Case Studies on Ontology Reuse

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1 Case Studies on Ontology Reuse Elena Paslaru Bontas, Malgorzata Mochol, Robert Tolksdorf (Freie Universität Berlin, Germany paslaru, mochol, Abstract: The development of new ontologies does not tap the full potential of existing knowledge sources and ongoing ontology engineering methodologies do not address ontology reuse to a satisfactory extent yet. In this paper we analyze the challenges related to the reuse process on the basis of two scenarios in the domains of erecruitment and medicine, which aim at building domain ontologies by reusing existing knowledge sources. Key Words: Semantic Web, ontology engineering, ontology reuse Category: I.2.1, I.2.4, J3 1 Introduction While it is generally accepted that building an ontology from scratch is a resource intensive process, the development of new ontologies does not tap the full potential of existing domain relevant knowledge sources. Typically, when implementing an ontology based application, the underlying ontology is built from scratch, does not resort to available ontological knowledge on the Web and is implicitly tailored to specific application needs, which in turn means that it cannot be reused in different settings. Ontology reuse can be defined as the process in which available (ontological) knowledge is used as input to generate new ontologies. Depending on the content of the knowledge sources and their domain overlapping one can distinguish between ontology merging and integration[11]. Available ontology engineering methodologies address reusability issues only marginally. Though most of them mention the possibility of reusing existing knowledge sources as input for the conceptualization phase, they fail to define precisely how ontology discovery and the subsequent evaluation of candidate ontologies should be performed, and they do not clarify the implications of reuse in the overall process. For example [17] describes in detail how to build ontologies from scratch, but gives a relatively sketchy recommendation for reusing existing ontologies. Some methodologies partially address this issue in the context of ontology customization/pruning i.e. extracting relevant fragments from very comprehensive, general purpose ontologies[15, 10]. [11] gives a detailed description of reuse process but does not examine its implications in the overall engineering process. In this paper we aim at examining the costs and benefits of ontology reuse by means of two case studies in which existing knowledge sources are involved in building new ontologies in the domains of erecruitment and medicine respectively. We enumerate the most significant cost drivers and benefits arising from reuse which influenced the decision about its profitability in the corresponding scenarios [Sections 2 and 3]. The case studies were conducted independently by engineering teams with similar experience in

2 the domain of ontology engineering. The human resources ontology [Section 2] was developed by a team of ontology engineers, while the realization of the medical ontology was inconceivable without a intensive collaboration with domain experts [Section 3]. Besides, the motivation for reusing existing knowledge sources was slightly different: due to the nature of sources to be reused standard classifications ontology reuse was a major requirement in the recruitment scenario, while the reusage of available medical ontologies in the second experiment was motivated mainly by presumed implementation cost savings. These different motivations had an important contribution to the cost benefit evaluation of the process. 1.1 Costs and Benefits of Ontology Reuse Ontology reuse is recommended by default in current methodologies and guidelines as a key factor to develop cost effective and high quality ontologies. Reusing ontologies should reduce engineering costs since it avoids rebuilding existing ontologies. This process is, however, related to significant costs and efforts, which may currently outweigh its benefits. First, as in other engineering disciplines, reusing some existing component implies costs to find, get familiar with, adapt and update the necessary modules in a new context. Second building a new ontology means partially translating between different representation schemes or performing scheme matching or both. The translation between representation formalisms is a realistic task only for similar modelling languages and even in this case current tools do face some important limitations[12, 6, 16, 13]. Moreover a large amount of knowledge, especially in thesauri or classifications, is formalized in a proprietary language and common translation tools are thus not applicable. Provided a common representation formalism the source ontologies need to be compared and eventually merged. For this purpose one needs a generic scheme matching algorithm, which can deal with the heterogeneity of the incoming sources w.r.t. their structure, domain, representation language or granularity. Matching algorithms though containing valuable ideas and techniques can not be currently applied in an efficient operationalized manner to an arbitrary reuse scenario due to their limitations w.r.t. the mentioned issues. On the other hand the benefits of ontology reuse go beyond the common cost saving and interoperability aspects usually mentioned in related engineering disciplines. Since ontologies are understood as a means for a shared knowledge conceptualization, reusing existing ontological sources increases application interoperability both on the syntactic and on the semantic level. Humans or software using the same ontology are assumed to hold the same view upon the modelled universe of discourse, and thus define and use domain concepts in the same way. The decision whether to reuse or newly build an ontology given a set of application requirements should be ideally supported by means to estimate the costs and the benefits of the two alternatives in a comparable quantitative way. The efforts arising from the (reuse oriented) ontology building process could be calculated using a cost estimation

3 model ONTOCOM[7]. Otherwise, some of the benefits are often intangible and thus difficult or impossible to quantify. While the reduction of implementation costs and the staff time savings can be computed in the same manner as the produced costs, interoperability or more subjective aspects such as the improvement of the organizational image by using standard components can not be reliably embedded to the evaluation framework. Additionally to comparing the amount of resources invested in the two approaches a cost benefit analysis should take into account the content and the quality of the expected outcomes. W.r.t. ontology engineering this requires a general purpose and a usage related quality framework to evaluate target ontologies. The next sections describe our experiences and lessons learned w.r.t. the mentioned problems for two Semantic Web applications. 2 Case Study Human Resources The Knowledge Nets project explores the potential of Semantic Web from a business and a technical perspective by examining the effects of the deployment of Semantic Web technologies for particular application scenarios and market sectors. For this purpose we built an use scenario for the recruitment domain in which we analyzed the online job seeking and job procurement processes and the implication of using Semantic Web technologies in this area[5, 1]. The first step towards the realization of the erecruitment scenario was the creation of a human resources ontology (HR ontology). The ontology was intended to be used in a Semantic Web job portal by allowing a uniform representation of job postings and job seeker profiles and semantic matching in job seeking and procurement tasks. In order to support common practices from the industry and to maximize the integration of job seeker profiles and job postings from different organizations the ontology underlying the Semantic Web job portal had to be aligned to established domain specific standards and classifications. We identified the subdomains of the application setting (e.g. professional and educational skills, types of professions and industrial areas) and several useful knowledge sources covering them. As candidate ontologies we selected some of the most relevant classifications in the area, deployed by national and international agencies and statistic organizations: i). Profession Reference Number Classification (BKZ) and Standard Occupational Classification (SOC) 1 ; ii). Classification of Industrial Sector (WZ2003) and North American Industry Classification System (NAICS) 2 ; iii). German Translation of Human Resources XML(HR-BA-XML); and iv). KOWIEN Skill Ontology 3. The candidate sources differ in the represented domain, the degree of formality and granularity. They cover the corresponding domains at diverse precision levels and are represented in different natural languages (English, German) and in a broad range of formats: text files (BKZ,

4 WZ2003), XML-schemes (HR-XML, HR-BA-XML), DAML+OIL (KOWIEN). While dealing with different natural languages complicated the process, human readable concept names in various languages were required in order to make the ontology usable in different job portals and to avoid language specific problems. Another important characteristic of the candidate ontologies was the absence of semantic relationships among concepts. Except for the KOWIEN ontology which contains relationships between skill concepts, the remaining ones are confined to taxonomical relationships at most. Consequently we had to focus on how vocabularies (concepts and relations) can be extracted and integrated into the target ontology. The selection of the candidate source ontologies was followed by their customization and integration to the target ontology. Due to the application setting, classification standards like the occupation classification and the classification of industrial sectors had to be completely integrated in the new ontology. A part of KOWIEN ontology was used to define concepts representing competencies to describe job requirements as well as job seeker skills. To extract the relevant fragments of KOWIEN we compiled a small conceptual vocabulary (of approx. 15 concepts) from various job portals and job procurement Web sites and matched these core concepts to the source ontology. The usage of the ontology in semantic matching tasks requires that it is represented in a highly formal representation language. For this reason the implementation of the human resources ontology was realized by translating several semistructured input formalisms and manually coding text based classification standards to OWL. 2.1 Costs and Benefits of Reuse in the HR domain A wide range of standards for process modelling and classification schemes for the recruitment domain are already available. Several taxonomies for the description of skills, classification of job profiles and industrial sectors have been developed by major organizations in these fields (e.g. the German Federal Agency of Employment). Using these standards was a central requirement for the simplification of the communication between international organizations accessing the portal and for interoperability purposes. Besides, reusing classification schemes like BKZ and WZ2003, which have been completely integrated to the target ontology, implied significant cost reduction. They guaranteed a comprehensive conceptualization of the corresponding subdomains and saved the costs incurred by a collaboration with domain experts. We are currently evaluating the preliminary HR ontology in order to estimate the costs implied by reusing existing sources. About 15% of the total engineering time were spent on gathering the relevant sources and about 35% were spent on customizing the selected source ontologies. Due to the heterogeneity of the knowledge sources and their integration into the final ontology up to 40% of the total engineering time were necessary to translate these sources to the target representation language OWL. Finally the refinement and evaluation process required 10% of the overall time. The aggregation of knowledge from different domains and the evaluation of available candidate knowl-

5 edge sources proved to be a very time consuming and tedious task because of the wide range of classifications available so far 4. The second cost intensive factor was related to technological issues. Translating various representation formats to OWL can not yet be performed optimally. Though tedious, the manual selection of relevant parts from the KOWIEN ontology and the HR-BA-XML standard was possible in our case, but there is a need for tools which assist the ontology engineer during this kind of tasks on real world, large scale ontologies with many thousands of concepts. Despite these problems our experiences with HR domain make us believe that reusability is both desirable and possible. The benefits of reusing standard classifications in this application setting outweigh their costs. Even though the ontology is still under development, it already fulfills the most important requirement of the application scenario, which is related to interoperability and knowledge share among job portals. However reusing available ontologies requires a considerable amount of manual work, even when using common representation languages like XML-schemes or OWL. The reuse process would have been significantly optimized in terms of costs and quality of the outcomes with the necessary technical support. 3 Case Study Medicine The project A Semantic Web for Pathology analyzes the usage of ontologies in a retrieval system for image and text data in the medical domain. The underlying ontology is used for concept based search techniques and for the semantic annotation of pathology reports[8, 9]. The ontology should cover both domain and application relevant knowledge which is specific to the healthcare institution involved in the project. Additionally, the usage of the ontology for semantic annotation required a maximal coverage of the vocabulary used by domain experts in medical reports. We first identified and analyzed over 100 knowledge sources that describe some aspects related to our application domain. The result of this phase was a list of potentially relevant sources, which however differed to a large extent in the covered domain and degree of formality: i). SNOMED and DigitalAnatomist 5 which describe the anatomy of the lung as well as typical diseases and are aligned to the UMLS thesaurus 6 ; ii). UMLS Semantic Network which describes generic and core medical concepts as part of UMLS; iii). XML-HL7, a standard XML based format for the representation of patient data and patient records; and iv). Immunohistology Guidelines used by domain experts in diagnosis procedures 7. Despite the size of the ontologies to be reused 8, significant parts of the pathology domain were not covered by existing ontologies. 4 For the engineering of the HR ontology we selected 24 classification systems and ontologies[2] from a much larger set of available sources in this area. The result of their evaluation were the 6 sources mentioned in Section This list is specific for the medical organization involved in the project. 8 For example SNOMED s semantic network contains over concepts.

