Ontologies & Reasoning in Enterprise Applications

Undeﬁned 1 (2009) 1–5 1 IOS Press

Editor(s): Name Surname, University, Country Solicited review(s): Name Surname, University, Country Open review(s): Name Surname, University, Country

Daniel Oberle SAP Research Karlsruhe, Vincenz-Priessnitz-Str. 1, 76131 Karlsruhe, Germany, E-mail: [email protected]

Abstract. Despite the hype around the Semantic Web, ontologies & reasoning still share a touch of ivory tower technologies. While very prominent in academia, the breakthrough in enterprise applications has not happened. One of the reasons lies in the elusive advantages of the technology. Consequently, the paper carefully identifies eight value-adding features of ontologies & reasoning and positions them to existing technologies. Further, the paper argues that combinations of the value-adding features can benefit enterprise applications. Another reason lies in inherent challenges when adopting the technology which are also identified in this paper. Therefore, we also present two of our research contributions that trade off between added value and challenges.

Keywords: Semantics, Semantic Technologies, Ontologies, Reasoning, Enterprise, Industry, Commercial, Application

1. Introduction One reason for this conception is on the level of terminology. As pointed out in [35], it is the very breadth, While in the last years there has always been great depth, and range of possibility inherent in semantic academic interest in Semantic Web and in seman- technology that can prevent companies from experi- tic technologies and even first industrial products ap- menting with it, though it may be one of the most use- peared, we did not see the industrial breakthrough of ful commercial innovations of the past decade. The Semantic Web technologies. [4] Rather, semantic tech- murkiness of the word itself, not to mention the stan- nologies have been criticized for being elusive, espe- dards, acronyms, and jargon that can dominate the dis- cially for use in enterprise applications [67]. As the cussion of semantics, only adds to the confusion. late Bill Inmon, widely recognized as father of data It is one of the goals of this paper to counter the warehousing, mentioned: “The other day, I attended elusiveness of semantic technologies in an enterprise a conference on semantics. I went there with a truly setting. The first step to remedy the elusive nature is open mind. I wanted to learn, and I was open to mul- to be specific of what we are talking about. Therefore, tiple interpretations and multiple opinions. What I re- we limit ourselves to ontologies & reasoning in the ally wanted to know was: What business problem was following. With respect to ‘ontologies’ we stick to the semantics solving? What relevance was there between definition of [28]. By reasoning we mean any logical semantics and the real world? What value proposition deduction on the basis of an ontology, typically based for the conduct of business was there that was being on description logics or logic programming.1 While addressed by semantics? I really tried to understand there is still a certain bandwidth of interpretation in on- what was going on. And at the end of the day, I found nothing. Maybe it was there and I didn’t see it, but I 1This definition rules out technologies such as case-based reason- found nothing.” [32] ing [2] or RETE-based business rules management [24].

0000-0000/09/$00.00 c 2009 – IOS Press and the authors. All rights reserved 2 Daniel Oberle / Ontologies & Reasoning in Enterprise Applications tologies & reasoning, it is still much more concrete as The declared goal of the Norwegian Oil and Gas in- semantics or semantic technologies (cf. [35]). dustry is to enable information integration to support, The second step in countering the elusive nature is e.g., the integration of different data sources and sen- to identify the added value of ontologies & reasoning sors, the validation of information delivered by differ- on a pure technological basis and how it benefits enter- ent sources, and the exchange of information within prise applications. To complete the picture, the paper and between companies via Web services [65]. also identifies challenges when adopting ontologies & This requires that Oil and Gas IT systems, e.g., as- reasoning in a commercial setting. Therefore, the con- set management or facility monitoring, share informa- tribution of the paper helps researchers and practition- tion according to a reference model, and that they can ers since they now have at least a qualitative argumen- interpret messages using that model. Such a reference tation line for why an enterprise application based on model is given by the ISO 15926 Oil and Gas ontology ontologies & reasoning is superior. Contrasted with [37] which is formalized in OWL. As depicted in Fig- the identified challenges, this allows to make an edu- ure 1, the ontology shall serve as a common vocabulary cated decision whether ontologies & reasoning should to exchange information between field data and and the be used at all. In turn, this helps to make a pitch in front onshore participants (operators and vendors). In par- of decision makers to judge potential investments, thus ticular, as pointed out in [1], the offshore industry has addressing typical struggles such as mentioned in [67]: huge economic investments in data acquisitions, analy- “... the connection between what a business can do sis, visualization, documentation and archiving. Some with IT WITH a semantic model remains elusive and of the data are in use for decades and it is one of the vague. That is the problem. Ever tried to talk with main assets. Organizational units and IT systems last the CEO or VP of Sales about their semantic model rarely more than few years. The most stable element in for customer? That’s a sure way to leave the office this environment is the terminologies used in the busi- quickly .” ness domains along the value chain. Therefore, it is the Finally, the paper summarizes two of our contribu- purpose of ISO 15926 to capture the terminology in a tions that trade off between the added value and chal- formal and reusable way. lenges which have been published in [54] and [51]. The first is targeted at common software developers and en- ables them to build software on the basis of an ontology in their familiar environment without having to ac- quire knowledge in ontologies. The second presents a reference architecture for using ontologies in existing enterprise settings in a non-intrusive way. The paper is structured as follows. To substantiate the contributions, we first introduce a prominent scenario in Section 2 where ontologies & reasoning are actually used in an industrial setting, viz., the Oil & Gas industry. The scenario is used to underline the added value of ontologies & reasoning in Section 3 and how it benefits enterprise applications (Section 4). Sec- Fig. 1. Ontologies play a major goal in the Norwegian Oil & Gas tion 5 is concerned with explaining typical challenges industry’s strategy. [1] that range from technology agnostic to ontology specific. The two approaches for trading off benefits and First middleware solutions are available to address challenges are presented in Section 6 before we con- the task of information integration, e.g., IBM’s In- clude in Section 7. formation Integration Framework [15]. Such middleware solutions allow information exchange between the existing IT systems based on ISO 15926. Further, 2. Scenario they facilitate the creation of new composite applications, such as production optimization or equipment This section introduces a running example in the Oil fault protection. Such composite applications depend and Gas industry. The example will be used throughout on information stemming from several existing IT sys- the paper to underline the claims. tems, and, thus, benefit from semantically unambigu- Daniel Oberle / Ontologies & Reasoning in Enterprise Applications 3 ous information exchange. According to [1], using ISO Scenario As pointed out in the running example, the 15926 it is possible to: most stable element in the Oil and Gas industry is the terminologies used in the business domains along the – integrate data within and across business domains value chain. Therefore, capturing this domain in the – create an architecture for Web services ISO 15926 ontology provides benefits since it corre- – include reasoning as a part of the ontology for sponds better to an Oil and Gas expert’s conceptual- creation of autonomous solutions ization of the domain. – include uncertainty as a part of the ontology to cope with risks Related Work The use of classes and relations as – be able to store data over time main modeling primitives is well established in other – to use the ontology as a reference ontology (meta disciplines such as database design and software en- data set) to be used in-house and between compa- gineering. Therefore, conceptual modeling is not id- nies iosyncratic for ontologies and has been introduced as – to use the ontology in engineering early as the Entity-relationship Model [14] or UML [10]. [13] already concluded that, for inexperienced end-users, conceptual models are more effective for 3. Value-adding Features of Ontologies & database access than the relational data model. Reasoning 3.2. Flexibility

[16] discusses added values on the more general Explanation Database design and (model-driven) level of ‘semantic technologies’ stemming from the software engineering typically start with a conceptual experiences from a specific research project. In con- model which is then manually or automatically trans- trast, the following subsections identify eight value- formed to an executable model. That means the initial adding features more concretely for ontologies & rea- conceptual model is hardly adaptable after the trans- soning as a result of five years applied research across formation. In contrast, ontology languages and stores several research projects, industrial prototypes and provide for schema flexibility (frequently dubbed the products. ‘schema last’ feature) where classes and relations can Ontologies & reasoning inherit from several re- be added, changed or deleted also during runtime of an lated and precursory technologies, e.g., logics, theo- application. rem provers, deductive databases, AI, or semantic nets. Scenario Flexibility also benefits the Oil and Gas Therefore, many of the value-adding features are not setting, e.g., when new types of equipment have to be unique but are also offered by related technologies. captured in the ISO 15926 ontology after it is initially However, we claim that the combination of all value- deployed in applications. adding features provides a unique advantage. Related Work The currently prevailing relational 3.1. Conceptual Modeling databases require that a database schema exists prior to data entry. However, flexibility is typically given for Explanation Ontologies are a means for modeling a graph-based data and conceptual modeling languages specific universe of discourse according to the well- (cf. [6] for an overview). Object-oriented database known principle of conceptualization [19]. Conceptual management systems [36] also support this feature to modeling languages make use of intuitive modeling a certain extent. primitives (typically classes and relations). According 3.3. Enduser Editing to [44], conceptual models are more effective because human performance in man machine interactions de- Explanation Through the combination of conceptual pends on the “gulf” between the human’s conceptual- modeling and flexibility the feature of enduser editing ization of the task and the presented interface. When becomes possible. Enduser editing means that a lay- the gulf is large, performance is poor. When the gulf is man user can create, change, and populate the ontology small, performance is good. One could theorize, then, by generic tools. A good example for such a generic that conceptual models better correspond to an end- tool is the Semantic Media Wiki [38]. Contrast this user’s conceptualization of a database than machine- with applications based on relational databases, where oriented models. [30] GUI logic has to be developed individually. 4 Daniel Oberle / Ontologies & Reasoning in Enterprise Applications

Scenario ISO 15926 is engineered collaboratively to prevent modeling from scratch by giving a well- by special interest groups each of which focus on a engineered starting point for one’s own ontology. The specific sub-domain of Oil and Gas. Examples for use of such an ontology prompts an ontology engi- such sub-domains are drilling and completion, opera- neer to sharpen his/her notion of the concepts to be tion and maintenance, or production and reservoir. It modeled. A prominent example is the DOLCE foun- is the task of each special interest group to capture dational ontology [26]. Ontology design patterns [52] the corresponding sub-domain ontologically and inte- save modeling efforts by providing templates for ever grate it in the overall ISO 15926. Using the Semantic re-occurring modeling needs. Thus, they prevent re- Media Wiki, it becomes possible for the domain ex- inventing the wheel and make modeling decisions bet- perts to collaboratively model and document their sub- ter traceable. Finally, the OntoClean [29] approach domains. provides explicit quality criteria for building an onto- Related Work There are other technologies that pro- logically correct taxonomy. vide generic tools for enduser editing. A good example is topic maps [61] which also are a means for concep- Scenario Using a foundational ontology as starting tual modeling including the feature of flexibility. point for ISO 15926 has indeed been discussed and would lead to a philosophically sound basis. It would 3.4. Reuse provide the means for disambiguating terms such as ‘drilling’ which can be both an activity and a topic of Explanation The declarative nature of ontologies expertise. Fundamental ontology design patterns such makes them particularly eligible for reuse. That means as ‘participation in an event’ would make the overall the effort of conceptualizing a universe of discourse ontology better to maintain and comprehensible. Onto- is undertaken once and the resulting ontology can be Clean would provide for an ontologically sound taxo- reused in multiple applications. This is especially the nomic backbone of ISO 15926. goal of reference ontologies. In addition, ontologies are often attributed as being a ‘shared’ conceptualiza- Related Work To the best of our knowledge, the idea tion, i.e., users agree on the conceptualization, what of having foundational ontologies and quality criteria potentially increases the level of reuse. There are sev- has not occurred in other disciplines. Note, however, eral methodologies proposed for reaching the consen- that both can be applied to other conceptual modeling sus of ontologies, e.g., [62]. languages as well, e.g., UML class models. There is a bulk of related approaches to ontology design patterns Scenario In the Oil and Gas setting, the ISO 15926 — especially the well-known design patterns in soft- ontology shall be used for several purposes and in dif- ware engineering [25]. ferent applications as discussed in Section 2. Thus, reuse is one of the inherent value-adding features sought by using an ontology. 3.6. Web Compliance Related Work Although it is not their initial motiva- Explanation Up until the late 90ies there were sev- tion, technologies such as conceptual database design eral proprietary and incompatible ontology languages (with, e.g., ERM [14]), UML [10], Model-Driven En- and corresponding tool suites. The recommendations gineering with its platform-independent models [33], or XML Schemas, can achieve reuse as well. Ontolo- of the W3C Semantic Web Activity yielded standard- gies are often built without consensus, and, more im- ized languages such as RDF(S) [11] and OWL [41] portantly, a shared conceptualization can also be rep- that are Web compliant. That means every element of resented by other languages, e.g., UML. an ontology is identified by a URI, XML serializa- tions are available, and ontologies can be distributed, 3.5. Best Practices modularized and referenced across the Web. Besides, there are W3C recommendations for interlinking on- Explanation Best practices comprise foundational tologies with existing Web resources (SA-WSDL [21], ontologies, ontology design patterns and quality cri- SA-REST [27], RDFa [3]) as well as the query lan- teria for obtaining ‘better’ results or more efficiency guage SPARQL [53]. The recommendations are cur- in modeling. Foundational ontologies are a means rently applied to realize the Web of Linked Data [9]. Daniel Oberle / Ontologies & Reasoning in Enterprise Applications 5

