=Paper=
{{Paper
|id=Vol-2030/HAICTA_2017_paper37
|storemode=property
|title=An Inference Strategy for Knowledge Units
|pdfUrl=https://ceur-ws.org/Vol-2030/HAICTA_2017_paper37.pdf
|volume=Vol-2030
|authors=Michal Peták,Milan Houška
|dblpUrl=https://dblp.org/rec/conf/haicta/PetakH17
}}
==An Inference Strategy for Knowledge Units==
https://ceur-ws.org/Vol-2030/HAICTA_2017_paper37.pdf
An Inference Strategy for Knowledge Units
Michal Peták1, Milan Houška1
1
Department of Systems Engineering, Faculty of Economics and Management,
Czech University of Life Sciences Prague, Kamýcká 129, 165 21 Prague 6 - Suchdol
Czech Republic, e-mail: petak@pef.czu.cz, houska@pef.czu.cz
Abstract. Inference algorithm is an integral part of any expert system based on
and working with procedural knowledge such as production rules. In general,
also the production rules containing the compound statements in an antecedent
of a consequent part is well known and supported with knowledge or expert
systems. On the other hand, if a production rule is of an advanced internal
structure (is transformed into a knowledge unit), standard inference algorithms
(e.g. modus ponens or modus tollens) could provide insufficient results. The
aim of this paper is to suggest a modification of the inference algorithms to be
appropriate for working with knowledge units. The formal notification of the
algorithm is accompanied with a practical example in the domain of Enterprise
Resource Planning (ERP) system used for the support of a particular business
process.
Keywords: Knowledge unit, Inference mechanisms, Expert system,
Knowledge representation.
1 Introduction
Inference engines (methods, strategies, procedures) are present in expert systems
used in various problem domains. (Moreno and Espejo, 2015) present a performance
evaluation of three different inference engines (rule based reasoning, fuzzy based
reasoning and Bayesian based reasoning) for failure mode identification in shafts.
They compare different types of inference mechanisms to improve the expert system
and conclude that, under their conditions, the best inference mechanisms are
Bayesian and Fuzzy rules inference.
(Venturelli et al., 2017) propose a method to evaluate the efficiency of Corporate
Social Responsibility (CSR). The outcome of the application is a system designed to
measure the CSR identity of a company. The algorithm of the Fuzzy Expert System
aggregates multicriteria evaluations of a problem. The assessments of behavior and
the resulting decisions are represented in blocks of rules, drawn up by an inference
engine in fuzzy logic. The Fuzzy Expert System unites the ability of an expert system
to simulate the decision-making process with the uncertainty typical of human
reasoning, present in fuzzy logic. The aim is create a model using Fuzzy Expert
System approach, which serves to combine the intuition and the experience of the
experts who supply a knowledge base with the formal rigor of a logic system.
304
The expert system by (Chen and Pollino, 2012) employed the Bayesian Inferential
Network to map a suitable biotope for particular animal species (Juvenile Astacopsis
gouldi – giant freshwater crayfish of Tasmania). The results of the inference are
visualized with a Geographical Information Systems (GIS).
(Fakhrahmad et al., 2015) use the expert system to reduce ambiguity in automated
translation. This expert systemcombinestwo techniques: Forward chaining and Data
mining. Forward chaining Word Sense disambiguation (FchWSD) is very efficient
because it can disambiguate other ambiguous words existing in the context in
addition to the target ambiguous word in just one pass through the knowledge base.
The performance of the proposed system in terms of Recall and Precision was
encouraging compared to its counterparts.
Technical systems and their modernization or aging are being analyzed with
mathematical models Krejci (2013). Regardless, another expert system on
microstructural characterization of dual-phase steels was developed by (Ghanei et al.,
2015). Thisexpert systembased on an adaptive neuro- fuzzy inference system
(ANFIS) helps the human users with evaluation of material state: Evaluation of non-
destructive control.
