IEEE Systems, Man and Cybernetics Magazine - October 2021 - 32

Overview
In nuclear power plants, due to the continuous moves of
experienced personnel to different department or retirements,
a vast amount of expertise and systems-specific
knowledge is lost. This leads to longer training periods for
new employees and sometimes to delayed responses to
problems. The loss of expertise costs nuclear power plants a
large amount of money, as they have to invest in training
less experienced staff. This leads to indirect losses in
delayed or incorrect activities, in particular in reactor maintenance
and inspection activities. Moreover, the unavailability
of expert systems limits the ability of staff to perform
their tasks effectively. Retrieving information related to any
procedure or operation from an uncountable number of documents
consisting of countless iTexts is time-consuming.
Especially during an operation, searching and locating
desired instructions from iTexts has to be done quickly and
with accuracy or the entire operation may lead to failure. In
addition, there is no proposed effective way to integrate network
structure with the knowledge associated with iTexts,
with limitations to learn about different operations.
There are few technologies and tools that deal with
learning from text, such as Google's Natural Language AI,
LUIS of Microsoft, and so on. Much research [1]-[8] has
been done on this topic. Some popular methods include
knowledge-based systems, ontology-based learning, OpenIE-based
approaches, knowledge graph development from
texts, entity and entity relationship recognition techniques,
semantic network development, and similar knowledge
representation techniques. But these approaches have limitations
when considered in terms of iTexts. This is where
IERS is different from other tools and technologies. The
main techniques that were used in IERS are parts-of-speech
tagging of natural language processing, text-to-speech,
speech-to-text, and word-to-vector using neural networks
and use of domain knowledge for the identification and classification
of different terms and key phrases in iTexts.
IERS Details
IERS is implemented based on inputs and outputs where
multiple input documents are uploaded into IERS and
their contents transformed into HESN. The input can also
be voice or small text that is used to train IERS, which
could be about different information or instruction. To
capture human experience, an adaptive knowledge structure
called HESN is proposed, as shown in Figure 1. The
document is first uploaded using the user interface and
then sent to the Python server from where each text in the
document is read one by one. Parent text and child text
based on numbering in the document is identified. Parent
text means the header or operation title. Child text are the
procedures or steps to perform that operation. While
reading the document, IERS detects text type. For example,
it could be a regular text or text with bullet points, a
table with or without borders, or an acronym table section.
Based on text type, information is split into acronym
data (contains acronym), table data (contains all table
information with table title), and textual data (all other
Parent
Sentence
Child
Sentence 1
Tag 1
Class A
Class B
Tag 2
Tag 3
Class C
Class D
Ontology
Experience
a-i1
a-i2
Class-i
Rule-ij
p-ij1
p-ij2
Class-j
a-k1
a-k2
Figure 1. The HESN superstructure.
32 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE October 2021
Class-k
Rule-jk
p-jk1
p-jk2
a-j1
a-j2
Tag 4
Child
Sentence 2
Tag 5
Class E
Class F
Tag 6
Tag 7
Class G

IEEE Systems, Man and Cybernetics Magazine - October 2021

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IEEE Systems, Man and Cybernetics Magazine - October 2021 - Cover3
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