Improving E-Manufacturing Efficiency through the Ontololigcal Filtering System

Copyright © IFAC Infonnation Control Problems in Manufacturing, Salvador, Brazil, 2004

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IMPROVING E-MANUFACTURING EFFICIENCY THROUGH THE ONTOLOGICAL FILTERING SYSTEM Raffaello LEPRA TTI Ulrich BERGER Brandenburg University of Technology at Cottbus Chair of Automation Technology P.O. Box 10 1344,03013 Cottbus, Germany. {iepratti, [email protected]} Tel: +49 - 355 - 69 24 57 Fax: +49 - 355 - 69 23 87

Abstract: In today's global market scenario enterprises unit building collaborative environments, whereby global manufacturing represents a key issue for ensuring market share. An unequivocal communication form among all involved process agents, i. e. human and machines, constitutes a crucial requirement. Referring to the shop-floor level, this isn 't easily achievable due to the different professional and cultural backgrounds of the involved machine operators. In this paper an innovative man-machine interaction concept for simplifying human tasks will be presented. A prototype based on the Ontology Filtering System has been developed. It enables formalisation of natural language instructions for mUlti-purposes in automation technology. Preliminary trial tests have been performed and show suitability towards a future use in the production field. Copyright © 2004lFAC Keywords: Artificial Intelligence, Knowledge-based Systems, Man-Machine-Interaction, Manufacturing System, Natural Languages, Programming Systems, Robot Programming.

1. STRUCTURE OF THE PAPER

concept mentioned above has been tested. In the last section (section 7), first trial tests are briefly discussed and future research purposes are illustrated. The latter substantially consists in the OFS employment for programming purposes within a heterogeneous machine environment in order to prove its suitability towards a future use in a production context.

This paper is organized as follows : Communication discrepancies among humans within global collaborative manufacturing environments are in the next section (section 2) underlined, stressing the reasons being connected to. The third section focuses on the human presence in future production scenarios considering trends and changes in their activity competItions as consequence of a growing automation degree, characteristic of the next generation manufacturing systems. Hereby, the need of a natural interaction form is pointed out. In Section 4 the possibility of natural language use as interaction means between humans and machine is further discussed . Section 5 shows how ontologies could contribute to overcome possible differences in natural expressions, whereas in Section 6 a concept for a natural language interface is being presented describing a platform prototype called Ontological Filtering System (OFS), by means of which the

2. INTER- AND INTRACULTURAL COMMUNICATION ISSUES In the modem global market scenario markets of different countries become ever more dependent on each other. Every country reacts by opening its economy towards other countries trying so to archive vast economical advantages. However, at the same time, enterprise requirements for market competition are getting more complex as an effect of a strong customising of final products. In order to respond to this situation and ensure market share, enterprises

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customisation, rapid reaction to market changes and quick time-to-market of new innovative products.

unite and build virtual collaborative environments called Virtual Enterprises, which are characterized by a high number of organizations being geographically widespread, that temporary collaborate in a project-oriented manner, in order to achieve common goals (Gebauer and Segev 1998). For external observers, these networks often appear as whole, real enterprises. However, they consist of autonomous parts, which could be added and removed to the network according to changes in customer requirements and collaborative environment dynamics. A pre-requisite for a smooth co-operation is represented by an unequivocal communication among all involved actors. Unfortunately, there are still a lot of understanding problems, which endanger a successful interoperation. These are mainly due to differences in cultural backgrounds. Indeed, people belonging to different international or even national communities incline to perceive and mentally process same real world situations in different cognitive ways, assigning these to different models (so called mental models). Thus, also two interaction partners speaking the same language and using an identical terminology can misunderstand each other, since vocabulary terms represent merely etiquettes of cognitive categories (Lepratti and Berger, 2003) (Fig. I). Communication can be therefore successful, not only thanks to knowledge in speaking and understanding partner's foreign languages, but above all because of mutual considering knowledge and cultural standards involved in the communication process (Eckensberger, 1996) Additionally, among same cultural communities the communication processes can still become abrupt. E. g. different professional qualifications are major causes of possible wrong interpretations. One might take into account differences in professional backgrounds between enterprise managers or engineers and personal at the shop-floor level. Therefore, possible deviations could arise at both levels: at the intercultural and at the intracultural one as well.

