • They present a holistic approach that combines query processing and result vocalization.

Introduction to Voice-Based Data Analysis

  • How can data be analyzed if a user is visually impaired? Almost all prior work on OLAP focuses on visual interfaces. The authors’ goal is to answer OLAP-style queries via voice output instead.
  • Assume a voice input from the user, such as: “How does the flight cancellation probability in New York depend on flight date and start airport?”
  • As their focus is on voice output generation, their system uses a simple, keyword-based method to translate voice input into an OLAP query, for example:
    SELECT avg(cp) FROM table WHERE airportState='New York' GROUP BY flightSeason, airportCity.
  • Then, it uses data sampling, user modeling, and optimal voice output planning to select a concise description of the query result.
  • In general, communication between users and computers is increasingly shifting towards speech interfaces (e.g., devices such as Google Home or Amazon Echo are designed primarily for voice interaction).
  • To generate voice answers, they exploit the following main ideas:
    1. Sampling: Due to conciseness constraints, they consider a space of relatively coarse-grained voice descriptions and evaluate them on small data samples.
    2. Prioritization: Based on Monte-Carlo Tree Search.
    3. Pipelining: Voice output is transmitted sequentially.

Key Contributions to OLAP Vocalization

  • They introduce the problem of OLAP vocalization. They present a speech grammar and an associated user behavior model.
  • They describe an approach for interleaved query evaluation and result vocalization. This approach uses sampling and pipelining and is tailored to the particularities of their scenario.