SENTENCE SIMILARITY MEASURES TO SUPPORT
WORKFLOW EXCEPTION HANDLING
A. Aldeeb, D. M. Pearce, K. Crockett and M. J. Stanton
Department of Computing and Mathmatics, Manchester Metropolitan University
Chester Street, Manchester, M1 5GD, U.K.
Keywords: Workflow Management System, Exception Handling, Case-based Reasoning, Sentence Similarity
Measures.
Abstract: Exceptions occurrence in workflow systems is common. Searching in the past exceptions handlers’ records,
looking for any similar exception serves as good sources in designing the solution to resolve the exception
at hand. In the literature, there are three approaches to retrieve similar workflow exception records from the
knowledge base. These approaches are keyword-based approach, concept hierarchies approach and pattern
matching retrieval system. However, in a workflow domain, exceptions are often described by workflow
participants as a short text using natural language rather than a set of user-defined keywords. Therefore, the
above mentioned approaches are not effective in retrieval of relevant information. The proposed approach
considers the semantic similarity between the workflow exceptions rather than term-matching schemes,
taking account of semantic information and word order information implied in the sentence. Our findings
show that sentence similarity measures are capable of supporting the retrieval of relevant information in
workflow exception handling knowledge. This paper presents a novel approach to apply sentence similarity
measures within the case-based reasoning methodology in workflow exception handling. A data set,
comprising of 76 sentence pairs representing instance level workflow exceptions are tested and the results
show significant correlation between the automated similarity measures and the human domain expert
intuition.
1 INTRODUCTION
A workflow management system (WFMS) is
essentially a set of tools for modelling, enactment,
and monitoring of business processes (Jablonski and
Bussler, 1996). Workflow process definition
(workflow schema) is the formal representation of a
business process (Casati et al., 2000). The workflow
schema is composed of activities (tasks) that
collectively achieve the business goal. Workflow
tasks are performed by workflow participants
(Human or automated agent) according to their roles
and the structure of the organization.
It is not guaranteed that designers always do a
perfect job in defining a workflow type that totally
represents all properties of the underlying business
process (Hwang et al., 1999). In addition, the IT
infrastructure of the WFMS and external factors can
raise problems. Therefore, the occurrence of
workflow exceptions is unavoidable and there is a
need to handle those exceptions efficiently. Rule-
based reasoning (RBR), Model-based reasoning
(MBR) and case-based reasoning (CBR) are
approaches being used to handle exceptions in
workflow systems (Luo et al., 2003; Hwang et al.,
2005). Workflow exceptions may require human
intervention to establish proper handlers. Those
handlers can be stored in a knowledge base to be
used to handle similar exceptions in future in case of
no available rules to handle them. Searching the
exceptions handlers’ records in the knowledge base,
looking for any similar exception serves as good
sources in designing the solution to resolve the
exception at hand. (Luo et al., 2003; Montani, 2009;
Hwang et al., 1999; Schmidt and Vorobieva, 2008;
Grigori et al., 2001; Aldeeb et al., 2008). This can
be achieved by applying CBR methodology to
support the management of exceptions in business
process execution. The main challenge in applying
CBR to support exceptions handling in workflow
systems is how to represent exceptions as cases,
finding an effective retrieval mechanism of similar
256
Aldeeb A., Pearce D., Crockett K. and Stanton M. (2010).
SENTENCE SIMILARITY MEASURES TO SUPPORT WORKFLOW EXCEPTION HANDLING.
