Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Cognitive Approach based on the Actionable Knowledge Graph for
  supporting Maintenance Operations   In the era of Industry 4.0, cognitive computing and its enabling technologies
(Artificial Intelligence, Machine Learning, etc.) allow to define systems able
to support maintenance by providing relevant information, at the right time,
retrieved from structured companies' databases, and unstructured documents,
like technical manuals, intervention reports, and so on. Moreover, contextual
information plays a crucial role in tailoring the support both during the
planning and the execution of interventions. Contextual information can be
detected with the help of sensors, wearable devices, indoor and outdoor
positioning systems, and object recognition capabilities (using fixed or
wearable cameras), all of which can collect historical data for further
analysis. In this work, we propose a cognitive system that learns from past
interventions to generate contextual recommendations for improving maintenance
practices in terms of time, budget, and scope. The system uses formal
conceptual models, incremental learning, and ranking algorithms to accomplish
these objectives.

9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio