Int. J. Man-Machine Studies (1987) 26, 29-40
KRITON: a knowledge-acquisition tool for expert
systems
JOACHIM DIEDERICH, INGO RUHMANN AND MARK MAY
Research Division "Expert Systems ", Institute for Applied Information Technology, German Research Institute for Mathematics and Data Processing, Schlofl
Birlinghoven, Postfach 1240, D-5205 Sankt Augustin 1, West Germany
A hybrid system for automatic knowledge acquisition for expert systems is presented. The system integrates artificial intelligence and cognitive science methods to construct knowledge bases employing different knowledge representation forma- lisms. For the elicitation of human declarative knowledge, the tool contains automated interview methods. The acquisition of human procedural knowledge is achieved by protocol analysis techniques. Textbook knowledge is captured by incremental text analysis. The goal structure of the knowledge elicitation methods is an intermediate knowledge-representation language on which frame, rule and constraint generators operate to build up the final knowledge bases. The intermedi- ate knowledge representation level regulates and restricts the employment of the knowledge elicitation methods. Incomplete knowledge is laid open by pattern- directed invocation methods (the intermediate knowledge base watcher) triggering the elicitation methods to supplement the necessary knowledge.
1. Introduction
The KRITON system for knowledge acquisition is designed to meet the require- ments of practical knowledge engineering tasks. The starting point in developing the system was the assumption, that no single acquisition method will be powerful enough to overcome the so called knowledge-acquisition bottleneck in knowledge engineering. To fill that gap, it requires hybrid knowledge-acquisition tools, employing several knowledge-acquisition methods to capture different kinds of human knowledge. Within the domain of expert systems, two major knowledge sources are available, in principle:
(1) The human expert with his declarative and procedural knowledge of the domain in question. This knowledge has been obtained in long practice and is often turned to account without sufficient meta-knowledge about the way it is used. To model problem solving processes mounting on these, often incom- plete and unstructured, knowledge chunks is the task assigned to expert systems.
(2) Well-structured static knowledge, fixed in the traditional mode of knowledge representation: natural-language documents, text books, technical descrip- tions and instructions.
Depending on the actual application, all of the above mentioned knowledge sources may become important and a knowledge-acquisition tool should be able to meet these requirements.
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30 J. DIEDERICH E T A L .
The aim of the present paper is to put forward an integrated methodological approach, which takes into consideration different types of expert knowledge (declarative knowledge vs procedural knowledge) combining so far divergent methods to a modular knowledge acquisition system, each submethod being able to acquire information on specific aspects of the problem-solving process and to transform the gained information into a knowledge-representation formalism.
The KRITON-approach for automatic and semi-automated knowledge acquisition integrates methods of artificial intelligence with those of cognitive science. One of the important strategies of knowledge engineering is the interview, i.e. the dialogue between knowledge engineer and expert to inquire about important terms and concepts of an application domain (Newell & Simon, 1972) and their interdependence.
From cognitive science we adopted the method of protocol analysis, i.e. processing and transformation of texts gained by transcribing protocols of loud thinking during a problem-solving process. In AI, the analysis of thinking-aloud protocols has been automated quite early (Waterman & Newell, 1971, ]973).
The analysis of texts with respect to syntactic, semantic and pragmatic criteria also goes back to cognitive science. Although content analysis has developed into a standard method in the social sciences, it still represents a not much used option for knowledge acquisition on the basis of natural-language texts. KRITON uses a form of incremental text analysis to take advantage of these valuable knowledge sources. Figure 1 shows the basic architecture of the KRITON system. In short: thrce knowledge elicitation methods are employed, namely an automated interview, text analysis and protocol analysis. After a completion process and a consistency check
KRITON 31
the elicited information is transformed into an intermediate knowledge- representation language consisting of a descriptive language for functional and physical objects, and a propositional calculus.
Frame, rule and constraint generators operating on the intermediate repre- sentation level are finally used to build up the destination knowledge base. On the other side, the already acquired knowledge guides the employment of the elicitation methods to complete the knowledge bases incrementally.
2. Methods for knowledge elicitation
On its first processing level our model makes use of three different knowledge elicitation methods, to be outlined in this chapter.
2.1, INTERVIEW
One of the most important strategies of knowledge engineering is the interview. Grover (1983) distinguishes four different interview techniques for rule acquisition:
(1) Forward scenario simulation
An applicational situation within a problem domain is selected and investigated under laboratory conditions. The expert reports on the relevant terms and concepts and describes the steps in problem-solving, i.e. his or her own reasoning to achieve a goal.
