A Semantic Question Answering in the Domain of Smart Factories

EasyChair Preprint 3005

Abstract

Industrial manufacturing has become more interconnected between smart devices such as the industry of things edge devices, tablets, manufacturing equipment, and smart phones. Smart factories have emerged and evolved with digital technologies and data analytics in manufacturing systems over the past few years. Basically, smart factories make complex data enables digital manufacturing and smart supply chain management and enhanced assembly line control. However, the more data created by smart factories, the harder it is for human operators and experts to understand the meaning of data because of the readability of machine-readable data. Nowadays, smart factories produce a large amount of data that needs to be apprehensible by human operators and experts in decision making. However, linked data is still hard to understand and interpret for human operators, thus we need a translating system from linked data to natural language or summarize the volume of linked data by eliminating undesirable results in the linked data repository. In this work, we propose a semantic question answering that can understand and interpret linked data repository in the domain of a smart factory. We have used heterogeneous RDF Turtle datasets from one of the OPC UA Server which is connected to the Fraunhofer IWU edge devices, an annotated time-series data SPARQL endpoint and statically generated data from eniLINK linked data repository. The semantic question answering might interpret the data from open or closed domain questions, but rather we will examine the question answering system in a restricted smart factory domain with the above-mentioned data source. Lastly, we will perform qualitative and quantitative evaluation of the semantic question answering, as well as discuss findings and conclude the main points regarding our research questions.

Keyphrases: Industry 4.0, Information Retrieval, Natural Language Processing, Semantic Web, Web 3.0

@booklet{EasyChair:3005,
  author    = {Orçun Oruç and Uwe Aßmann},
  title     = {A Semantic Question Answering in the Domain of Smart Factories},
  howpublished = {EasyChair Preprint 3005},
  year      = {EasyChair, 2020}}