Download extending office systems to manage administrative knowledge

A Representation Scheme for Managing Complex Knowledge
Chandra S. Amaravadi1
1
School of Computer Sciences, Western Illinois University, Macomb, USA
[email protected]
Abstract. A representation scheme called CKR-1 is introduced to deal with the challenges of representing
complex knowledge. Complex knowledge is defined as deep knowledge concerning a complex object, event,
situation or process. The nature of complex knowledge is identified from samples of knowledge drawn from
the insurance industry. These examples are used to highlight the efficacy of the representation scheme as
well as its limitations. CKR-1 is a type of semantic network that supports class/sub-class and several types of
relationships. It derives its expressivity from ability to support abstractions, elaborations and alternative
points of view. Despite this, the scheme suffers from limitations stemming from the difficulty in expressing
abstract concepts.
Keywords: knowledge representation, complex knowledge representation, domain engineering, knowledge
engineering, knowledge management, insurance industry.
1
Introduction
The basis for intelligent behavior in knowledge-based systems lies in the explicit representation of knowledge. But
despite decades of research, knowledge representation remains a standing challenge in the field. Initially, researchers
in the ‘70’s and ‘80’s developed schemes such as frames, rules, logic and semantic networks.
These were
subsequently developed and refined in the context of domains such as natural language, qualitative physics,
medicine and software development [1]. The idiosyncracies of the domain dictated the type of representation
scheme employed. For e.g. Schank developed and used scripts for processing natural language. He introduced a
specialized language to describe actions in scripts such as “P TRANS P to POST OFFICE,” “P MBUILDS LINE
POSITION.” Since the objective of the representation is to achieve intelligent behavior in a specific domain, issues
in reasoning associated with these problems were also explored. In many cases, inferencing dominated the research
rather than the representation per se. Researchers attempted to address problems such as selective inheritance (or
subsumption), non-monotonic reasoning, belief revision, probabilistic reasoning and temporal reasoning [ibid]. In
recent times emphasis of researchers has shifted in many cases completely into procedural approaches with
emphasis on neural networks [2], case-based reasoning [3] and into multi-paradigm approaches utilizing perhaps
rules, frames and objects [4]. The semantics of the representation have not been fully addressed. Ontologies are
also a recent development and define terminologies and relationships in a specific domain. Their main purpose is to
facilitate knowledge sharing and interoperability of information systems [5] rather than deep reasoning. While
numerous intelligent applications have been developed, the emphasis has been on applying existing techniques in
new areas (see for example [6]). The conceptual and epistemic levels in Brachman’s terminology [7], i.e. the
primitives and their relationships have not been addressed adequately for the business domain.
Knowledge
engineering is still a challenge in this respect and especially more so for complex types of knowledge. We will
define complex knowledge (CK) as deep knowledge associated with a complex object, event, situation, idea or
process. Examples of such knowledge include ‘design knowledge,’ ‘decision knowledge,’ ‘legal knowledge’ etc. In
this research, we will focus on how to represent such knowledge. The knowledge should be of sufficient depth such
that an intelligent agent is able to answer questions about the domain from the resulting knowledge base.
2
Relevant Literature
Seminal work in knowledge representation was carried out in the ‘70’s and ‘80’s. Current literature in this area,
especially as it pertains to CK is sparse for reasons already mentioned. There seem to be two approaches to
knowledge engineering which can be thought of as “top-down” and “ bottom up.” The first is to identify models of
the domain and to implement them using a well known representation scheme [8]. This is the method primarily
used for Expert Systems [9]. ‘Pictorial Knowledge Representation’ for example, uses a domain model to represent
geometric figures such as ‘triangles’ and ‘polygons’. The figures are represented as ‘nodes’ in a classification
network with separate ‘property nodes’ describing properties such as “the sum of interior angles of a triangle must
be 1800” [10] . The scheme supports image recognition of geometric shapes.
In the second approach, the representation itself is part of an application or an environment. KL-One is the most
well-known of these [11]. KL-One arranges “concepts” in a classification hierarchy from the most general
(“thing”) to the most specific (e.g. “Red Ferrari” ). Classes and instances are referred as “general concept” and
“individual concept.” The individual concept needs to be linked to a general concept to be valid. Properties of
concepts are modeled with “roles” (e.g ‘color’ of a car) that are inherited from the parent concept (“vehicle”).
Concepts can be formed from more general concepts by restricting roles. Allowable fillers (“Role sets”) are defined
by the “Value Restriction” property of a role. For e.g. ‘part’ of a ‘car’ could be the role set ‘door’,‘engine’, ‘trunk’.