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Fig. 2: Customizing effect on the product life cycle. In this sense, it faces the challenge to produce according to a lot-size one paradigm of low cost and high quality (Berger and Lepratti. 2002b). in product development and Customising consequently offering a high variety of product features leads to a strong individualisation in the production domain. Hence, such situation influences the normal course of the product life cycle making it unforeseeable and risky in terms of investment and resource plans (see Fig. 2). In particular two effects should be stressed: On the one hand the lot-size value isn't stable enough but rather vacillates between a tolerated range's maximum and minimum ("TB," in Fig. 2). On the other hand reaction times to lot-size value changes are too slow ("T R,'" in Fig. 2). These are consequences of difficulties in recognizing and removing process defaults or adapting parameters, in order to re-stabilising process behaviour according to the actual market circumstances. In order to overcome these former problems, modem manufacturing industry needs a transition from traditional manufacturing methods into highly flexible knowledge-based global manufacturing. Such a challenging aim involves many difficulties, due to the growing automation degree in production plants. The United Nations Economic Commission for Europe i announced for instance that the employment of industrial robots (IRs) in the German automotive industry in the year 2002 has reached the 100.000 units leading, thus, to a relation of one IR per every 10 shop-floor operators (Fig. 3). This trend will be confirmed in the last year as well, since 30.000 more IRs have been adopted 2•

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Fig. I: Communication barriers in collaborative enterprise environments.

In connection to this evolution and to new requirements of manufacturing industry, drastical changes in shop-floor activities take place. Personal competences become more important than before and require an higher qualification level (GMA, 2003). If previously worker activities were normally carried out at the same machine -e. g. for monitoring of its

3. HUMAN'S ROLE IN FUTURE EUROPEAN MANUFACTURING SCENARIO The European Manufacturing Industry is in a transition from being a mass production industry towards a knowledge-based customer- and serviceoriented one, providing production on demand, mass

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right operation behaviour-, nowadays an operator has to govern an heterogeneous machine environment absolving various kinds of activities from programming to diagnostic as well as from intervention to maintenance ones (Zuhlke, 1999). 1,5X

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4. NATURAL LANGUAGE AS INTERACTION MEANS

Consequently, in order to support this transition process avoiding process breakdowns, researcher and industrial staffs are involved in conceiving and developing new intuitive surface technologies for multiple purposes activities. Major industrial targets related to could be resumed as follows (Berger and Lepratti, 2003) (see Fig.4): (i) reaction times to lotsize value changes should be drastically shorted (TR,}«TR,/) and (ii) the so called ramp-on-time steepness within the start-up phase should be raised, in order to faster reach the planned lot-size saturation value. Furthermore, as a side effect, the whole product life cycle is remarkable reduced (TL,2«TL. /).

Main goal of the research project described in this paper is the definition of an interaction means for the man-machine communication based on the structure of natural languages. This poses enormous challenges due to their features such as ambiguity, vagueness, possible abbreviations, metaphorical use and its continuous grammatical development and lexical enrichment (Lepratti et. ai, 2003) .. Still, at the same time, natural language represents the most familiar communication form among humans starting from children with their first expressions up to experts with their special treaties about the universe (Sowa, 2000). So, through its employment relevant and meaningful advantages could be achieved. On the one hand, people aren't compelled to learn special syntax and semantic languages (such as common program languages). On the other hand, enterprises could ~conomize production costs thanks to possible reduction of personnel setting-in period costs.

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In order to successfully use natural language as communication, human and machine need a shared information understanding, despite the fact that they communicate at different expression levels (see Fig.3) (Brachman, 1979) (Guarino, 1994).

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user-friendly and efficient interaction forms between human operators and smart industrial robots, which permit to embed human knowledge and experiences. Thus, the importance of a more Natural Feeling Human Interface is hereby empathized. One of the central challenges consists in the development of front-end interfaces being extremely intuitive in their use by means of e. g. natural language expression as dialogue-based and goalorientated multi-modal interaction form (multi-user, multilingual, multi-channel and multipurpose). This should be adaptive to both human and machine requirements, embracing the whole spectrum of personnel's cultural, linguistic and professional backgrounds, circumventing communication barriers and providing, at the same time information filtering and its proper presentation in a cross-media environment.

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Fig. 4: Aimed targets through employment of an innovative natural man-machine interaction.

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3.2 Towards a Natural Feeling Human Interface.