6 Medical ontologies like SNOMED and Digital Anatomist had to be tailored to the particular needs of the application and its domain, in our case lung pathology. For this purpose domain experts identified 4 central concepts (i.e. lung, pleura, trachea and bronchia ) and extracted similar or related concepts from the two ontologies (i.e. concepts which are connected by any type of relationship with the core concepts). Since both ontologies are, as part of UMLS, aligned to the UMLS Semantic Network, the latter was also included to the application ontology. The result was a set of approximately 1000 concepts describing the anatomy of the lung and lung diseases. Pathology specific concepts, since available at most in text form, were added manually to the ontology (approx. 150 concepts). Candidate relationships were extracted from Digital Anatomist, SNOMED and UMLS Semantic Network. Approximately 50 relations were evaluated by domain experts, who finally inserted approximately 20 generic or medicine specific core relations to be included to the target ontology. The conceptualization phase was followed by the translation of the UMLS data to OWL[8]. Large parts of pathology specific knowledge had to be implemented manually since they were not supported by the source ontologies. To increase reusability we identified thematic clusters like anatomical, clinical and pathology specific subontologies and separate the application relevant knowledge (e.g. describing the form of the patient records) from the general purpose medical knowledge. A first and important evaluation of the generated ontology was focused on its appropriateness for the semantic annotation of medical reports. A comparison of the ontology vocabulary with a lexicon generated from an archive of medical reports resulted in further refinements of the target ontology, especially w.r.t. language issues Costs and Benefits of Reuse in the Medicine Domain The complexity of the application domain makes the building of a pathology ontology from scratch extremely costly. Reusing existing sources increases interoperability, since the target ontology is, at least partially, aligned to UMLS, which is used by several medical information systems. However, though containing a huge amount of domain information, the reuse of UMLS and integrated libraries like SNOMED and Digital Anatomist in our application setting was not trivial, due to their often ambiguous modelling decisions and error prone integration scheme[4, 14, 3]. The task specificity of each UMLS library, the complexity of the complete thesaurus and the heterogeneous coverage degree for specific medicine subdomains made a high quality customization for concrete application needs difficult. Besides most of the available medicine ontologies lack a representation format which supports sharing and reuse. These aspects became even more important when information sources do not share the same degree of formality (like in the case of the UMLS libraries, which are mapped to a common data format), but are represented as XML- or database schemes or natural language. 9 The text archive contained German documents, but most of the ontological concepts were denominated by English terms. Details related to the refinement process are presented in [8]

7 Since the retrieval system using the ontology is still under development, the product oriented benefits of the reusing process can not be evaluated completely at this point. However we may say that, for this scenario, the efforts related to the customization of the source ontologies required over 45% of the overall time necessary to build the target ontology. Further 15% of the engineering time were spent on translating the input representation formalisms to OWL. The reuse oriented approach gave rise to considerable efforts to evaluate and extend the outcomes (approx. 40% of the total engineering time). According to our experiences in this project the benefits of reuse were outweighed by their costs, because of the difficulties related to the evaluation and (technical) management of large scale ontologies and because of the costs of the subsequent refinement phase. In a second experiment the application ontology was built semiautomatically by domain experts on the basis of a domain specific document corpus. From a resource point of view, building the first ontology involved four times as many resources than the second approach (5 person months for the UMLS based ontology with 1200 concepts vs person months for the text close similar sized ontology), while the recall of the ontology w.r.t. the semantic annotation task was consequently improved. 4 Conclusions Typically ontology reuse starts with the identification of knowledge sources useful for the application domain, which differ both in the represented content, and in the formalization (thesauri, XML-schemes and DTDs, UML diagrams, textual descriptions etc.). An automatic integration of the source ontologies means not only the translation of the representation languages to a common format, but also the matching of the resulting schemes. Our experience during the presented case studies showed that due to scalability and heterogeneity issues both of these steps can not be performed efficiently using current techniques. An analysis of the resulted ontologies w.r.t. their dependencies to the source ontologies revealed that they reuse source vocabularies to a large extent. In the same time additional ontological primitives like properties and axioms are not supported explicitly in many knowledge sources and their integration to the target ontology is problematic because of the comprehension difficulties encountered by domain experts in getting familiar with such complex structures. There is a need for more pragmatic methods and tools which exploit the content of the source ontologies to a maximal extent depending on their particular domain and level of formality. Exploiting the lowest common denominator of the source ontologies i.e. their vocabulary, proved to be extremely useful in our reuse experiments [see Sections 2 and 3]. A solution towards a generic (semiautomatical) method for ontology reuse might be an incremental process which concentrates on the vocabulary of the input sources and subsequently insert additional information (semantic relationships, axioms, etc. if available explicitly) corresponding to application needs. Such a process would not tap the full potential of current technologies in the corresponding research areas, but would

8 imply significant cost reductions associated with less efforts for manually pre- and postprocessing of the results. Our current work is oriented towards the elaboration of a cost estimation method for ontology engineering allowing a more systematic cost benefit analysis for reuse processes. Acknowledgments This work has been supported by the EU Network of Excellence KnowledgeWeb and by the projects: A Semantic Web for Pathology funded by the DFG (German Research Foundation) and realized in collaboration with the Institute for Pathology at the Charitè Hospital, Berlin, Germany and the Department of Computer Linguistics, University of Potsdam, Germany and Knowledge Nets which belongs to the InterVal Berlin Research Center for the Internet Economy and is funded by the German Ministry of Research BMBF. References 1. C. Bizer, R. Heese, M. Mochol, R. Oldakowski, R. Tolksdorf, and R. Eckstein. The Impact of Semantic Web Technologies on Job Recruitment Processes. In International Conference Wirtschaftsinformatik (WI 05), Ch. Bizer, M. Mochol, and D. Westphal. Recruitment, Report, April A. Gangemi, D. M. Pisanelli, and G. Steve. An Overview of the ONIONS Project: Applying Ontologies to the Integration of Medical Terminologies. Data Knowledge Engineering, 31(2): , U. Hahn, M.Romacker, and K. Schnattinger. Automatic knowledge acquisition from medical text. In Proc. of the 1996 AMIA Annual Symposium, pages , M. Mochol, R. Oldakowski, and R. Heese. Ontology-based Recruitment Process. mochol/papers/semtech.pdf, B. Omelayenko and M. Klein, editors. Knowledge Transformation for the Semantic Web. Frontiers in Artificial Intelligence and Applications. IOS Press, E. Paslaru Bontas and M. Mochol. A Cost Model for Ontology Engineering. Technical Report TR-B-05-03, FU Berlin, ftp://ftp.inf.fu-berlin.de/pub/reports/tr-b pdf, April E. Paslaru Bontas, S. Tietz, R. Tolksdorf, and T. Schrader. Generation and Management of a Medical Ontology in a Semantic Web Retrieval System. In Proceedings of the CoopIS/DOA/ODBASE (1), pages , E. Paslaru Bontas, R. Tolksdorf, and T. Schrader. Ontology-based Knowledge Organization in a Semantic Web for Pathology. In I-KNOW2004, 4th International Conference on Knowledge Management, B. J. Peterson, W. A. Andersen, and J. Engel. Knowledge Bus: Generating Applicationfocused Databases from Large Ontologies. In Proceedings of the KRDB, H. S. Pinto and J. P. Martins. A methodology for ontology integration. In Proc. of the Int. Conference on Knowledge Capture(K-CAP2001), pages ACM Press, H. S. Pinto, D.N Peralta, and N.J. Mamede. Using Protege-2000 in Reuse Processes. In Proceedings of the OntoWeb-SIG3 Workshop at the 13th International Conference on Knowledge Engineering and Knowledge Management EKAW 2002, T. Russ, A. Valente, R. MacGregor, and W. Swartout. Practical Experiences in Trading Off Ontology Usability and Reusability. In Proc. of the KAW99 Workshop, S. Schulze-Kremer, B. Smith, and A. Kumar. Revising the UMLS Semantic Network. In Proc. Medinfo 2004, 2004.

9 15. B. Swartout, R. Patil, K. Knight, and T. Russ. Toward Distributed Use of Large-Scale Ontologies. In Proc. of the 10th Knowledge Acquisition for Knowledge-Based Systems Workshop, M. Uschold, M. Healy, K. Williamson, P. Clark, and S. Woods. Ontology Reuse and Application. In Proc. of the International Conference on Formal Ontology and Information Systems - FOIS 98, pages , M. Uschold and M. King. Towards a Methodology for Building Ontologies. In Proc. of the IJCAI 95, Workshop on Basic Ontological Issues in Knowledge Sharing, 1995.