(2) Goal decomposition
The knowledge engineer divides the overall problem into subgoals and asks the expert to describe paths for achieving the subgoals.
(3) Procedural simulation
Grover (1983) uses this umbrella term for protocol analysis. In his opinion controlling interventions by the knowledge engineer are absolutely necessary.
(4) Pure reclassification
Expert statements are further differentiated and classified into specific objects and relations between objects by means of a dialogue between knowledge engineer and expert. As a result of the interview, object-relations may be reclassified and new taxonomic relations eventually discovered. An interview techniques not mentioned in Grover's classification is:
(5) Laddering
The expert is asked to name important concepts of the problem domain in question. These concepts are then used as basis for the interview to follow. Especially supertypes and instances of generic concepts are inquired about, allowing the derivation of a taxonomic structure.
2. 1.1. Interview methods in K R I T O N
In the KRITON system, interview techniques are completely automated, that is to say, the expert interacts directly with the system. A combination of the repertory grid techniques forward scenario simulation and laddering is used to explore the relevant concepts of a problem domain.
The top-level technique is the repertory grid approach: triples of semantic related concepts are presented to the expert in form of natural-language sentences and the
32 J. DIEDERICH E T AL. expert is asked for attributes two of the concepts share distinguishing them from the third.
If the expert is not able to name discriminating attributes, the system switches into laddering mode to explore taxonomic relations between the concepts. The expert may either answer with a single word, denoting a specific concept, or can enter flee text, which is analysed through morphologic-syntactic techniques to detect the relevant concepts.
The interview produces structured objects at the intermediate knowledge repre- sentation level. These objects incorporate the explored taxonomic relations and attributes.
2.2. P R O T O C O L A N A L Y S I S
Protocol analysis refers to the automated or semi-automated analysis of thinking- aloud protocols, that is, tape-recorded utterances of an expert during a problem- solving episode. The result of the protocol analysis can be considered as a path through successive knowledge states representing the sequence of the problem solving events. In case that an expert system uses this sequence of knowledge states (e.g. in consultation) a surface modeling of the human problem-solving process takes place.
Although automatic protocol analysis has been suggested as an adequate method for knowledge acquisition in expert systems for some time now, fully developed systems are rare. A consistent approach to protocol analysis is described by Kuipers & Kassirer (1983, 1984), their approach aiming at both, a structural description of the problem domain and a qualitative simulation of the transitions between knowledge states during the problem solving process. A constraint language is used to fill up incomplete protocol segments. The power of protocol analysis quite decisively depends on the quality of the protocol recording. Only if the protocol is actually one of loud thinking during a problem-solving process and only if this protocol has been correctly transcribed, automatic analysis will be successful. As the success of any protocol analysis depends on the quality of the recorded information, detailed and adequate instructions with respect to the attainment of protocol recordings of verbal utterances during the problem-solving process are of great importance. In any case, it requires psychologically trained manpower to achieve a constant cognitive load of the thinking-aloud expert (for a comprehensive review on problems with verbal data, see Ericsson & Simon 1980, 1984).
Granularity of expert knowledge has turned out to be a serious and not easy to handle problem. Even the most careful employment of protocol analysis will not avoid problem-irrelevant knowledge elements to be acquired. As soon as not directly problem-relevant concepts are uttered by the expert, they are contained in the verbal material and hence are fed into the analysis. For example, this can be the case when the expert starts commencing upon, explaining or evaluating his thoughts or actions.
The other extreme, however, might as well occur, namely an expert communicat- ing his "compiled knowledge" to the system. That is to say, that the expert over his extended learning process has combined inference steps so that the verbal report on the problem-solving process is incomplete. The expert skips, more or less small,
KR|TON 33
nonetheless essential inference steps. Even if this does not necessarily affect the efficacy of the future expert system, it will reduce the explainability of the problem-solving process.
2. 2. 1. Protocol analysis in the K R I T O N system
Protocol analysis as a knowledge elicitation method is used in the KRITON system in order to get hold of procedural human knowledge. Ideally, knowledge that was part of text analysis or some previous interview is observed "in action" during the protocol recording. Goal structure of the protocol analysis is the propositional part of the intermediate knowledge representation level. In K R I T O N protocol analysis is accomplished in five steps. First the transcribed protocol is partitioned into segments on the basis of the experts speech pauses during recording. The second step is the semantic analysis of the segments, creating propositions for each segment. In a third step, the appropriateness of the selected operators and arguments is checked upon. Next, a knowledge-base matching is attempted to instantiate variables inside the propositions (variables are inserted if appropriate references for pronouns etc. cannot be found). In a last step, propositions are arranged according to their appearance in the natural language protocol.