Ad-hoc First-order, Scenario One reason for formalizing ISO 15926 in Data Models Hierarchies XML Higher-order, (UML, STEP) OWL, besides its original language EXPRESS, is cer- (Yahoo!) DTDs Modal Logic DB Logic Thesauri tainly because OWL is a W3C recommendation and Terms Schema Programming many tools for editing, storage, and reasoning exist. Web compliance benefits the envisioned ‘smarter so- informal formal lutions’ such as Web portals based on ISO 15926 (cf. ‘Ordinary’ Structured XML Description Figure 1). Glossaries Glossaries Schema Logics Data Informal Formal Dictionaries Hierarchies Taxonomies Related Work Web compliance is also given for the (Folksonomies) whole set of recommendations that revolve around MetaData, Glossaries & Thesauri, Logical XML. XML Schema [20], especially, is often consid- XML Schemas, Data Dictionaries Taxonomies Languages ered a full-fledged substitute for RDF(S) in the context Data Models of Linked Data. In fact, the differences between XML Fig. 2. The continuum of formality. Adapted version from [64]. Schema and RDF(S) are elusive [17]. 3.8. Reasoning 3.7. Formality Explanation As already mentioned in Section 1, any Explanation Typically, ontology languages such as logical deduction on the basis of a formal ontology OWL are based on formal logics with model-theoretic is defined as reasoning in this paper. Different rea- semantics. On the one hand, this provides for unam- soning services are offered by inference engines (also called reasoners) depending on the ontology language. biguous meaning of modeling primitives. As an ex- Description logic reasoners typically offer subsump- ample, consider the formal semantics of the ‘subclass- tion checks, automatic classifications and consistency of’ modeling primitive. The model theory underlying checking. Logic-programming-based reasoners allow OWL basically maps the modeling primitive to the for rules and instance retrieval with larger data sets. subset operator in set theory. Contrast this with the The reasoning services are a benefit because the of- incomplete, ambiguous, and informal specification of fered functionality does not have to be developed from UML [55]. On the other hand, the model theory is a scratch for each application. prerequisite for having a proof theory, and, thus, reasoning becomes possible as another value-adding fea- Scenario Section 2 discussed that reasoning shall be ture (cf. Section 3.8). used for the creation of autonomous solutions in the Oil and Gas domain. For example, rules can be applied Scenario Since one of the goals of ISO 15926 is to to declaratively capture and infer knowledge on the ba- capture the most stable element of terminologies in sis of ISO 15926 in production optimization. the Oil & Gas domain, formality benefits the endeavor Related Work The reasoning services of logic-pro- by countering ambiguity. Being a prerequisite for rea- gramming-based inference engines overlap with RETE- soning, formality also helps the goal postulated in [1], based business rules management systems [24]. How- which is to enable autonomous solutions by applying ever, the latter are less formal with respect to the con- reasoning on the basis of ISO 15926. tinuum depicted in Figure 2 and are not paired with a conceptual modeling language. Related Work As depicted in Figure 2, the formality of a (conceptual modeling) language is actually a continuum [64]. At the one extreme, there are purely 4. Benefits for Enterprise Applications informal, i.e., natural, languages to specify an “ontology.” However, in our definition, speaking of an ontol- The added value of ontologies & reasoning de- ogy is only valid in the rightmost category of ’logical scribed in the previous section can be applied to benefit languages.’ In between are languages such as ERM, enterprise applications. As shown in Figure 3, the eight that do provide a mathematical definition, yet lack a value-adding features of ontologies & reasoning are of model-theoretic formal semantics as a prerequisite for more or less importance to achieve the following ben- reasoning. efits: new business scenarios, increased productivity of 6 Daniel Oberle / Ontologies & Reasoning in Enterprise Applications software engineering, increased productivity of infor- of Web service discovery and composition is targeted mation workers, and improved enterprise information by annotating a Web service with an ontology. The au- management. tomation is then achieved by reasoning. The basic idea The argumentation line we apply here is qualita- is also foreseen in the Oil and Gas setting since an ar- tive and visualized by Harvey Balls. Harvey Balls [34] chitecture for Web services and the use of ISO 15926 are round ideograms used for visual communication is explicitly mentioned. of qualitative information. They are commonly used More recently, ontologies & reasoning are also ap- in comparison tables to indicate the degree to which a plied to more established software engineering in order particular item meets a particular criterion. to achieve cost and time reduction. Examples are – facilitation of software reuse by ontologies [7], 4.1. New Business Scenarios – improved software configuration management [56], or In extreme cases, arbitrary combinations of value- – simplification of middleware management tasks adding features can create new business scenarios. A [46]. prominent example is the global movement of govern- ments and local authorities starting to put their data on Another benefit often sought is quality improve- the Web of Linked Data (cf. also Section 3.6). Open ments in software engineering. For example, there is government data projects have sprung up in countries potential in quality improvements when problems such around the world from the United States, Australia as the discovery of Web services are reduced to com- and New Zealand to The Netherlands, Sweden, Spain, mon reasoning services (cf., e.g., [39]). That means, Austria and Denmark, not to mention an increasing development effort, and, thus, the likeliness for errors number of city- and local-authority-based initiatives in the code is minimized by instead applying an exist- from Vancouver to London. New business scenarios ing formally sound and correct reasoner. achieved through open government data vary, includ- In our running example, building Oil and Gas appli- ing increasing transparency and democratic account- cations would accordingly take less time, require less ability, supporting economic growth by stimulating cost, or feature improved quality — independent of new data-based products and services, and improving whether the application actually uses ISO 15926. how public services are delivered. [57] In essence, this example combines the value-adding features of con- 4.3. Increased Productivity of Information Workers ceptual modeling and Web compliance (Linked data especially) to achieve the benefit. Information workers (also known as knowledge Another example, which also pairs the value-adding workers) in today’s workforce are individuals able to features of conceptual modeling and Web compliance, act and communicate with knowledge within a specific is the Yahoo SearchMonkey [43] enabling a business subject area. They advance their overall understanding model for improved result presentation by specialized of that subject through focused analysis, design and/or search engines. Yahoo harvests RDFa-enriched Web development. They use research skills to define prob- pages and stores the extracted RDF triples in its index. lems and to identify alternatives. Fueled by their ex- In turn, developers can build their own search engine pertise and insight, they work to solve those problems, on the basis of an ontology with a development kit al- in an effort to influence company decisions, priorities lowing access to Yahoo’s search index. and strategies. Information workers may also be found in the Oil and Gas setting, e.g., an operator in an on- 4.2. Increased Productivity of Software Engineering shore control room. As businesses increase their de- pendence on information technology, the number of The most important value-adding features lever- fields in which knowledge workers must operate has aged for increased productivity of software engineer- expanded dramatically. [18] ing by ontologies & reasoning are conceptual model- Efficient access to useful information should pro- ing paired with formality and reasoning. The increase mote information worker productivity by facilitating in productivity has so far been achieved in basically faster, higher quality decision making. If information three ways. access influences productivity, its distribution and dif- Probably the first attempt has been the research field fusion patterns should in turn correlate with the rela- of Semantic Web Services [42]. Here, full automation tive productivity of information workers. In the infor- Daniel Oberle / Ontologies & Reasoning in Enterprise Applications 7

Value-adding Conceptual Flexibility Enduser Reuse Best Web Formality Reasoning features: Modeling Editing Practices Compliance

Benefit for enterprise apps:

New Business Scenarios

Improved productivity of software engineering

Improved productivity of information workers

Improved Enterprise Information Management

Value-adding feature is of central major medium minor no importance for benefit for enterprise application

Fig. 3. Technological value-adding features of ontologies & reasoning lead to benefits for enterprise applications. mation age, new technologies, new ways of working, Gas industry is to enable information integration based and an increasing availability of information could sig- on ISO 15926 to support, e.g., the integration of dif- nificantly affect productivity and specifically the pro- ferent data sources and sensors, the validation of in- ductivity of information workers. [8] formation delivered by different sources, and the ex- It is mainly through the combination of the con- change of information within and between companies ceptual modeling, flexibility, and enduser editing fea- via Web services [65]. In fact, there has been a plethora tures that this benefit is achieved. A good example is of ontology-based information integration approaches ontology-enhanced business intelligence such as pre- (cf. [45]). sented in [59]. Here, an ontology conceptualizes the The benefit of improved EIM is mainly achieved enduser’s domain in an intuitive way. This could be through a combination of conceptual modeling, flexi- ISO 15926 for capturing the Oil and Gas terminol- bility, reuse, Web compliance, and formality. As men- ogy. The operator of an onshore control room interacts tioned before, conceptual modeling improves under- with the business intelligence solution via the ontol- standability by the enduser in general. Flexibility is of ogy which can also be personalized and changed for major importance since it allows to evolve the con- his purposes. The ontology shields the enduser from ceptual model over time according to the enterprise’s IT systems’ intricacies by means of mapping legacy representation needs. In addition, modeling a domain data structures to the ontology for intuitive interaction. once and reusing it enterprise-wide is one of the goals This is achieved by rule-based ontology-reasoning in sought by EIM. Web compliance facilitates linkage to this particular case. and integration of Web resources. Finally, the formality counters ambiguities of enterprise information. 4.4. Improved Enterprise Information Management