Expert systems are being used to simulate human decision-making process in
complex decision situations (Venturelli et al., 2017). For this purpose, they use
various kinds of knowledge representations. Mařík et al. (2004) distinguish the
procedural and declarative knowledge as follows: procedural knowledge means
“knowing how”, declarative knowledge means “knowing what”.
Knowledge unit is an extended procedural production rule with fixed internal
structure (Domeová et al., 2008, see Materials and Methods for its definition). In this
paper we propose an advanced inference algorithm suitable for a specific kind of
production rules – knowledge units. We show how to use the internal structure of the
knowledge unit to determine relationship among individual parts of the
precedingknowledge unit and the successing knowledge unit within the inference
process. The inference process is also demonstrated on a practical example –
particular process within the working with an ERP system.
2 Materials and Methods
2.1 Production Rules and Their Inference
Inference strategy, i.e. the strategy how the system operates the responses to the
questions, is the basic element of any expert system. In general, the inference engine
compares the facts in the base of facts with the knowledge, usually represented by
production rules. Production rules are an appropriate knowledge representation for
this purpose, because on one hand they are easy to be retrieved from human experts
and, on the other hand, are suitable for automated processing with expert systems.
Standard production rules (IF – THEN rules) are consisting of two parts:
antecedent (evidence, situation, problem) and consequent (hypothesis, action,
solution). The production rules formalized by the statement (Mařík et al., 2004)
305
E → H,
where E is an evidence and
H is a hypothesis,
are suitable for inferring in both direct and indirect forms. Direct inference is based
on the rule of “modus ponens” which is formalized as follows
E, E → H
,
H
and means that if evidence E and production rule E → H are valid, then hypothesis H
is also valid. (Gass and Harris, 2001) describe this forward chaining as an approach
to reasoning in which an inference engine determines the effect of current known
variable values on unknown variables by applying all rules whose premises can be
established as being true.
On the other hand, indirect inference can also be used. Indirect inference is expressed
by the rule of “modus tollens” and can formally be written as
¬H, E → H
¬E
which means that if the hypothesis H is NOT valid and the rule E → H is valid, then
the evidence E is also NOT valid. Backward chaining refers to an approach to
reasoning in which an inference engine endeavours to find a value for an overall goal
by recursively finding values for subgoals. Examining rule conclusions to identify
rules that could possibly establish a value for the goal is important for the effort of
finding a value for the immediate goal (Gass and Harris, 2001).
2.2 Knowledge Unit
For the purposes of this work we understand the knowledge unit as an enhanced
production rule containing the compound statement in the antecedent and simple
statement in the consequent part. As we showed in our previous work (Dömeová et
al., 2008), this kind of the production rules meet better the requirements of a
problem-oriented representation of an explicit knowledge.
Formally, we suggested to record knowledge unit as (Dömeováet al., 2008):
KU = {X, Y, Z, Q},
where X stands for a problem situation,
Y stands for the problem being solved in the X problem situation,
Z stands for the objective of solving the elementary problem,
Q stands for a successful solution of the elementary problem (result).
306
Even though there is no unique way to create sentences based on the production rules
(Kendal and Creen, 2007), we may always express the knowledge unit in the
following textual form (Dömeová et al., 2008):
“If we want to solve an elementary problem Y in the problem situation X to reach the
objective Z, then we should apply the solution Q.”
Another advantage of the knowledge unit concept is that it allows to define unary
and operations with knowledge units such as drill down or roll up (unary operations)
or merging or decomposition (binary operations, see Dömeová et al., 2008) with no
influence to the complexity of the expressions of the knowledge units in natural
languages (Rauchova et al., 2014). These operations could serve as the start point for
thinking about the modifications of the inference mechanisms.
3 Results and Discussion
3.1 Inference Chain with Knowledge Unit
To be operable, any knowledge unit could be formally expressed as a production rule
in the following form:
IF (X and Y and Z) THEN Q.