2.3. TEXT ANALYSIS
Knowledge engineering phase models recommend the knowledge engineer to start off with studying manuals and documents on the problem domain in question. This can be very time-consuming, particularly if the knowledge engineer is supposed to become an expert on the topic before beginning his or her actual work.
For about 40 years content analysis has been concerned with analysing texts, especially newspaper articles. Since the 1950s, programs for automatic content analysis are available (Krippendorff, 1980; Merten, 1983). Utilization of these methods for constructing knowledge-based systems have, in the best case, been outlined in the published literature. Nishida, Kosaka & Doshita (1983), for example, analyse hardware manuals by means of action-event models. Frey, Reyle & Rohrer (1983) use discourse representation structure (DRT, by H. Kamp) as "intermediate level" between the natural-language text (a fragment of German language) and a data basis.
2. 3. I. Incremental text analysis in K R I T O N
KRITON supports the knowledge engineer in incremental content analysis. The knowledge engineer can ask for statistical information on keyword frequencies in a selected text. If a text seems expedient for knowledge acquisition, the user can define the size of a text-fragment surrounding the keywords, to be used for the generation of basic propositions in a similar manner to that in protocol analysis.
The resulting propositional structures are sometimes faulty and therefore not appropriate for inference processes. The goal structures as part of the intermediate knowledge representation are to be constructed in an interactive process, where possible objects and relations are presented to the user in a menu and window system. Appropriate items can be selected by mouse operations and the correspond- ing knowledge structures are set up.
34 J. D I E D E R I C t l E T AL.
3. Utilization of acquired knowledge
The employment and use of the already acquired knowledge has major advantages and is an important task for knowledge acquisition tools. In KRITON, the already captured knowledge is used in several ways, depending on the amount and quality of the existing knowledge.
Moreover, existing knowledge is completed by acquisition knowledge bases
(AKBs) for better guidance of the ongoing acquisition process. These acquisition knowledge bases are viewed as an integral part of the KRITON system. In every stage of the acquisition process, the user can use these knowledge bases in addition to existing knowledge for better employment of the KRITON facilities for knowledge-based knowledge acquisition. AKBs contain a set of structured objects defining important concepts of the domain. They are predefined declarative deep models of a domain with the sole purpose of optimizing the ongoing acquisition process.
Depending on the richness and quality of the existing knowledge, the already acquired knowledge is used in the following ways:
guidance of the acquisition process through discovery of incompleteness (see also section 5);
completion of domain-dependent deep models (AKBs);
employment as an Interpretation Model for the discovery of new situations (see also Breuker & Wielinga, 1985).
4. Intermediate knowledge representation level
In our system, all output from the mentioned above techniques is translated into an intermediate knowledge-representation system. This representation system has two subparts: a descriptive language for functional and physical objects, representing the generic concepts, and a propositional calculus representing the transformation path of these concepts during the human problem-solving process.
The description language consists of structured objects, their features and interrelations in a semantic net. The semantic net is the goal language for the methods interview and text analysis and serves as the basis for the frame-generation process.
The second part of the intermediate knowledge representation language is a propositional calculus, using semantic primitives to describe the basic relations of concepts detected by protocol analysis. The set of semantic primitives is not complete and will have to be updated for each application domain (e.g. technical applications).
The intermediate knowledge representation level allows integration of different knowledge sources and supplies the tool with openness towards elicitation methods currently not available. Moreover, it can be used for the generation of various knowledge bases for different expert system shells and knowledge-representation systems taking advantage of the facilities of interactive knowledge-base generation.
KRITON 3 5
different purposes:
openness for extensions (possible integration of currently unknown elieitation methods);
knowledge acquisition for different knowledge representation tools; storage of incomplete knowledge for the ongoing elicitation process; integration and employment of
acquisition knowledge bases;
maintaining information closer to the sources (e.g. expert utterances);
management of knowledge bases with varying degrees of completeness in different knowledge representation languages.
5. Dealing with incomplete knowledge: knowledge-guided
knowledge elicitation
The use of knowledge elicitation methods depends not only on decisions of the knowledge engineer but also on requirements the KRITON system detects on the basis of the already acquired knowledge.
A significant role in dealing with
incomplete knowledge
is played by thewatcher.