Enterprise Information Management (EIM) is a par- 5. Challenges of Adopting Ontologies & ticular field of interest within information technology Reasoning area. It specializes in finding solutions for optimal use of information within organizations, for instance to A myriad of scientific contributions have been pub- support decision-making processes or day-to-day op- lished particularly in the Semantic Web community erations that require the availability of knowledge. It that leverage the value-adding features of ontologies tries to overcome traditional IT-related barriers to man- & reasoning. Yet, scientific contributions usually end aging information on an enterprise level. with a simple prototype in the context of a PhD stu- In our example, it is one purpose of ISO 15926 to dent’s dissertation. Seldomly is the software matured facilitate and enable information integration across IT to a state where it is documented, distributed, and fur- systems. The declared goal of the Norwegian Oil and ther maintained. Besides, the scientific contributions 8 Daniel Oberle / Ontologies & Reasoning in Enterprise Applications are often only tested against toy examples and not have to switched, specific software components might thoroughly run through a set of real-world test cases have to be replaced because of license incompatibili- with enterprise-scale amounts of data. In addition, ties, etc. The challenge typically increases with the size the scientific contributions are typically integrated in of the legacy code and size of the enterprise’s portfolio. single-user software engineering environments such as The following issues have to be addressed: Eclipse. However, enterprise-scale software engineer- Build or buy? If the required technology is not read- ing typically requires a highly collaborative software ily available, the enterprise is first prompted to engineering environment where dozens of developers decide to buy the technology or even to consider work in parallel on the same piece of software. Fi- redeveloping the technology on its own. Reasons nally, the contributions are geared to exactly one pur- for redevelopment might be immaturity of the ex- pose. Suitable ontology languages, editors, reasoners, isting technology or license incompatibilities or and stores are chosen according to a specific use case. strategic investments. In the case of adopting ex- Different use cases might require different reasoners or isting software, the challenges continue below. A stores. When applied in a productive setting, it is ben- redevelopment is only possible if the required ex- eficial to keep the complexity of additional technolo- pertise is at hand (cf. Section 5.6). gies, i.e., the number of different stores or reasoners, Maturity level of tools As already explained above, low, however. Each additional technology represents a scientific work usually ends with a simple proto- cost factor since it has to be obtained or (re-)built, in- type and is lacking the required maturity. Most tegrated in the existing landscape, and maintained. Be- of the time the software is undocumented and not cause of the problems, only few use cases are eventu- maintained. It requires a considerable effort to ally transferred into a commercial product. One of the bring academic prototypes to the required matu- few exceptions is that of ensuring consistency in car rity level. Often such projects fail when the origi- configurations [5]. nal developer or community is not available. This In order increase the rate of successful transfers might stop the whole project or lead to redevelop- from scientific contributions to commercial applica- ment on the enterprise’s behalf. tions, the use cases need to (i) run in an enterprise- Enterprise scale performance Large enterprises of- scale setting, (ii) consider boundary conditions such ten feature heaps of data in the area of tera as existing software engineering lifecycles or devel- bytes. The scientific contributions are often only opment infrastructures, (iii) run in a multi-developer tested against toy examples and not thoroughly landscape, and (iv) keep the required additional seman- run through a set of real-world test cases with tic technology as minimal as possible. More specifi- enterprise-scale amounts of data. Depending on cally, if a use case is to be adopted by an enterprise, the ontology language, enterprise scale perfor- several challenges have to be faced as introduced in mance might not even be theoretically possible [47]. Figure 4 aligns the challenges along an axis that due to the complexity of the language. moves from technology-agnostic to ontology-specific How to handle ontologies in the transport system? challenges. So far, general answers to the challenges Business applications for large enterprises typi- are hardly available. In the following subsections we cally feature a transport system for dealing with walk through the challenges in more detail. updates. When mission critical software is to be updated, a test system is set up which can be 5.1. Technical Integration transported automatically to a productive system. The transport happens at a specific point in time, The first challenge concerns the technical integra- e.g., when a test period has been run successfully. tion of the use case, and, in particular, its required on- Often the test systems are even cascaded to a pre- tology language, editor, store, and reasoner. Techni- productive system for detailed tests. So far it re- cal integration means the required technology needs to mains unclear how to deal with the flexible nature be embedded in the existing landscape of the adopt- of ontologies in such software transport systems. ing enterprise. As an example, consider an Oil company that wants to incorporate ISO 15926 along a spe- 5.2. Technical Integration ×n? cific ontology store and API in its existing IT landscape. Adaptors have to be written to legacy code and Suppose several use cases of ontologies in software databases, versions of programming languages might engineering are adopted by an enterprise. Each use Daniel Oberle / Ontologies & Reasoning in Enterprise Applications 9

Technology agnostic Ontology-specific challenges challenges

Challenge 1 Challenge 3: Challenge 5: Technical Integration Cost-benefit ratio How to measure the benefits? Challenge 2: Challenge 4: Challenge 6: n x Technical Modeling depends Training Integration ? on use case

Fig. 4. Overview of challenges. case might require a different ontology language possi- 5.3. Modeling Depends on Use Case bly based on a different logic. That means different use cases might require different editors, stores, and rea- Besides the cost of technical integration, there is soners. In this case, the challenge of technical integra- also the significant cost of modeling an ontology as tion might have to be faced ×n. The situation is further discussed in [58]. Therefore, the challenge is to minimize modeling costs as much as possible. Indeed, if complicated when the different use cases are geared at several use cases are to be applied, the hope is that different beneficiaries. Some might benefit the devel- modeling a domain once and capturing it in an on- oper and some might benefit the enduser, eventually. tology will suffice for all use cases. However, differ- That means editor, reasoner, or store might have to be ent use cases typically have different representation integrated both in the developer run time or the appli- needs. Below, we show two typical phenomena that cation run time environment. Both environments might cause this challenge. Note that the phenomena are not be completely different. idiosyncratic to ontology languages but are also known Coming back to our running example, the Oil com- from other conceptual modeling languages or database pany might be forced to integrate ISO 15926 in its ex- schema design (normalization theory). isting IT landscape in order to be interoperable and Regarding the first phenomenon, consider the do- compliant with partners. This use case affects the in- main of Oil and Gas as a simple example. When mod- formation worker and might require the integration of eling this domain, there might be the need to rep- resent information about equipment (such as pumps a suitable ontology store and API in its existing IT and valves) and at which production plant it is de- landscape. However, the Oil company might also fea- ployed. For one use case it might be enough to rep- ture a large IT department where custom software is resent equipment and plant as classes and to estab- developed in house. In order to increase the productiv- lish a simple relation between them to capture the de- ity of software engineering, the company fancies to es- ployment (cf. Figure 5 top). However, another use case tablish an ontology-supported environment for its de- might have to capture information about the deploy- velopers. This might require the integration of addi- ment itself, e.g., its duration. Therefore, a simple re- tional — and potentially different — ontology store, lation between equipment and plant is not sufficient reasoner and API. to meet this requirement since decidable ontology lan- Therefore, the second challenge is to minimize n guages typically do not allow attaching attributes and ideally to 1. The hope is that technical integration is relations to other relations. The deployment relation not required on a ‘per use case’ basis but that soft- has to be reified to a separate class (cf. Figure 5 bot- tom). ware components such as ontology editor, stores, and There is a second phenomenon that leads to differ- APIs can be factored out much like what happened ent representations of the same domain. For example, with database management systems. The NeOn API the modeling applied by [40] to the domain of Web ser- and plug-ins [63] are a step in this direction. However, vices is geared towards reasoning. That means model- our experience shows a different trend because of the ing decisions are taken in a way such that subsumption very special representation and reasoning needs of the reasoning can be applied to eventually discover web individual use cases. services. In contrast, [48] models the same domain but 10 Daniel Oberle / Ontologies & Reasoning in Enterprise Applications