Epistemological Level

The European Information Society Technology Programme Advisory Group (Ahola, 2001) and the Society for Measure and Automation Technology of the German Engineering Federation (VDIIVDAGMA, 2003) underlie the necessity to overcome difficulties concerned with modem man-machine systems characterized by interaction forms that can only vary slightly in a matter according to predetermined functions or predefined syntax and semantic codes. The technology breakthrough will focus on the development of new knowledge for

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While machines communicate at logical level, where information primitives are merely pointers and

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memory cells being combined in a certain sequence structure by means of logical operators (for instance a bit sequence) and, thus, with a predefined and unambiguous meaning, humans communicate at the linguistic one, where expressions are directly associated to nouns and verbs making meanings ambiguous without knowledge of the context of use.

A linguistic ontology has been built for a variety of purposes within the automation technology domain. Four steps have been principally performed on the basis of a robot cell for deburring and dressing of aluminium diesel engines (Berger and Lepratti, 2002a) (Fig. 6) a) Fixing Basis Terminology First of all a basical terminology W consisting of a set of basic terms WN as well as a set of possible actions W y among them have been fixed by recognizing principal elements playing a significant role within the considered process domain of context.

5. THE ROLE OF ONTOLOGY An effective mutual commitment, which allows a constrain or at least an approximation of the intended meaning is necessary. Referring to Fig. 5 the Ontological Level represents the level where possible interpretation uncertainties of information contents, which arise during a communication process, are removed making information unambiguous and allowing that machines process it. Therefore, the Ontological Level is located between the levels of subjectivity and objectivity. However,formal Ontologies have to be distinguished from linguistic ones. Formal ontologies derive from Aristotle's metaphysics and, thus, are intended as a study of the nature of being, where all world's things are classificated in categories according to their form assuming this as an abstraction derived from sensory experiences, and so independently of the language being used to describe them. Linguistic Ontologies are comparable to large scale lexical resources, that cover most words of a language and describe the various word senses, providing at the same time an ontological structure based on semantic relationships. In (Guarino, 1997) various definitions of linguistic ontologies could be found. Noy's and McGuinness's one (Noy and McGuinness, 200 I) was identified as the most effective for the purpose of this work. According to them, ontologies "define a common vocabulary for researchers, who need to share information in a domain of use ". Perhaps, it is difficult to explain exactly the meaning of a Linguistic Ontology, but its definition is less important than the role it plays. In context of this paper project an Ontology represents an interface level, on which possible information ambiguities are filtered out and meanings formalized, in order to be correctly understood and processed from machines.

WN={W N1 ' wNl' .., wN,)={robot, gripper, workpiece ..} WV={W V1 ' WVl' .. .., wv,)={grasp, open, move ..} W= WNU Wv Such terminology is used to forward information to the machines. b) Terminology Enrichment and Establishment of the Functional Vocabulary Due to the diverse possibilities in natural language expression, a major number of terms, that could be used for the same intended meaning, are found out and included in WN and W y ., defining so the user's vocabulary and consequently the list of primitive procedures that users refer to in their interaction. Since, for instance, the English language bases on a lexicon of more than two hundred thousands different terms, this step was supported using the WordNetO Electronic lexical Database (Fellbaum, 1998). c) Chaining Up Terms As already stressed terms -either Noun or Verbs- could refer to the same meaning but with different degree of granularity in their level of specification. So, e. g. there are terms, which are general in their expression and others that go very deep with their meaning. The first ones could be used for everybody in difference circumstances without special knowledge in the domain of use (the so called Everyone Terminology), while the second ones are normally employed only by experts (the so called Expert Terminology). Anyway, all terms, which can be adopted within the same domain of knowledge and refer to the same basis terminology, are chained up in so called Synsets (Synonymy-Set).

6. THE ONTOLOGICAL FILTERING SYSTEM

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They refer to the same basis term within the knowledge domain of application They are connected to each other per different semantical relations (synonym, antonym, hyponym an so on) They have different granularity in their semantical level of specification They have a shared meaning for users.

Figure 7 shows an example of Synset for the basic term Robot.

Evaluation

Fig. 6: Workflow structure for the development of ontologies.

d) Structuring Ontology Network The last step -and also the most difficult one- concerns merging all

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Synsets defined at point "c" into a whole semantic network call Ontological Network. This occurs (I) identifying identical terms belonging to different Synsets, (2) aligning identified terms, (3) merging corresponding Synsets into each other and (4) building new semantical relations and/or recognizing and removing semantical inconsistencies. (Fig. 8).

In order to further formalize instructions in written English and make machines able to understand them following further subtasks were necessary. • • •

EVt!fYone Terminology Level

To realize these subtasks at the chair of Automation Technology at the Brandenburg University of Technology a test platform called Ontological Filtering System (OFS) was developed (Fig. 9). Its acts as interface between user and machines. The dialog platform converts text inputs into a semantic representation and converts user instructions into machine readable codes.