This allows to define the relationships among elements of the knowledge units within
the basic inference method. The basic form of elementary inference is defined as
follows:
Let's have two knowledge units
KU1 = {X1,Y1,Z1,Q1}
and
KU2= {X2,Y2,Z2,Q2}.
Further suppose that the statement IF (X and Y and Z) THEN Q is valid. Then the
inference with knowledge units can proceed from element Q1to Y2, thus
KUINF = {Q1} ==> {Y2}
In the language form the operation could be written as follows:
“IFweare not able to apply the solution Q1, it becomes the elementary problem
Y2solved within the problem situation X2 to reach the objective Z2; THENthe
solution Q2 should be applied."
The inference mechanism with knowledge unit is described in Fig. 1.
307
Fig. 1. Inference with knowledge unit
3.2 Case Study
The illustrative example deals with the problem "Application user error analysis" is
occurred within the working with an ERP system. The objective is to find errors in
the application or find the application, where the error occurred. The error can be
vaguely identified by the expert. In this case knowledge units are optimized to
knowledge level of an operator on first line helpdesk support. This level was
explicitly defined by the expert. For the case study, it is designed for the following
scenario:
An error occurred in the application "SharePoint Nintex Workflow" with
elementary describe the error as follows: "One column requires a different type of
information". This type of error may have origin in several integrated systems. First
step is the diagnosis in application where error occurred. The result of first step is the
identification application where the error occurred. The second step is diagnosis,
which define the module or object, where is the original error in application. And
after finding the error is next step to fix the error. The purpose of inference is to find
the error in origin application and make first analysis. Boundary of the knowledge
domain is the issue of diagnostic errors in the context of integrated applications.
Design of the expert system will be similar like in the case of authors (Moreno and
Espejo, 2015) and it is diagnostic. As the starting point for the inference the
knowledge units developed according to the procedure defined in Houska and
Rauchova (2013) is used.
308
KU1
• X1 Purchase (under the statutes)
• Y1 Create purchase request
• Z1 Provide the delivery for the project
• Q1 Fill out the request form of purchase request and send
KU2
• X2 Solving an error in purchase request workflow
• Y2 Find application, where is error
• Z2 Start analytical test application
• Q2 Identify an error application
We can also KU2 describe: "If we want to solve the error in the “purchase request
workflow” and find which applications it is an error, then we must run the analytical
test in application where error occurred and identify an error application."
KU3
• X2 Detection error in application
• Y2 Repair error or data inconsistency
• Z2 Ensure correct operation of the system
• Q2 Make a correction in the object
KU4
• X4 Repair in the object KS systemization
• Y4 Detect true values and process consistency
• Z4 Correct classify employee
• Q4 Repair / Correction
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Fig. 2. Inference with knowledge unit in “Application user error analysis”.
In this Fig. 2. are knowledge units fill with concrete values during the inference. This
case study, demonstrate the inference chaining used for finding errors in application
Workflow purchase. The error message stands at the top of the figure. The inference
with knowledge units defined location of the error and make result for correction.
After correction, continues the process with re-initiating the purchase request. In the
KU1, 2, 3 and 4 are concrete knowledge unit for this case:
KU1
• X1 Purchase (under the statutes)
• Y1 Create purchase request
• Z1 Provide the delivery for the project
310
• Q1 Fill out the request form of purchase request and send = Error state:
„One column requires a different type of information”
KU2
• X2 Solving an error in purchase request workflow
• Y2 Find application, where is error ("Q1" = "Y2" ==> "One column
requires a different type of information")
• Z2 Start analytical test application
• Q2 Identify an error application = "KS systemization"
KU3
• X2 Detection error in application
• Y2 Repair error or data inconsistency ("Q2" = "Y3" ==> "In the KS
systemization)"
• Z2 Ensure correct operation of the system
• Q2 Make a correction in the object = “Repair systemization tree”
KU4
• X4 Repair in the object KS systemization
• Y4 Detect true values and process consistency ("Q3" = "Y4" ==>
"Repair systemization tree)
• Z4 Correct classify employee
• Q4 Repair / Correction = "Correction value"
The inference with knowledge unit is defined like special operation between more
than one knowledge units. In the real case study is illustrated the inference
mechanism with crisp knowledge unit. The case study has a real ground, of the
simple problem and it solve problem in few integrated software application in
corporation. This case study helps in diagnosis applications problems-errors and is
designed for first level of support helpdesk operators.