deployment support, the SAP landscape would feature two technol- Equipment Plant ogy stacks. If ontology editors, stores, or reasoners are to be integrated in both stacks, the TCO will approx- imately rise by the factor of 2. The number of technologies equals 1 if ontology language, reasoner, edi- Equipment Deployment Plant tor, and store are to be integrated once. If different reasoners, editors, or stores are required, the number will rise accordingly. Fig. 5. Different use cases might require different representations of the same domain of discourse. Continuing the running example of Section 5.2, the ]of technologies would equal 2 because the Oil com- for the purpose of establishing a reference ontology. pany has to integrate different ontology stores and Here, modeling decisions are influenced by ontology APIs for two different use cases. If the technology quality criteria such as OntoClean [29] leading to a dif- stacks of the IT landscape and the development envi- ferent ontology. ronment differ, the ]of stacks in landscape would also equal 2. 5.4. Cost-Benefit Ratio 5.5. How to Measure the Benefits? The fourth challenge is to arrive at a positive overall cost-benefit ratio for each use case. On the one hand, Calculating the TCO of an additional technology ontologies & reasoning promise benefits for enterprise represents an estimate and depends on probabilistic applications as discussed in Section 4. On the other variables. The calculation and determination of the hand, the costs, e.g., caused by technical integration or benefits is even less concrete. Therefore, the fifth chal- modeling, have to be summed up and contrasted to the lenge lies in measuring the benefits what boils down envisioned benefits. Here, the term Total Cost of Own- to finding measures for proving that an ontology-based ership (TCO) [23] comes into play which is a finan- application is beneficial compared to another solution. cial estimate. The purpose of TCO is to help enterprise The ontology and Semantic Web community has managers determine direct and indirect costs of a prod- been struggling to evaluate their contributions accord- uct or system. TCO is a management accounting con- ingly. Indeed, one hardly finds scientific methods or cept that can be used in full cost accounting or even measures to prove the benefits. Other communities ecological economics where it includes social costs. share similar struggles, however. It is equally hard to The total cost of ownership of a software application measure the benefits for knowledge management ap- has a correlation to the number of systems/components plications, conceptual modeling techniques in general, and different technologies. These are the main multi- or object-oriented programming languages compared pliers of TCO drivers in a software product. One way to procedural languages.. of formalizing the TCO is shown below Probably even harder than a sound scientific eval- uation is coming up with a business case. A business TCO ∼ TCO drivers × ]of stacks in landscape × case captures the rationale for initiating a project or ]of technologies task and is the basis of an enterprise manager’s decision for investment. A business case is often presented in a well-structured document, but may also sometimes where a TCO driver might be any administrative, come in the form of a short verbal argument or pre- operational or user specific action. More specifically, sentation. The logic of the business case is that, when- such a driver could be the required acquisition of ontol- ever resources or effort are consumed, they should be ogy experts, training of existing employees, technical in support of a specific business need. As an exam- integration costs, modeling costs, maintenance, tech- ple, consider the exemplary business need of an Oil nology buy-in or redevelopment costs. Each technol- company to increase the productivity of its in-house IT ogy (but also inside the same technology) introduces department with custom software development by 5% different tools, UIs, and system behaviors which all thus saving several thousand Euros per year. A proper end up in a higher effort to install, learn, run, and main- business case must answer to how this can be achieved tain software. The stacks in the landscape is best explained by an example: with complete ABAP and Java – under consideration of the cost-benefit ratio Daniel Oberle / Ontologies & Reasoning in Enterprise Applications 11

– including a deployment plan of available (human) soning in enterprise applications. In the following, we resources briefly present two contributions which have been pub- – defining quantifiable success criteria lished in [54] and [50], respectively. The first coun- – proposing an exit strategy ters the challenge of training by enabling software en- – concerning the business capabilities and impact gineers to develop applications on the basis of an on- – specifying the required investment tology in their familiar environment. The second ad- – including a project plan dresses the challenge of technical integration by in- – in an adaptable way, meaning the proposal can be troducing a reference architecture for using ontologies tailored to size and risk. in existing enterprise settings in a non-intrusive way. Both contributions sacrifice some of the value-adding 5.6. Training features to solve the dilemma.

Two of the TCO drivers mentioned in Section 5.4 6.1. A Flexible Transformation between OWL and are the training of existing employees and the acquisi- Ecore tion of ontology experts. Both concern the challenge of training and are further discussed below. So far, the Semantic Web community has addressed Concerning the training of existing developers, their the software engineering community by applying its expertise typically lies in one specific technology. strengths in formality and reasoning capabilities to Even if they are willing to adopt and familiarize with software engineering for productivity improvement. A a new technology or paradigm, educating them re- good example for this stream of work is given in [66]. quires training costs. Usually, the training costs are In addition, the Semantic Web community leveraged very high and managers are not willing to expend them the multitude of editors for software engineering lan- unless there is a compelling business case. Here, the guages such as UML to create ontologies [12]. How- intricacies of ontology languages and reasoning tech- ever, there has been little research on allowing soft- niques, especially description-logic-based, are note- ware engineers to incorporate existing Web ontologies worthy since they are particularly hard to understand into their familiar software engineering environments. for a non logician. [22] A potential remedy against this At first sight, the task of incorporating ontologies dilemma is discussed in Section 6.1. seems straightforward since the main modeling prim- In the case of existing information workers, they, itives (classes and relations) in common software en- too, have to be trained if they are confronted with on- gineering languages, such as UML, and ontology lan- tology interpretation or editing. As an example, even guages are identical. This task is becoming impor- if the Oil company applies a potentially intuitive tool tant since the number of significant Web ontologies is such as the Semantic Media Wiki, basic knowledge growing and software engineers are increasingly chal- about the structure of an ontology is required to effi- lenged to build software relying on such ontologies. ciently use the tool. We already learned in the running example, that the Oil Acquiring ontology experts is also problematic since and Gas industry targets information integration based the technology is still rather new and not established in on ISO 15926 [65]. Corresponding software solutions, the industry. Contrast this with the wide-spread nature such as [15], rely on the ontology but typically require of relational databases. If there is no convincing busi- manual remodeling of ISO 15926 in a software engi- ness case, an enterprise might decide to realize the use neering language. In order to prevent such manual ef- case with conventional technologies, i.e., technologies forts and still enable software engineers to use their fa- where there is expertise readily available in the com- miliar environments, we propose a transformation be- pany. A proposed remedy here is to include the top- tween OWL and the modeling languages Ecore plus ics of ontologies & reasoning in standard curricula in OCL in [54]. computer science courses. The Ecore language from the Eclipse Modeling Framework (EMF) [60] is derived from the Meta- Object Facility (MOF) [49] — an Object Manage- 6. Trading Off Added Value and Challenges ment Group (OMG) standard for meta-data definition which shares the same semantics as the UML class Our recent research has focused on trading off added modeling language. MOF and Ecore also use a sub- value and challenges of applying ontologies & rea- set of the graphical language of UML to provide di- 12 Daniel Oberle / Ontologies & Reasoning in Enterprise Applications agrammatic representations of meta-models. The Ob- ontology. The OWL API allows to plug in different ject Constraint Language (OCL) complements UML, reasoners for performing the required reasoning tasks. and, thus, Ecore, by the possibility to add logic-based constraints to elements of a class model. 6.2. Mapping Pragmatic Class Models to Reference At second sight, there are less similarities between Ontologies both languages, which makes the transformation intri- cate. For example, OWL does not rely on the unique The recent W3C Semantic Web recommendations, name assumption unlike Ecore. In addition, this lack of such as OWL, are increasingly used to specify refer- similarities opens configuration options for transform- ence ontologies. The idea of a reference ontology such ing OWL ontologies. Therefore, we allow a software as ISO 15926 is to be a generic, commonly agreed engineer to adjust the transformation between the two upon conceptual model of a domain. To this end, mod- extremes of a result simple to understand or a result eling decisions are taken in a way such that the in- that preserves as much as possible of the source ontol- tended meaning of domain terms are captured as pre- ogy. cisely as possible. One goal of reference ontologies is to facilitate and

enable information integration across IT systems. As d L e C z i

x in our running example, information integration shall O

o m + B

o t x x T be based on ISO 15926. This requires that IT systems s o o u B B C T T share information according to the reference ontology, and that they can interpret messages using that ontol- Maximum Maximum ogy. Simplicity Preservation However, existing IT systems are based on database schemas or class models that deviate from the refer-