The watcher is an always active demon controlling the intermediate knowledge representation for missing components. For example, the user (the knowledge engineer or the expert) might have generated several objects during the incremental text analysis without any relation to the taxonomic organization of the objects of the corresponding domain (i.e. no information about the inheritance paths, part-of relations or instance relations was given). The watcher checks all objects at the intermediate knowledge representation level for missing, but possible or indispen- sable, links (every object has to be placed in a taxonomic organization), sends a message to the user and recommends the employment of an elicitation method to complete the knowledge base. The watcher is also invocated if an elicitation method starts, informing the user about incomplete parts of the knowledge base. Further- more, the user can delegate the selection of concepts to be used in an interview to
J Protocol-Analysis
3
36 J. D I E D E R I C t [ E T A L .
the watcher. In this case the demon looks for semantic related but incomplete objects and triggers an interview further exploring that domain.
6. Knowledge-base generation
As mentioned above, the intermediate knowledge representation is the blackboard for frame, rule and constraint generation.
The task of the frame generator is to translate the information stored in structured objects and their relation into a frame language. In principle, this is a simple syntactic transformation process. After frame generation, the user can interactively correct the translation process with a structure editor.
The output of the protocol analysis is the input for the rule generator. A subset of propositional clauses, extracted from adjacent segments in the thinking-aloud protocol, is offered to the user for rule generation. The user can either reject the proposal or use it for rule generation. Rule junctors and rule actors are inserted by selection from pop-up menus, premises and actions by entering the corresponding proposition number. Again, a rule editor can be called to correct for shortcomings of the automated protocol analysis.
Thus far, frame and rule generator build knowledge bases using the BABYLON frame and rule language (Di Primio & Brewka, 1985).
If the user, through interaction with the system, detects global value restrictions, the constraint generator is used to represent these global restrictions in a constraint language (Guesgen, unpubl.).
7. Steps in knowledge engineering with KRITON
The following is a description of phases in automatic knowledge acquisition using the KRITON methodology. These steps are not strongly chronological. Especially through the influence of the knowledge-guided elicitation process, loops (cyclic and alternating employment of the different KRITON submethods) are probable and for applications of considerable size can be considered necessary.
There is no doubt, that in certain cases the exclusive employment of single submethods of KR1TON will be successful.
The technique of incremental content analysis will be described in more detail in forthcoming publications. The overall knowledge-acquisition process consists of three levels: knowledge elicitation, intermediate knowledge representation and
knowledge base generation.
(I) Definition of the domain
The actual knowledge domain defined by the situation, in which human problem-solving process occurs, is initially investigated by means of interview techniques. The definition of the domain and the breaking down of the experts extensive knowledge into proportionate subparts is an important precondition for the automated acquisition process.
(II) Elicitation of declarative knowledge by automated interview techniques and incremental content analysis
The important terms and concepts of a concrete knowledge domain to be investigated by means of automated protocol analysis or other acquisition
KRITON 37
methods for procedural knowledge are inquired about and entered into the computer-based analysis system. Interview and text analysis are employed in a cyclic manner until the network of structured objects reaches a significant size. (III) Guided protocol recording
A protocol of loud thinking is tape-recorded. This requires a careful guidance to secure constant verbalization of the expert during the problem-solving work. It will take quite a few protocols if the problem domain is not to be restricted to a single problem-solving path.
(IV) Transcription
The recorded protocol is transcribed. While punctuation is not used, speech pauses are supplemented. The protocol is entered into the analysis system. (V) Protocol segmentation
The protocol is automatically divided into numbered segments, the speech pauses determining the length of the segments.
(VI) Search for the knowledge elements in the segmented protocol
The segmented protocol is searched for the various knowledge elements of the problem space. Concepts that are detected are stored together with the segments they are contained in.
(VII) Semantic analysis of the segmented protocol
By comparison with the available lexicon entries, all words contained in the segments found by V. are examined whether they include:
(a) ordinal relations, e.g. A is smaller than B or X is equal to Y or;
(b) tendencies, e.g. The state of X is stable. The value of Y continues to increase.
(VIII) Propositionalization
If such elements are found, the knowledge elements together with the operators interconnecting them are set down in a propositional calculus.
(IX) Completion of propositions
The system tries to find knowledge elements, which allow a completion of the above mentioned propositions. This is first done within the same segment, subsequently in the neighbouring segments.
(X) Knowledge base matching
The method described under VIII does not allow the identification and solution of references, especially over longer distances. In case of a proper realization of protocol recording, however, complex syntactic constructions are not to be expected. By way of trial, the completion of propositions is accomplished by searching for complete propositions displaying the components already extracted. The missing arguments are taken from these propositions.
(XI) Intermediate knowledge representation
All output from the protocol analysis is integrated in the intermediate knowledge representation system. This language supplies a propositional language as a goal for the protocol analysis. Each proposition consists of an operator, i.e. a semantic deep case representing the basic relation between the acquired concepts, a segment marker, i.e. a pointer to the origin of the proposition in the natural language protocol, and the relevant concepts.