3.3 Comparison with a Standard Inference Procedure
Base of facts:
• User (employee) not valid, "NAV"
• Systemization number not valid, "KS"
• Wrong data, "ActiveDirectory = AD"
• One column requires a different type of information, "Error"
• Purchase request, "Error", "NAV"
• Systemization, "KS", Wrong number
• Object employee, "NAV", Wrong department
• Employee <=50%
• More AD ID
• Wrong AD tree
• Purchase request, "Error", "AD"
• Purchase request, "Error", "KS"
311
Base of rules:
• Rule 1 If "User (employee) not valid, "NAV"" Or "Systemization number
not valid, "KS"" Or "Wrong data, "AD"" Then "One column requires a different type
of information"
• Rule 2 If "Purchase request, "Error", "KS"" Then "User (employee) not
valid, "KS""
• Rule 3 If "Systemization, "KS", Wrong number" Then "Purchase request,
"Error", "KS""
• Rule 4 If "Purchase request, "Error", "AD"" Then "Wrong data, "AD""
• Rule 5 If “More AD ID" Or "Wrong AD tree" Then "Purchase request,
"Error", "AD""
• Rule 6 If "Purchase request, "Error", "NAV"" Then "Systemization,
"NAV", Wrong number"
• Rule 7 If "Object employee, "NAV", Wrong department" Then "Purchase
request, "Error", "NAV""
Fig. 3. Standard inference procedure “Backward chaining”
312
Within the standard inference, the consequent is derived with a sequential chaining of
the production rules using the basis of the facts. When inferred knowledge units,
individual knowledge units (or their specific parts, respectively) are merged for
deriving the consequent, or even another knowledge unit. The main difference
between the standard approach and our new one approach lays on the definition of
the basis of the facts. In the standard approach, the set of the rules and basis of the
facts are strictly separated. On the contrary, knowledge units cover both the
production rule and additional information (the problem situation and the solution of
the elementary problem (antecedent in a standard production rule)); thus some facts
are already integrated directly into the knowledge unit.
Moreover, as the structure of the knowledge units is standardized, it predetermines
the order of the knowledge unit by production rules. Just this way of the order allows
to base the inference on the facts integrated directly in the units. The inference
among two or more knowledge units stems from this principle - the fixed
interrelation between the elements of the knowledge units "Q1" è "Y2"
4 Conclusion
In this paper we proposed a way to process a knowledge unit as an enhanced
production rule with an inference engine in an expert system. In particular, backward
chaining inference algorithm was used. Our approach contributes to decreasing the
entropy in the chaining when the production rule consists of compound statements in
antecedent or consequent part of the production rule. Of course, the next necessary
step is to show the application of our algorithm on more particular examples and
compare the success rates of a standard inference algorithm and this new one. The
user-oriented point of view could be used for this purpose (Berankova et al., 2008).
In case of success new opportunities for future research are opened. First, other
inference strategies (e.g. forward chaining) could be modified too to be suitable for
inferring the knowledge units. Furthermore, under uncertainty, fuzzy expert systems
based on fuzzy knowledge units need the inference strategy for fuzzy knowledge
units too.
Acknowledgements. The research is supported by the grant project of the Internal
Grant Agency of the FEM CULS Prague “Data, information and knowledge in expert
systems”, No. 20171026.
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