L x x d o o e C z B

B ence ontology although they potentially capture the i O x

A A o

m +

B o o t x same domain. The reason is that class models are task- A N s o u B C A specific, with the focus on an efficient implementa- tion of an application. In contrast to reference ontolo- Fig. 6. Slider metaphor for the adjustable transformation from OWL gies, modeling decisions are geared towards a prag- to Ecore. [54] matic and efficient model. Due to those differences, one often faces the situation where class models and Depending on the use case, the users are therefore reference ontologies are incompatible in the sense that enabled to adjust the transformation meeting their re- a 1:1 mapping between them does not exist. quirements. Hence, the realization of our transforma- Since the reference ontology is typically designed tion features two “sliders” for adjusting the ABox and ex post, i.e., long after IT systems have been put in TBox transformation (cf. Fig. 6). By default, both slid- place, the IT systems’ pragmatic class models have to ers are in the initial position, where only the TBox will be mapped to such a reference ontology. Mapping be- be transformed. At the next marks of the sliders cus- tween reference ontologies and pragmatic class mod- tomized TBox and ABox transformations will be per- els is typically a non-trivial task which requires a flex- formed. In the last mark of the sliders all transforma- ible mechanism to cope with all kinds of disparities tion options, including OCL constraints, can be man- between the two kinds of models. Further, the map- aged. For example, one could specify that the prop- ping has to be bidirectional since the IT system has to erty hierarchy is not important but the transitive prop- send and receive messages expressed according to the erty characteristic should be considered which can also conceptualization underlying the reference ontology. cause a materialization of inferred knowledge about in- In addition, for coping efficiently with the disparities dividuals, if the ABox is marked by the second slider. between reference ontology and class model, the map- The transformation is realized as an Eclipse plug-in ping process itself must happen at runtime and on the and, thus, integrates seamlessly with a developer’s fa- instance level. An asset management application might miliar environment. Note, however, that this approach contain instance data about a specific pump. This in- sacrifices value-adding features discussed in Section 3. stance data has to be represented by means of ISO Our corresponding Eclipse plug-in makes use of the 15926 for information exchange with other IT systems OWL API [31] for accessing and managing an OWL via a middleware solution. Finally, the established IT Daniel Oberle / Ontologies & Reasoning in Enterprise Applications 13 systems cannot be touched in most cases. That means, [5] J. Angele, M. Erdmann, and D. Wenke. Ontology-based the mapping mechanism has to be realized in a non- knowledge management in automotive engineering scenarios. intrusive way. In M. Hepp, P. D. Leenheer, A. de Moor, and Y. Sure, editors, Ontology Management, Semantic Web, Semantic Web Ser- In [51,50], we contribute a novel approach for bidi- vices, and Business Applications, volume 7 of Semantic Web rectionally mapping between reference ontologies and And Beyond Computing for Human Experience, pages 245– class models. As required for integrating existing IT 264. Springer, 2008. systems, the approach is non-intrusive, i.e., it can [6] R. Angles and C. Gutierrez. Survey of graph database models. be implemented without having to change the class ACM Comput. Surv., 40:1:1–1:39, February 2008. [7] B. Antunes, N. Seco, and P. Gomes. Using ontologies for soft- model. The approach is flexible, i.e., it does not rely ware development knowledge reuse. In J. Neves, M. F. San- on static 1:1 mappings between the class model and tos, and J. Machado, editors, Progress in Artificial Intelligence, the reference ontology. Instead of using static map- 13th Portuguese Conference on Aritficial Intelligence, EPIA pings between the class model and the ontology, our 2007, Workshops: GAIW, AIASTS, ALEA, AMITA, BAOSW, approach uses rules which are dynamically evaluated BI, CMBSB, IROBOT, MASTA, STCS, and TEMA, Guimarães, Portugal, December 3-7, 2007, Proceedings, volume 4874 of at run-time. Lecture Notes in Computer Science, pages 357–368. Springer, 2007. [8] S. Aral, E. Brynjolfsson, and M. V. Alstyne. Information, tech- 7. Conclusion nology and information worker productivity: Task level evi- dence. NBER Working Paper 13172, The National Bureau of Economic Research, Cambridge, MA, Jun 2007. Our findings of five years trying to bring ontolo- [9] C. Bizer, T. Heath, and T. Berners-Lee. Linked data - the story gies & reasoning into commercial enterprise applica- so far. Int. J. Semantic Web Inf. Syst., 5(3):1–22, 2009. tions have been quite disillusioning. Even after a care- [10] G. Booch, J. E. Rumbaugh, and I. Jacobson. The unified mod- ful elaboration on the technology’s value-adding fea- eling language user guide. J. Database Manag., 10(4):51–52, 1999. tures, the inherent challenges usually favor the use of [11] D. Brickley and R. Guha. RDF vocabulary description lan- established and more mature technologies. guage 1.0: RDF Schema. Recommendation, W3C, Feb 2004. Thus, future research in the Semantic Web commu- http://www.w3.org/TR/rdf-schema/. nity should also pay attention to the challenges. Re- [12] S. Brockmans, R. Volz,A. Eberhart, and P. Löffler. Visual mod- searchers and practitioners can apply the matrix to eling of OWL DL ontologies using UML. In S. A. McIlraith, D. Plexousakis, and F. van Harmelen, editors, The Semantic identify which value-adding feature they actually ap- Web - ISWC 2004: Third International Semantic Web Confer- ply and contrast them with the challenges. Thus, an ed- ence,Hiroshima, Japan, November 7-11, 2004. Proceedings, ucated statement can be made why ontologies & rea- volume 3298 of Lecture Notes in Computer Science, pages soning shall be used. 198–213. Springer, 2004. Finally, more research should be conducted in trad- [13] H. C. Chan, K. K. Wei, and K. Siau. Conceptual level ver- sus logical level user-database interaction. In J. I. DeGross, ing off the challenges, e.g., by better integration with I. Benbasat, G. DeSanctis, and C. M. Beath, editors, Proceed- the software engineering community and their wide- ings of the International Conference on Information Systems, spread and mature tools. ICIS 1991, December 16-18, 1991, New York, NY, USA, pages 29–40. Association for Information Systems, 1991. [14] P. P. Chen. The entity-relationship model - toward a unified view of data. ACM Trans. Database Syst., 1(1):9–36, 1976. References [15] R. Credle, V. Akibola, V. Karna, D. Panneerselvam, R. Pil- lai, and S. Prasad. Discovering the Business Value Patterns of [1] Integrated operations and the oil & gas ontology. Technical Chemical and Petroleum Integrated Information Framework. report, The Norwegian Oil Industry Association (OLF), POSC Red Book SG24-7735-00, IBM, August 2009. Caesar Association (PCA), 2008. [16] F. Dau. Semantic technologies for enterprises. In S. Andrews, [2] A. Aamodt and E. Plaza. Case-based reasoning: Foundational S. Polovina, R. Hill, and B. Akhgar, editors, Conceptual Struc- issues, methodological variations, and system approaches. AI tures for Discovering Knowledge - 19th International Confer- Commun., 7(1):39–59, 1994. ence on Conceptual Structures, ICCS 2011, Derby, UK, July [3] B. Adida, M. Birbeck, S. McCarron, and S. Pemberton. RDFa 25-29, 2011. Proceedings, volume 6828 of Lecture Notes in in XHTML: Syntax and processing. Recommendation, W3C, Computer Science, pages 1–18. Springer, 2011. Oct 2008. http://www.w3.org/TR/rdfa-syntax/. [17] S. Decker, S. Melnik, F. van Harmelen, D. Fensel, M. C. A. [4] J. Angele. Ontoprise: Semantic web technologies at business. Klein, J. Broekstra, M. Erdmann, and I. Horrocks. The seman- In C. Pautasso, C. Bussler, M. Walliser, S. Brantschen, M. Cal- tic web: The roles of XML and RDF. IEEE Internet Comput- isti, and T. Hempfling, editors, Emerging Web Services Tech- ing, 4(5):63–74, 2000. nology, Whitestein Series in Software Agent Technologies and [18] P. F. Drucker. Landmarks of tomorrow: a report on the new Autonomic Computing, pages 1–2. Birkhäuser Basel, 2007. “post-modern” world. Harper Colophon, 1965. 14 Daniel Oberle / Ontologies & Reasoning in Enterprise Applications