Thus an enhanced frame-based representation is employed to describe the structure of concepts. Relationships
between roles bind instances to specific roles e.g. ‘John’ as a ‘driver’ of the ‘red Ferrari’. The most interesting
feature from the point of view of CK are “Description Wires,” used to make assertions of the type “John drove the
Red Ferrari at the Grand Prix.” The assertions utilize sets of linkages between concepts for example, between
‘Ferrari’ and ‘car’ and between ‘race’ and ‘grand prix’ and are stored outside the representation. Despite its power,
the linkages between concepts and roles render it difficult to partition the resulting network and to comprehend the
knowledge in the knowledge base. Not surprisingly, recent work has shifted to the linguistic level, in the form of
ontologies.
KL-One has inspired other major and minor schemes including Telos [12] and KRS [13].
An
interesting feature of Telos is the representation of instances of an object and its properties as predicates e.g. [Car]
[Ferrari, red, john]. Recently, an extensive attempt has been made with multi-layered semantic nets to represent
natural language [14]. Multi-nets address the conceptual and epistemic levels by including an extensive set of
objects, events and relationships as well as constructs for representing quantification. The network is also
noteworthy in separating the intensional from the extensional representation. Despite its power, the representation is
unwieldy for business realms. In a different domain, Zarri proposed a representation language NKRL, whose basic
constructs are ‘events’ and ‘concepts’. ‘Concepts’ are objects that can occur in ‘events.’ ‘Events’ and ‘concepts’ are
placed in separate classification hierarchies. Events have a case-frame structure (e.g. ‘SUBJ’, ‘OBJ’, ‘TIME’) and
are manually entered from news stories. Some relationships between events are defined such as Event 1 causes
Event 2. The representation is utilized in information retrieval [15]. A similar scheme is proposed by Gomez who
uses event relationships (“sim” – simulataneous, “sprec” – strong precedence) to model complex processes [16].
Yet another approach, AEI-3 has been proposed recently. AEI-3 uses semantic networks and makes use of two node
types (“class,” “instance”) and two link types (“structural,” “descriptive.”) to represent large volumes of
administrative knowledge (for example, “Manugistics is a client of BSS” or “the van leaves BSS at 11:00 am.”)
[17].
It overcomes some of the traditional limitations of semantic nets such as tractability, but is a minimalist
design owing to the relative simplicity of administrative knowledge. The basic ability echoed in all schemes is to
model concepts and relationships with substantial variations in the way either are dealt with. A few methods include
propositions as well. Except for Multi-nets the conceptual and epistemic levels are not developed adequately.
3
The Nature of Complex Knowledge
There is a paucity of literature concerning the nature of complex knowledge. Lacking empirical evidence, we will
hypothesize some characteristics based on samples from a text book by [18]. In their foreword, they state “The
American Institute for Chartered Property Casualty Underwriters and the Insurance Institute of America are
committed to expanding the knowledge of professionals in risk management, insurance, financial services, and
related fields through education and research.” Thus their comments establish the rationale for using the text as an
example of complex knowledge. A few representative samples are illustrated in Table 1.
Table 1. Samples of Complex Knowledge [18]
Item#
1.
2.
3.
4.
5.
6.
7.
Example
Property includes real property and personal property. Real property is land, buildings and other property
attached to it. §1.6.
A liability loss exposure is any condition or situation that presents the possibility of a claim alleging legal
responsibility of a person or business for injury or damage suffered by another party. §1.6.
Types of insurers include stock insurers, mutual insurers and reciprocal exchanges. §1.11.
To be insurable, a loss should have a definite time and place of occurrence… § 1.8
Underwriting expenses include acquisition expenses, general expenses, premium taxes and licenses § 3.8
Depreciation is allowance for physical wear and tear or technological or economic obsolescence §6.14.
A contract of good faith is an obligation to act in an honest manner and to disclose all relevant facts §7.7.
Note: “§” refers to section numbers, there are no page numbers in the cited reference.
Individual instances of CK in the insurance domain appear to exhibit one or more of the following characteristics:
a)
They describe objects, events, actions, situations, concepts, objectives or policies (item #1, 3).
b) Objects are generally concrete such as “automobile,” “property,” and “underwriter.” Concepts are abstract
such as “loss,” “depreciation” and “indemnify.”
c)
They elaborate or define a concept as in item#2 and 5.
d) The elaboration imposes additional conditions or restrictions (item #4).
e)
Concepts involve other concepts or objects that may or may not be explicitly defined (item#3).
f)
Objects and concepts are related to other concepts/objects through structural, axiomatic, mathematical or
logical relationships (item#5).