In Proceedings of the 12th International Conference on Enterprise Information Systems - Artificial Intelligence and Decision Support Systems, pages
256-263
Copyright
c
SciTePress
cases and the calculation of the similarity. Current
applications of CBR in workflow exception handling
use keyword-based retrieval system, concept
hierarchies, and pattern matching and the use of
decision trees to retrieve similar cases from the case
database (Luo et al., 2003; Montani, 2009; Hwang et
al., 1999; Schmidt and Vorobieva, 2008; Grigori et
al., 2001). Some of these approaches depend on
matching individual words in the current exception
description with individual words in the textual
material in the case base. On the other hand, the
concept hierarchies approach is based on defining a
concept of similarity and incorporates the notion of
concept hierarchies. A concept hierarchy is a partial
order of concepts, which indicates general-to-
specific ordering where each case attribute has its
own concept hierarchy. However, in a workflow
domain, exceptions are often described by workflow
participants as a short text using natural language
rather than structural patterns of sentences. The
workflow participants may express the same
exception using quite different sentences in terms of
structure and word content because of the diversity
in human word usage. In addition, the same word
can have different meanings. Therefore, irrelevant
information may be retrieved and the relevant
information may be missed. Some approaches
restricting the allowable vocabulary, use
intermediaries to generate indexing and search keys,
or constructing explicit models of relevant domain
knowledge. However, these approaches lack the
flexibility to support the diversity in word usage of
human, require expert users to generate indexing and
search keys. These shortcomings and the limitations
require an alternative approach which needs to
consider the semantic similarity between the
workflow exceptions rather than term-matching
schemes. This motivates us to investigate the area
of semantic sentence similarity measures and their
potential application in workflow exception handling
(Li et al., 2006; Feng et al., 2008; Aminul and
Inkpen, 2008; Aliguliyev, 2009; Landauer et al.,
1998).
This paper presents a novel approach to the
application of sentence similarity measures within
the CBR methodology to handle instance level
workflow exceptions. A case study of the motor
insurance process is used to prove the concept of our
approach. The initial findings are encouraging and
show that sentence similarity measures can be
applied in the retrieval of relevant information in
workflow exception handling in the knowledge base.
The rest of this paper is organized as follow:
Section 2 discusses exception handling in workflow
systems. Section 3 introduces some sentence
similarity measures used in this research. In section
4, CBR as a methodology in the proposed approach
is presented while section 5 illustrates a proof of
concept prototype and case study. Finally, section 6
concludes and mentions some enhancements
foreseen as a future work.
2 EXCEPTIONS HANDLING
IN WFMS
WFMSs are designed to follow standard business
processes and routine. However, these processes
face the need to handle exceptions that fall outside
the normal control flow (Casati et a., 2000).
Exceptions occur commonly in workflows (Kumar
and Wainer, 2005; Sadiq et al., 2005; Hwang and
Lee, 2005). Workflow exception is any deviation
from an ideal collaborative process that uses the
available resources to achieve the task requirements
in an optimal way (Klein and Dellarocas, 2000).
There are four main causes of business process
exceptions: system errors, data issues, external
factors, and process design (Kelly, 2005). System
errors can be independent of the transaction data and
business logic and can be caused by underlying
system problems, such as servers being down or
services that are not available. Data issues can be
missing, invalid or inconsistent data. External
factors can trigger a process exception, such as that a
specific item is out-of-stock or unavailable. Lastly,
there can be process design issue that raise
exceptions when specific cases need non-standard
treatment for business reasons. Handling those
exceptions depends on their type, severity and at
what level they occur. Possible approaches to handle
exceptions include ignore, retry, partial roll-back
followed by forward execution, add some extra
activities, delete some planned activities, or any
change to the part of the workflow definition that not
executed yet (Hwang et al., 1999).
Some workflow exceptions can be anticipated by
the workflow designer, therefore they are called
expected exceptions. However, others can not be
anticipated and they are called unexpected
exceptions. The expected exceptions are handled by
rule-based reasoning. Those rules are characterised
by the following components (Casati et a., 2000;
Luo, et al., 2003):
The Event part represents the symptoms of an
exception
SENTENCE SIMILARITY MEASURES TO SUPPORT WORKFLOW EXCEPTION HANDLING
257
The Condition is a boolean statement that checks
if the symptoms is really an exception
The Action describes the procedures that must be
invoked to deal with the exception.