[19] E. Falkenberg, W. Hesse, P. Lindgreen, B. Nilsson, J. Oei, 2010. C. Rolland, R. Stamper, F. V. Assche, A. Verrijn-Stuart, and [35] N. D. Kho. Semantics in practice. EContent — Digital Content K. Voss. FRISCO — a framework of information system con- Strategies & Resources, http://www.econtentmag. cepts — the FRISCO report. Technical report, IFIP WG 8.1 com/?ArticleID=70970, Nov 2010. Task Group FRISCO, 1998. [36] W. Kim. Object-oriented databases: Definition and research [20] D. C. Fallside and P. Walmsley. XML Schema part 0: Primer directions. IEEE Transactions on Knowledge and Data Engi- second edition. Recommendation, W3C, Oct 2004. http: neering, 2:327–341, 1990. //www.w3.org/TR/xmlschema-0/. [37] J. W. Kluewer, M. G. Skjæveland, and M. Valen-Sendstad. [21] J. Farrell and H. Lausen. Semantic annotations for WSDL and ISO 15926 templates and the semantic web. Position pa- XML Schema. Recommendation, W3C, Aug 2007. http: per for W3C Workshop on Semantic Web in Energy Indus- //www.w3.org/TR/sawsdl/. tries; Part I: Oil and Gas, http://www.w3.org/2008/ [22] D. Fensel, M.-C. Rousset, and S. Decker. Workshop on 11/ogws-papers/kluwer.pdf, 2008. comparing description and frame logics. Data Knowl. Eng., [38] M. Krötzsch, D. Vrandecic, and M. Völkel. Semantic Me- 25(3):347–352, 1998. diaWiki. In I. F. Cruz, S. Decker, D. Allemang, C. Preist, [23] B. Ferrin and R. E. Plank. Total cost of ownership models: D. Schwabe, P. Mika, M. Uschold, and L. Aroyo, editors, The An exploratory study. Journal of Supply Chain Management, Semantic Web - ISWC 2006, 5th International Semantic Web 38(3), 2002. Conference, ISWC 2006, Athens, GA, USA, November 5-9, [24] C. Forgy. Rete: A fast algorithm for the many patterns/many 2006, Proceedings, volume 4273 of Lecture Notes in Computer objects match problem. Artif. Intell., 19(1):17–37, 1982. Science, pages 935–942. Springer, 2006. [25] E. Gamma, R. Helm, R. E. Johnson, and J. M. Vlissides. [39] L. Li and I. Horrocks. A Software Framework for Match- Design patterns: Abstraction and reuse of object-oriented de- making Based on Semantic Web Technology. In Proceed- sign. In O. Nierstrasz, editor, ECOOP’93 - Object-Oriented ings of the Twelfth International World Wide Web Conference, Programming, 7th European Conference, Kaiserslautern, Ger- WWW2003, Budapest, Hungary, 20-24 May 2003, pages 331– many, July 26-30, 1993, Proceedings, volume 707 of Lecture 339. ACM, 2003. Notes in Computer Science, pages 406–431. Springer, 1993. [40] D. L. Martin, M. H. Burstein, D. V. McDermott, S. A. McIl- [26] A. Gangemi, N. Guarino, C. Masolo, A. Oltramari, and raith, M. Paolucci, K. P. Sycara, D. L. McGuinness, E. Sirin, L. Schneider. Sweetening ontologies with DOLCE. In and N. Srinivasan. Bringing semantics to web services with A. Gómez-Pérez and V. R. Benjamins, editors, Knowledge En- OWL-S. In C. L. Williamson, M. E. Zurko, P. F. Patel- gineering and Knowledge Management. Ontologies and the Schneider, and P. J. Shenoy, editors, Proceedings of the 16th Semantic Web, 13th International Conference, EKAW 2002, International Conference on World Wide Web, WWW 2007, Siguenza, Spain, October 1-4, 2002, Proceedings, volume Banff, Alberta, Canada, May 8-12, 2007, pages 243–277. 2473 of Lecture Notes in Computer Science, pages 166–181. ACM, 2007. Springer, 2002. [41] D. L. McGuinness and F. van Harmelen. OWL Web Ontol- [27] K. Gomadam, A. Ranabahu, and A. Sheth. SA-REST: Se- ogy Language Overview. Recommendation, W3C, Feb 2004. mantic annotation of web resources. Member submission, http://www.w3.org/TR/owl-features/. W3C, Apr 2010. http://www.w3.org/Submission/ [42] S. A. McIlraith, T. C. Son, and H. Zeng. Semantic web ser- SA-REST/. vices. IEEE Intelligent Systems, 16(2):46–53, 2001. [28] N. Guarino, D. Oberle, and S. Staab. What is an ontology? [43] P. Mika. Year of the monkey: Lessons from the first year of In S. Staab and R. Studer, editors, Handbook on Ontologies, SearchMonkey. In S. Fischer, E. Maehle, and R. Reischuk, Handbooks on Information Systems. Springer, 2nd edition, editors, Informatik 2009: Im Focus das Leben, Beiträge der 39. 2009. Jahrestagung der Gesellschaft für Informatik e.V. (GI), 28.9.- [29] N. Guarino and C. A. Welty. Evaluating ontological decisions 2.10.2009, Lübeck, Proceedings, volume 154 of LNI, page 387. with OntoClean. Commun. ACM, 45(2):61–65, 2002. GI, 2009. [30] A. R. Hevner, S. T. March, J. Park, and S. Ram. Design science [44] D. Norman. Cognitive engineering. In D. Norman and in information systems research. MIS Quarterly, 28(1):75– S. Daper, editors, User Centered System Design, pages 31–61. 105, 2004. Lawrence Erlbaum Associates, Hillsdale, New Jersey, USA, [31] M. Horridge and S. Bechhofer. The OWL API: A Java API for 1986. working with OWL 2 ontologies. In R. Hoekstra and P. F. Patel- [45] N. F. Noy. Semantic integration: a survey of ontology-based Schneider, editors, Proceedings of the 5th International Work- approaches. SIGMOD Rec., 33:65–70, December 2004. shop on OWL: Experiences and Directions (OWLED 2009), [46] D. Oberle. Semantic Management of Middleware, volume 1 of Chantilly, VA, United States, October 23-24, 2009, volume 529 Semantic Web And Beyond Computing for Human Experience. of CEUR Workshop Proceedings. CEUR-WS.org, 2009. Springer, 2006. [32] B. Inmon. The semantics mystery. BeyeNETWORK, [47] D. Oberle. Ontology Driven Software Development, chapter Article no. 4605, http://www.b-eye-network.com/ Conclusion — Challenges in Adopting Semantic Technolo- print/4605, Jun 2007. gies. Springer, 2012. To appear. [33] S. Kent. Model driven engineering. In M. Butler, L. Petre, and [48] D. Oberle, S. Lamparter, S. Grimm, D. Vrandecic, S. Staab, K. Sere, editors, Integrated Formal Methods, volume 2335 of and A. Gangemi. Towards ontologies for formalizing modular- Lecture Notes in Computer Science, pages 286–298. Springer ization and communication in large software systems. Applied Berlin / Heidelberg, 2002. Ontology, 1(2):163–202, 2006. [34] I. Kett, G. Schewe, I. Kett, and G. Schewe. Aussagekräftig [49] Object Management Group. Meta Object Facility (MOF) Core präsentieren. In Management Skills, pages 111–159. Gabler, Specification Version 2.4. OMG Document No. formal/2008- Daniel Oberle / Ontologies & Reasoning in Enterprise Applications 15