(XII) Checking for completeness in the network of structured objects
Usually, protocol analysis will exhibit voids in the network of structured objects. This is always the case, when concepts used during the thinking-aloud procedure
38 J. D I E D E R I C t l E T AL.
are not sufficiently defined. In this case, interview and text analysis should be repeated.
(XIII) Frame generation
Structured objects in the semantic net of the intermediate knowledge repre- sentation level are translated into frame format, i.e. the B A B Y L O N frame representation language. In general, frame generators for several other languages can be added, using the intermediate knowledge representation as a blackboard. (XIV) Rule generation
Rule generation is an interactive process realized by mouse-operations for the selection of propositions to be used on the left- or right side of rules. Corrective actions can be taken by calling a structure editor. The organization of rule sets as well as the selection of control strategies, for the present, remains a task of the knowledge engineer.
(XV) Constraint generation
If global dependencies between data are discovered while using KRITON, these relations are encoded in a constraint language. The selection of data and their relations proceeds by mouse interaction.
8. Comparison with other systems of automatic knowledge
acquisition
None of the so far developed systems for automatic knowledge acquisition has reached product features. Most systems are experimental in character. Though the approaches are often very different and based on different theoretical assumptions, some common features are identifiable. The efforts center around the construction of a conceptual structure by means of an interactive system.
Nevertheless, there is no system that handles multiple knowledge-representation formalisms or makes use of several different knowledge sources to acquire both declarative and procedural knowledge. Some systems, for instance R O G E T , have no elicitation component and are primarily used for purposes of knowledge base extension.
KADS (Breuker & Wielinga, 1985) is an interactive system using a set of different functions in support of the knowledge engineer. This includes assistance in planning problems, data interpretation and consistency check. KADS was mainly based on a KL-ONE implementation in P R O L O G provided with a simple rule interpreter, the rules being part of a network. This knowledge based system, containing task- dependent and domain-independent information, is used for the interactive analysis of a knowledge domain. Analysis is controlled by interpretation models that are typical for specific problem-solving processes (e.g. in a diagnostic task domain). In principle, KADS can be considered as a library containing different elicitation methods.
R O G E T (Bennett, 1985) directly interacts with an expert to construct a rule base that is understood as fundamental conceptual structure of the knowledge domain. R O G E T itself was developed in the context of EMYCIN and supports only systems of this type. A R O G E T consultation is used for the following tasks:
KRITON 39
(2) acquisition of conceptual structure; (3) analysis of conceptual structure;
(4) operationalization of conceptual structure for a specific knowledge engineer- ing environment (of the EMYCIN type).
ETS (Boose, 1984, 1985) is another interactive system for generation of knowledge bases of the EMYCIN and OPS 5 types. The heart of the system consists of an on-line implementation of the repertory grid test by Kelly (1955). Factor analytical methods are applied for an investigation of semantic distance between concepts and possible implicational relations. ETS only supports rule-base generation.
9. Implementation
So far, all components described in this paper are implemented in a preliminary form on a XEROX-1108 machine in INTERLISP-D using object-oriented features from LOOPS. The structured object representation of the intermediate knowledge representation language is realized in form of LOOPS-objects. Protocol and text analysis make use of a lexicon for "closed class" words. Detection of word-stem works by means of an analysis of inflection, which itself is part of the lemmatization component. Lemmatization is to some extent lexicon-based and partly rule-based. The hit rate is well above 90% and in so far comparable to that of other systems. A first application of the system is planned for the second half of 1986. Application domain is an expert system for planning and configuration of bureau equipment.
10. Conclusions
We are aiming at an integrated, modular system-tool for knowledge acquisition in expert systems. On the one hand the system should be of high supportive value for the acquisition of declarative and procedural expert knowledge. On the other hand it should be open, in the sense, that it provides facilities for its own extension and elaboration. At the present stage of the systems development the protocol and the text analysis still work inaccurately and sometimes erroneously. For the present, these shortcomings in automatic text analysis are compensated by the usc of appropriate editors, through which the employment and testing of the system in applied industrial fields is guaranteed.
To sum up: the approach presented here to automated knowledge acquisition not only has the potency of taking advantage of developments in hybrid knowledge representation formalisms, but also is hybrid in the sense that it makes use of information from different knowledge sources. In our opinion, the guaranteed openness for future extensions is a most promising feature of the knowledge acquisition tool presented here, supplying it with a remarkable amount of applicability for various industrial fields.
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