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Within the ontological network information ambiguities can be avoided with respect to the application domain leading used vocabulary terms (step "b") to machine language referents (step "a"). Therefore, the OFS allows filtering and standardization of natural expression forms and translates entered, syntactically and semantically plausible instructions in machine codes.

Fig. 7. A sample of Synset for the basis term Robot. On its top extremity Entity represents the correlated term with the most general meaning, while KR15 (KUKA Robot 15) is placed on the bottom as it shows the most specific meaning. 1)

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7. TESTS AND CONCLUSION

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In order to carry out first trial tests, persons have been asked to define (describe) objects, that were illustrated in some pictures. In order to collect different expression forms and to cover the largest terminology, persons working in different professional sectors (e. g. pharmacists, students of different faculties, artists, philosophers, teachers etc.) and specking, in the most of the cases, different languages have been interviewed. An extract of the performed survey is given in Table 1.

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Fig. 8. Finding and merging corresponding Synsets into a Ontological Network.

Tests within the OFS have been performed. These prove suitability towards a future use in production context motivating the authors to proceed in this research field.

Within the syntax and semantics of Onto lingua (Gruber, 1995) Semantic Classes such as Humans, Machines, Processes, Failures, Work Tools, Work Pieces as well as Relations among them have been defined, in order to gather terms together in one group according to their acquired semantic role in the domain of application and constrain their possible semantic combinations. An example of Classe and Relation between the classes "machine" (?mac) and "workpiece" (?wrk) is given below: (define-class machine (?mac) "Any mechanical or electrical device, that transmits or modifies energy to perform or assist performances of human tasks" relation to machine (?mac) = co-operation relation to work piece (?wkp) = handling relation to failure (?err) = restore

Fig. 9. The Ontological Filtering System as test platform for natural man-machine interaction.

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Brachman, R. J. (1979) On the Epistemological Status of Semantic Networks. In : Associative

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of Networks : Representation and Use Knowledge by Computers (N . V. Findler (Ed.)). Accademic Press. Eckensberger, L. H. (1996). Auf der Suche nach den (verlorenen?) Universalien hinter den Kulturstandards. In : Psychologische Kulturellen Handelns, (A . Thomas (Ed.)), pp. 165-197, Hogrefe Verlag, Gottingen. Gebauer, J., Segev, A. (1998) Assessing Internetbased Procurement to Support the Virtual Enterprise, Virtual-Organization.net, Newsletter (2,3). Gruber, T. R. (1995) Toward principles for the Tenn 1

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design of ontologies used for knowledge sharing. International Journal of Human-Computer Studies, 43, pp. 907-928 . Guarino, N. (1994) The Ontological Level. In : Philosophy and the Cognitive Science (R. Casati, B. Smith and G. White (Eds.)), Holder-PichlerTempsky, Vienna. Guarino, N. (1997) Understanding, Building and Using Ontologies. International Journal of Human and Computer Studues (46), pp. 290310. Lepratti R., Berger, U. (2003) Towards Ontology

Solutions for Enabling lnteroperability in Virtual Enterprises. In : Processes and Foundations for Virtual Organisations (L. M.

Table I : An extract of performed statistical survey

Camarinha-Matos and H. Afsarmanesh (Ed.)). Proceedings of the 4th IFIP Working Conference in Virtual Enterprises (PRO-VE 2003), pp. 307314. Kluwert, Boston-London. Lepratti, R.; Berger, U.; Arnold, K. P. (2003): Die Natiirliche Sprache zur Mensch-MaschineInteraktion im Wissenschaftsmagazin Forum der Forschung der BTU Cottbus, 7. Jahrgang Heft 16,2003, pp. 104-109. Noy, N. F. and McGuinness, D. L. (2001) Ontology

ACKNOWLEDGMENT The project described in this paper is performed in the laboratory of the Department of Automation Technology of the Brandenburg University of Technology. Further project trial tests with a FESTODidactic System, a KUKA KRI5 Robot and a SIEMENS Sinumerik 840D are foreseen.

Development 101 : A Guide to Creating Your First Ontology. Knowledge Systems Laboratory, Stanford University, CA, USA. Sowa, J. (1991) Toward the expressive power of natural language . In: Principles of Semantic Networks (1 . Sowa (Ed .)), pp. 157-189. Morgan Kaufmann, San Mateo, CA, USA . Sowa, 1. F. (2000) : Knowledge Representation:

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