12-10, December 2008. 2006. [50] H. Paulheim, D. Oberle, R. Plendl, and F. Probst. An architec- [59] M. Spahn, J. Kleb, S. Grimm, and S. Scheidl. Supporting busi- ture for information exchange based on reference models. In ness intelligence by providing ontology-based end-user infor- Software Language Engineering - Fourth International Con- mation self-service. In Proceedings of the First International ference, SLE 2011, Praga, Portugal, July 3-4, 2011, Lecture Workshop on Ontology-supported Business Intelligence OBI Notes in Computer Science. Springer, 2011. ’08, pages 10:1–10:12. ACM, 2008. [51] H. Paulheim, R. Plendl, F. Probst, and D. Oberle. Mapping [60] D. Steinberg, F. Budinsky, M. Paternostro, and E. Merks. EMF: pragmatic class models to reference ontologies. In S. Abite- Eclipse Modeling Framework. Addison-Wesley Longman, 2nd boul, K. Böhm, C. Koch, and K.-L. Tan, editors, Workshops revised edition, 2008. Proceedings of the 27th International Conference on Data [61] K. Steiner, W. Eßmayr, and R. Wagner. Topic maps - an Engineering, ICDE 2011, April 11-16, 2011, Hannover, Ger- enabling technology for knowledge management. In A. M. many, pages 200–205. IEEE, 2011. Tjoa and R. Wagner, editors, 12th International Workshop on [52] V. Presutti and A. Gangemi. Content ontology design pat- Database and Expert Systems Applications (DEXA 2001), 3- terns as practical building blocks for web ontologies. In Q. Li, 7 September 2001, Munich, Germany, pages 472–476. IEEE S. Spaccapietra, E. S. K. Yu, and A. Olivé, editors, Concep- Computer Society, 2001. tual Modeling - ER 2008, 27th International Conference on [62] C. Tempich, H. S. Pinto, Y. Sure, and S. Staab. An argumen- Conceptual Modeling, Barcelona, Spain, October 20-24, 2008. tation ontology for distributed, loosely-controlled and evolv- Proceedings, volume 5231 of Lecture Notes in Computer Sci- ing engineering processes of ontologies (DILIGENT). In ence, pages 128–141. Springer, 2008. A. Gómez-Pérez and J. Euzenat, editors, The Semantic Web: [53] E. Prud’hommeaux and A. Seaborne. SPARQL query language Research and Applications, Second European Semantic Web for RDF. Recommendation, W3C, Jan 2008. http://www. Conference, ESWC 2005, Heraklion, Crete, Greece, May 29 - w3.org/TR/rdf-sparql-query/. June 1, 2005, Proceedings, volume 3532 of Lecture Notes in [54] T. Rahmani, D. Oberle, and M. Dahms. An adjustable trans- Computer Science. Springer. formation from OWL to Ecore. In D. C. Petriu, N. Rouquette, [63] T. Tran, P. Haase, H. Lewen, Ó. Muñoz-García, A. Gómez- and Ø. Haugen, editors, Model Driven Engineering Languages Pérez, and R. Studer. Lifecycle-support in architectures for and Systems - 13th International Conference, MODELS 2010, ontology-based information systems. In K. Aberer, K.-S. Choi, Oslo, Norway, October 3-8, 2010, Proceedings, Part II, volume N. F. Noy, D. Allemang, K.-I. Lee, L. J. B. Nixon, J. Gol- 6395 of Lecture Notes in Computer Science, pages 243–257. beck, P. Mika, D. Maynard, R. Mizoguchi, G. Schreiber, and Springer, 2010. P. Cudré-Mauroux, editors, The Semantic Web, 6th Interna- [55] G. Reggio, M. Cerioli, and E. Astesiano. Towards a rigor- tional Semantic Web Conference, 2nd Asian Semantic Web ous semantics of UML supporting its multiview approach. In Conference, ISWC 2007 + ASWC 2007, Busan, Korea, Novem- H. Hussmann, editor, Fundamental Approaches to Software ber 11-15, 2007, volume 4825 of Lecture Notes in Computer Engineering, volume 2029 of Lecture Notes in Computer Sci- Science, pages 508–522. Springer, 2007. ence, pages 171–186. Springer Berlin / Heidelberg, 2001. [64] M. Uschold and M. Gruninger. Ontologies and semantics for [56] H. H. Shahri, J. A. Hendler, and A. A. Porter. Software conﬁg- seamless connectivity. SIGMOD Rec., 33:58–64, December uration management using ontologies. In Proceedings of the 2004. 3rd International Workshop on Semantic Web Enabled Soft- [65] F. Verhelst, F. Myren, P. Rylandsholm, I. Svensson, A. Waaler, ware Engineering (SWESE 2007) co-located at the 4th Eu- T. Skramstad, J. Ornæs, B. Tvedt, and J. Høydal. Digital Plat- ropean Semantic Web Conference (ESWC 2007) June 6th or form for the Next Generation IO: A Prerequisite for the High June 7th, 2007 Innsbruck, Austria, 2007. Available at http: North. In SPE Intelligent Energy Conference and Exhibition, //swese2007.fzi.de. 2010. [57] J. Sheridan and J. Tennison. Linking UK government data. In [66] T. Walter, F. S. Parreiras, and S. Staab. ntoDSL: An ontology- C. Bizer, T. Heath, T. Berners-Lee, and M. Hausenblas, editors, based framework for domain-speciﬁc languages. In A. Schürr Proceedings of the WWW2010 Workshop on Linked Data on and B. Selic, editors, Model Driven Engineering Languages the Web, LDOW 2010, Raleigh, USA, April 27, 2010, volume and Systems, 12th International Conference, MODELS 2009, 628 of CEUR Workshop Proceedings. CEUR-WS.org, 2010. Denver, CO, USA, October 4-9, 2009. Proceedings, volume [58] E. P. B. Simperl, C. Tempich, and Y. Sure. A cost estima- 5795 of Lecture Notes in Computer Science, pages 408–422. tion model for ontology engineering. In I. F. Cruz, S. Decker, Springer, 2009. D. Allemang, C. Preist, D. Schwabe, P. Mika, M. Uschold, [67] A. White. Semantic web moving ever close to the ‘semantic and L. Aroyo, editors, The Semantic Web - ISWC 2006, 5th enterprise’? Gartner Blog Network, http://blogs. International Semantic Web Conference, ISWC 2006, Athens, gartner.com/andrew_white/2009/04/30, Apr GA, USA, November 5-9, 2006, Proceedings, volume 4273 of 2009. Lecture Notes in Computer Science, pages 625–639. Springer,