The major challenge is to represent abstract concepts since they are complex, amorphous and often defined in terms
of other concepts/objects that are challenging to represent in a meaningful manner. Consider, “The board of
directors consists of elected officials.” The ‘board of directors’ (BOD) and ‘elected officials’ are complex concepts.
that are defined easily, but what about ‘elected officials’?.
4
Knowledge Engineering for Complex Knowledge
The objective of the representation is to serve as a foundation for knowledge-based systems and knowledgemanagement systems [17]. Since visual representations facilitate this task, we are committed to one that has a
graphical notation. Visual representations will be user friendly provided they are intuitive and not overly complex.
This places certain restrictions on the epistemology.
Secondly, the representation ought to provide sufficient
representational mechanisms or representational adequacy so that knowledge may be stored and queries, answered
[19]. Here, we will focus mainly only on intensional knowledge (definition) although it will be seen that the
knowledge can be readily used in inferencing and hence satisfying inferential adequacy [ibid]. We also assume that
syntactic elements can be enforced at the implementational level, so the current focus is on the logical and epistemic
levels. In view of the nature of CK we will impose further requirements on the representation scheme.
Complex concepts such as premium are defined in terms of other concepts such as ‘insurance coverage’ which may
themselves be complex. Therefore it is convenient to refer to high level concepts without having to redefine the
underlying concepts, leading to the requirement of supporting abstractions. The ability to demarcate concepts,
known traditionally as network partitioning [20], will ensure modularity. Another issue that arises is the multiplicity
of definitions. A single concept has alternative viewpoints making this a requirement as well. For example,
insurance could be described as a business or as an obligation to provide coverage. Relationships among concepts
can be simple (concrete) or complex (abstract). A class-subclass relationship is an example of a simple relationship.
Abstract relationships are complex because they are qualitative, involve multiple concepts and involve complex
conditions. Thus another requirement is to model both simple and complex relationships.
5
A SCHEME FOR COMPLEX KNOWLEDGE REPRESENTATION - CKR-1
We will refer to our scheme as CKR-1 in honor of KL-One [11] and in recognition of the fact that no solution is
likely to be complete or elegant. The scheme is in development. Following discussion from the previous section,
the scheme will be graphical and will have constructs to model objects, events, activities, situations, concepts and
associations.
Physical objects and concepts are not distinguished in CKR-1 – both are treated uniformly and are modeled by
rectangles with name of the concept/object as the label. Rectangles could also denote variables and these are
represented with the name of the variable starting with a capital letter as in “Value,” “Time.” We will use the
generic term ‘object’ to refer to objects and concepts, ‘event’ to refer to events, actions and situations and ‘concept’
to refer to objects and events. Events are represented by rectangles with a vertical bar towards the left and marked
as “E,” “A” or “S” to denote “events,” “actions,” or “situations” respectively. Because of the nature of CK, attributes
of objects are not given importance, so there are no extensive semantics here. Our philosophy is that there can be a
separate and independent mechanism at the implementation level supporting storage and retrieval of routine
properties of objects. ‘Interesting’ properties of objects are modeled by property links described subsequently.
Objects, concepts and events, situations and activities can be atomic or complex. A ‘disaster’ for e.g. is a complex
concept since it could be caused by fire, earthquake etc. and could result in property loss. On the other hand, ‘fire’
and ‘earthquake’ are simple concepts from the point of view of the domain. A special construct that is provided is a
“named” object or event which is a user-defined concept that is useful in concept definition as well as in modeling
abstractions. For example, loss could be defined as a situation where an object loses value. The same construct also
serves as a mechanism to partition the network as well as to express propositions.
There are two relationship types, ‘structural’ and ‘descriptive’. The structural relationships are “class-subclass,”
“part-subpart” and “has-a” (‘is-a,’ ‘p-sp’, ‘has-a’). Descriptive relationships are modeled with “rel: <name>” label
on the link where <name> is the name of the relationship. This allows for any arbitrary relationship without
semantic baggage. There are also standard relationship types such as logical relationships (‘GT’, ‘LT’..), Causal
(‘CL’), Temporal (‘TE+’ and ‘TE-‘), probabilistic (‘PR’), business relationships (example ‘payer payee’ – ‘PP’,
‘legal obligation’ – ‘OB’) and case relationships (‘Subj’, ‘obj’..) that are part of descriptive relationships. Structural
relationships are distinguished from descriptive only for the benefit of the user. Additionally, from the point of view
of inferencing, generic reasoning mechanisms would suffice for standardized relationships, but interpreters will need
to provide individual reasoning for non-standard relationships. Properties are modeled with a “p:” link. An asset
could have a property, “value” modeled with a “p: value” between “asset” and a variable denoting value (e.g.
