<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>A prototype of a question answering (QA) system, called Farseer, for the real-time calculation and dissemination of aggregate statistics is introduced. Using techniques from natural language processing (NLP), machine learning (ML), artificial intelligence (AI) and formal semantics, this framework is capable of correctly interpreting a written request for (aggregate) statistics and subsequently generating appropriate results. It is shown that the framework operates in a way that is independent of a specific statistical domain under consideration, by capturing domain specific information in a knowledge graph that is input to the framework. However, it is also shown that the prototype still has its limitations, lacking statistical disclosure control. Also, searching the knowledge graph is still time-consuming.</p>
</abstract>

A prototype of a question answering (QA) system, called Farseer, for the real-time calculation and dissemination of aggregate statistics is introduced. Using techniques from natural language processing (NLP), machine learning (ML), artificial intelligence (AI) and formal semantics, this framework is capable of correctly interpreting a written request for (aggregate) statistics and subsequently generating appropriate results. It is shown that the framework operates in a way that is independent of a specific statistical domain under consideration, by capturing domain specific information in a knowledge graph that is input to the framework. However, it is also shown that the prototype still has its limitations, lacking statistical disclosure control. Also, searching the knowledge graph is still time-consuming.

Towards Demand-Driven On-The-Fly Statistics

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Towards Demand-Driven On-The-Fly Statistics"}

A prototype of a question answering (QA) system, called Farseer, for the real-time calculation and dissemination of aggregate statistics is introduced. Using...

Towards Demand-Driven On-The-Fly Statistics

Online veröffentlicht: 07. Sept. 2023

Seitenbereich: 351 - 379

Eingereicht: 01. Feb. 2021

Akzeptiert: 01. Jan. 2023

DOI: https://doi.org/10.2478/jos-2023-0016

Schlüsselwörter
Dissemination, artificial intelligence, question answering, text-to-SQL, information modeling

© 2023 Tjalling Gelsema et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Towards Demand-Driven On-The-Fly Statistics

Online veröffentlicht: 07. Sept. 2023

Seitenbereich: 351 - 379

Eingereicht: 01. Feb. 2021

Akzeptiert: 01. Jan. 2023

DOI: https://doi.org/10.2478/jos-2023-0016

SchlüsselwörterDissemination, artificial intelligence, question answering, text-to-SQL, information modeling

© 2023 Tjalling Gelsema et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Dissemination, artificial intelligence, question answering, text-to-SQL, information modeling