However, relying on predefined rules sometimes
is not enough to deal with the unexpected workflow
exceptions caused by ad-hoc changes. In this case,
human intervention may be required to establish an
appropriate handler. The successful exception
handler can be stored for the future to deal with
similar exceptions. Therefore, case-based reasoning
can be applied to handle workflow exceptions by
retrieving the similar exceptions handlers in the
knowledge base. As we mentioned in the previous
section, workflow exceptions are often described by
workflow participants as a short text (sentence)
using natural language rather than a set of user-
defined keywords. This makes the process of
building concept hierarchies and generating index
keys of instance level workflow exceptions
complicated. Table 1, shows an example of instance
level workflow exceptions. Comparing those
exceptions using sentence similarity measures
directly is more practical, save time and effort that
will be spend in building concept hierarchies and
indexing keys. In the next section, sentence
similarity measures are discussed.
3 SENTENCE SIMILARITY
MEASURES
Sentence similarity measures have many
applications, for example, Web page retrieval, text
mining to discover unseen knowledge from textual
database (Atkinson et al, 2004), text summarization
(Erkan, and Radev, 2004), text categorization (Ko et
al., 2004) and machine translation (Liu and Zong,
2004). Similarity computation techniques designed
to detect the similarity between long texts are
centred in shared words because similar long texts
usually have a degree of co-occurring words.
However, in short texts of sentence length, word co-
occurrence may be rare or even null (Li et al., 2006)
because people express similar meaning using quite
different sentences.
The Latent Semantic Analysis (LSA) is one of
the active researches in sentence similarity
computation and information retrieval is (Landauer
et al., 1998; http://lsa.colorado.edu/). LSA is based
on statistical information of words in huge corpus.
In LSA approach, a semantic space is automatically
constructed for retrieval. The basic postulate is that
there is an underlying latent semantic structure in
word usage data that is partially hidden or obscured
by the variability of word choice. A statistical
approach is utilized to estimate this latent structure
and uncover the latent meaning. Words, the text
objects and, later, user queries are processed to
extract this underlying meaning and the new, latent
semantic structure domain is then used to represent
and retrieve information. A set of representative
words needs to be identified from a large number of
contexts (each described by a corpus). A word by
context matrix is formed based on the presence of
words in context. The matrix is decomposed by
singular value decomposition (SVD) into the
product of three other matrixes, including the
diagonal matrix of singular values. The diagonal
singular matrix is truncated by deleting small
singular values. In this way, the dimensionality is
reduced. The original word by context matrix is then
reconstructed from the reduced dimensional space.
Through the process of decomposition and
reconstruction, LSA acquires word knowledge that
spreads in context. When LSA is used to compute
sentence similarity, a vector for each sentence is
formed in the reduced dimension space; similarity is
then measured by computing the similarity of these
two vectors. Because of the computational limit of
SVD, the dimension size of the word by context
matrix is limited to several hundred. As the input
sentences may be from an unconstrained domain
(and thus not represented in the contexts), some
important words from the input sentences may not
be included in LSA dimension space
Li et al. (2006) proposed a method named
(STASIS) that can be used generally in applications
requiring sentence similarity computation. This
method is fully automatic and adaptable across a
range of potential application domains. The
proposed method dynamically forms a joint word set
only using all the distinct words in the pair of
sentences. Then, for each sentence:
A raw semantic vector is derived with the using a
lexical database.
A word order vector is formed for each sentence
using information from lexical database.
The significance of the words and their
contribution to the sentence meaning is weighted
using information derived from corpus.
By combining the raw semantic vector with
information content from the corpus, a semantic
vector is created for each of the two sentences.
Semantic similarity is computed based on the
two semantic vectors.
An order similarity is calculated using the two
order vectors.
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Finally, the sentence similarity is derived by
combining semantic similarity and order
similarity.
In the evaluation phase of the STASIS, a set of
sentence pairs are collected from a variety of articles
and books in computational linguistics. An initial
experiment on this data illustrates that the proposed
method provides similarity measures that are fairly
consistent with human knowledge (Li et al., 2006).
Both LSA and STASIS measures are used in
implementation of the proposed approach. The next
section illustrates the case-based reasoning
methodology in handling workflow exceptions and
applying sentence similarity measures in the
retrieval phase.