“Value”). A required property is modeled with “rp:” link. Consider, the board of directors consists of elected
officials. Here the required property is that officers must be elected. To accommodate required properties, we will
use the “rp:” link type. Since a concept could be related to more than one concept, we use an oval to represent nonsignificant relationships between two objects and a double oval for conjunctive relationships. A dotted double oval
would mean the same thing for disjunctive relationships. Thus some logical propositions could be made with the
scheme. There is a special link “elaboration” or “e:”, that uses the same standard relationship types, used primarily
for elaborating on properties of a relationship. It also serves as a crude mechanism for conditions and quantification.
We will illustrate CKR-1 with examples of knowledge. For example a mortgaged asset could be defined as an asset
for which some percentage is owned by a bank i.e. rest is owned by the mortgage. As shown in Figure 1, mortgaged
asset is connected by “s:is_a” structural link to asset. It has the required condition that it should be partly owned by
a bank. There is an “rp: owned by” link between the mortgaged asset and the owners which in this case are both the
bank and mortgagee. Note the use of ovals for multiple arguments.
s:is_a
mortgaged
asset
asset
rp: owned
:
by
mortgagee
bank
s : is_a
individual
business
Fig 1. Modeling simple concepts with CKR-1
A more abstract concept such as insurance coverage is difficult to represent. Insurance coverage is the legal
obligation of an underwriter to compensate the insured in the event of a loss. The concept is challenging because it
involves a number of abstract concepts such as “legal obligation,” “compensate,” and “loss.” It needs to be
represented as three assertions:
‘Compensate-1’: Insured suffers loss. It is understood that the loss amount is quantified.
‘Compensate-2’: Underwriter compensates insured for the loss amount.
Coverage: Compensate1 and Compensate2.
The first two propositions are shown in Figure 2. ‘Compensate-1’ is represented as a user-defined ‘event type’. The
insured has a ‘relationship’ with ‘loss’ i.e. the ‘subject’ of the ‘loss’. The ‘loss amount’ is a required property for
this concept; it refers to a variable ‘Loss amount’. ‘Compensate-2’ is similar. ‘Underwriter’ and ‘Insured’ share a
‘Payer-payee’ relationship with an ‘elaboration’ link (‘e:’) on the relationship to show the compensated amount.
The reader must note that ‘loss’, ‘insured’ and ‘underwriter’ are complex concepts. ‘Loss’ will be represented as a
degradation of ‘state’ of an ‘entity’ - which could be a person, object, animal etc. This method while awkward,
neatly partitions concepts and keeps relationships manageable; it can be used for more complex concepts such as
‘salvage rights.’ However, concepts such as ‘liability insurance’ defined as the obligation to pay on behalf of the
insured for damage or injury to others’ property for which the insured is legally responsible will challenge the
representation.
COMPENSATE-1
rel: subj
E
insured
loss
rp: loss
amount
Loss amt.
COMPENSATE-2
rel: pp
E
insured
underwriter
E: payment
amount
Loss amt.
Fig 2. Modeling complex concepts with CKR-1
6
Conclusions
CKR-1 is an initial attempt to model complex knowledge in business domains. It derives its power from the ability
to define arbitrary concepts in terms of other concepts using the ‘object’ types, relationship types and the concept
definition facility. Given this power, it may be easy to define contradictory knowledge. Knowledge consistency is
therefore an issue. There was an explicit concern for simplicity to ensure that the scheme does not suffer from the
weight of its own features. It is for this reason that record type structures for objects and relationships among these
were not included. Properties of interest could be modeled with the “p:” and “rp:” links. Also we started out with a
limited set of constructs that could be subsequently expanded. For instance, there is no construct for a conditional
assertion. Being completely graphical it is expected that a system based on CKR-1 would be easy to comprehend
and at the same time easy to implement.
We have allowed object/event properties to be concepts.
At the
implementation level, the property of an object when modeled as a concept should be treated as an ‘alias’ or
‘derivation’ of the actual property. One could also conceive of an ‘extensional layer’ for the representation that has
assertions about instances of the definitions in CKR-1. All of these are obviously future directions for the research.
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