4 CASE-BASED REASONING
METHODOLOGY
CBR is a reasoning paradigm that exploits the
specific knowledge of previously experienced
situations, called cases, to learn and generate
hypotheses about new situations (Montani, 2009;
Shiu and Pal, 2004a). The use of CBR can reduce
the amount of effort needed to formalize the
knowledge, since representing a real world situation
as a case is often simple. The use of CBR facilitates
an automatic acquisition and increases the operative
knowledge, without requiring a hard and time
consuming formalization of knowledge itself, as it
required by other methodologies, such as rule-based
or model-based reasoning (Montani, 2009).
However, rule-based and model-based reasoning are
more effective for applications where theory, not
experience, is the primary guide to problem solving
and the solutions are designed for a specific problem
and are difficult to be adapted (Limam et al., 2003).
A case consists of problem description and case
solution. CBR can therefore be described by the
CBR-cycle which comprises four activities (Watson,
1999):
1- Retrieve similar cases to the problem
description
2- Reuse a solution suggested by a similar
case
3- Revise or adapt that solution to better fit
the new problem if required
4- Retain the new solution once it has been
confirmed or validated
For complicated real world applications there are
some degree of fuzziness and uncertainty that almost
always encountered (Shiu and Pal, 2004b). AI
techniques such as fuzzy logic, neural networks and
genetic algorithms are helpful in areas where
uncertainty, learning and knowledge inference are
part of the system application (Shiu and Pal, 2004a;
Pal et al., 2004; Jeng and Liang, 1995; Pal and Shiu,
2004). In this research we suggest the sentence
similarity measures be part of the CBR to support
cases similarities calculation and retrieval phase of
the CBR-cycle.
Our approach is to maintain records about the
past experience of handling exceptions. Those
records form cases in the knowledge base to be used
to handle exceptions which need to be managed in
similar way, but may occur in different workflow
instances. The structures of the cases that represent
exceptions in the knowledge base are described in
the next section.
4.1 Exception Representation as a Case
The structure of the workflow exception cases is
inspired by and adapted from the work of Hwang et
al. (1999). Each workflow exception case consists of
three main components; Exception Information
Block (EIB), Exception Handler Block (EHB), and
Workflow Instance Block (WIB).
The EIB represents the problem description part
of the case and contains the following main
exception features or attributes:
Exception Description: Is the semantic
information that describes the exception by the
workflow user. This description takes the form of
short text (sentence length).
Status: Is the status of the workflow instance that
raises the exception e. g., initiated, active,
suspended, terminated or complete.
Workflow Participant: is the performer of the
activity that raises the exception. This can be
automated agent or human.
Workflow version: represents to which version
of the workflow schema the affected workflow
instance belongs.
Time: indicate when the exception occurs
Frequency: is a number indicates how many
times this exception case is successfully used.
When this number reaches a certain threshold
value, it will trigger the evolution of the process
model.
The EHB represents the solution part of the case
and indicates the action to be taken to handle the
exception. This can be an automated action or
manual action requiring user intervention. Generally,
the action can be under one of the following
categories:
SENTENCE SIMILARITY MEASURES TO SUPPORT WORKFLOW EXCEPTION HANDLING
259
Maintaining the workflow normal behaviour e.
g.: Ignore record, notify or add resource.
Modifying the workflow behaviour e. g.: retry,
suspend, modify, remove, change sequence,
terminate, re-assign and delay.
Modify the workflow schema e. g. making a new
version and the affected workflow instance will
follow the new version.
The third block, WIB, contains the workflow
instance itself which consists of the data and control
attributes of the workflow. The control attributes are
used by the exception handler.
4.2 The Exception Handling
Mechanism
The exception handling procedure is as follows:
The workflow participant propagates the
exception raising case to the Exception
Handling Coordinator (EHC) in the WFMS
accompanied by brief event description (short
text of sentence length, 15 to 25 words). This
sentence is the semantic information that
describes the type of the exception.
Upon receiving the exception case, the EHC
creates a temporary case template (TCT). The
TCT contains two blocks; workflow instance
information block (WIB) and exception
information block (EIB).
The event description in the TCT will be
compared with the exception description
attributes in the case database using sentence
similarity measures. Similar cases will be
retrieved based on a certain similarity rang
established by the human domain expert.
The most similar case with the highest overall
matching mark (based on an established
threshold value defined by the domain expert) is
chosen and its solution will be applied with or
without adaptation.
If no adaptation the case usage frequency field
is incremented and the TCT will be deleted.
In case of adaptation, a new case will be created
by adding the modified exception handler block
(EHB) to the TCT and storing it as a new case
in the case base
If there is no similar case found, the TCT will
be stored as a new case in the case base and
EHB will be added. The new EHB is based on
the judgement of the process engineer and the
domain expert
Case database maintenance will be performed
regularly merging highly similar cases or
removing unused cases.
As we mentioned above, the retrieval of similar
cases depends on the sentence similarity measure
between the current exception description and the
exceptions descriptions in the case database. In
addition, some CBR approaches e. g. (Weber and
Wild, 2005) use conversational scheme
(question/answer) with workflow participant to find
the best match between the case at hand and a
number of similar cases in case database. As these
questions and answers are short texts (sentence
length), again sentence similarity measures will be
very useful in retrieval of the best match. In next
section, a prototyping of the system and a case study
are presented.
5 A CASE STUDY
Car insurance claims handling is a process which
needs to be automated and managed efficiently and
be adaptable to the changing circumstances.
However, a fully automated solution for claim
processing cannot handle exception claims and does
not have real-time situational awareness capabilities.
Therefore, an effective exception handling
mechanism is needed. This is achieved by a
dedicated exception handling coordinator which can
be part of the workflow server or a separate node
connected with the workflow administrator.
To better illustrate how the proposed exception
handling mechanism works, a prototype of workflow
management system is developed to automate a
motor insurance claim process. The process consists
of 11 tasks involving 4 workflow participants
representing four roles in the insurance company.
These roles are: claim team, legal team, finance
team and technical team. Figure 1 shows the motor
insurance claim process at the modelling phase in
Petri notation. The workflow management system is
implemented in Java, and relies on Microsoft Access
database.
To prove the concept presented in this paper the
WFMS prototype carries out its run-time function
including the exception handling routine which
includes:
a) Instantiating a number of workflow instances
(claims) and coordination of tasks between the
workflow participants is practised
b) Exceptions are generated by the workflow
participants in the running instances and
exception handling procedures mentioned early
in the paper are examined.
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0
0.2
0.4
0.6
0.8
1
1.2
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73
workflow exceptions pairs
similarity
Figure 2: Exceptions pairs’ similarity measurement using
LSA.
0
0.2
0.4
0.6
0.8
1
1.2
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73
workflow exceptions pair
similarity
Figure 3: Exceptions pairs’ similarity measured by human
domain expert
6 CONCLUSIONS
Workflow exceptions are often described by
workflow participants as a short text (sentence
length) in natural language. Therefore, sentence
similarity measures can be used to compute the
similarity between the exception at hand with the
exceptions stored in case database in order to find
the proper exception handler. This paper presented a
framework to apply semantic sentence similarity
measures within case-base reasoning paradigm in
workflow exception handling. We believe that
sentence similarity measures are capable techniques
in helping retrieve appropriate cases. This research
provides the starting point in the semantic sentence
similarity application in WFMSs. As the sentence
similarity measurement techniques are still active
research area, we expect new enhancements to those
techniques which will provide more accurate results.
We concentrated in this paper on presenting the
ideas, the concepts, the architecture and the initial
experimental results of our approach. Further work
and publication will include the following:
Statistical and mathematical modelling of the
proposed approach and the obtained results to
compute the accuracy and the optimization of
our measurements
Study the effect of the noise (noisy sentence
which includes missed words, grammatical error
and spelling error) and how the similarity
measurement techniques cope with this
Apply the proposed approach to different
business processes from different domains.
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