International Graduate School in Information and Communication Technologies DISTRIBUTED REASONING SERVICES FOR MULTIPLE ONTOLOGIES

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1 International Graduate School in Information and Communication Technologies DISTRIBUTED REASONING SERVICES FOR MULTIPLE ONTOLOGIES PhD Thesis Proposal By Andrei Tamilin 18th Cycle 2003/2004 Academic Year Department of Information and Communication Technologies University of Trento Italy

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3 DISTRIBUTED REASONING SERVICES FOR MULTIPLE ONTOLOGIES Abstract Appearance of WWW significantly influenced on the way people use and exchange information. To cope effectively with the daily growing amount of information that becomes available in WWW the evolutionary step towards the machine-processable Semantic Web was proposed. Ontology forms the backbone of the Semantic Web and is a key enabling technology in it. Research on ontologies produced a number of languages for ontology representation, stable underpinning theory, provided various effective inference engines and created a number of ontology management tools. However, one of the distinctive characteristic of ontology technology is that existing reasoning engines treat ontologies as the monolithic entities, building single integrated ontology whenever the need of dealing with multiple ontology arises. This approach present two major drawbacks. First, it does not scale, since the size of particular ontologies to be integrated can be arbitrary large and thus making the logical inference mechanisms in general to be incapable to deal with it. Second, the reasoning procedure that have to be implemented in the integrated ontology should be general enough to apply to all translations of ontologies to be integrated. That is why this thesis proposal aims to development of distributed reasoning techniques that would allow to keep ontologies separated, perform reasoning in a distributed manner, and further implement such techniques in prototype of ontology distributed reasoning services provider. Keywords Distributed Reasoning, Ontology, Ontology Mapping, Semantic Web, Description Logics.

4 DISTRIBUTED REASONING SERVICES FOR MULTIPLE ONTOLOGIES 2 1 Introduction The early nineties of the previous century were marked as the birth of World Wide Web (WWW) - network of hyper-related resources. When in 1992 Tim Berners-Lee gave the first demo of the Web to the scientific audience nobody could even imagine the scale and role that WWW would have in a nearest future. Nowadays for many people, it has become an indispensable means of providing and searching for information. The landscape of WWW found a wide commercial application and gave birth to the new business area known as electronic commerce which uses WWW as a marketplace. However, the continued rapid growth of number of documents makes it increasingly difficult to locate the relevant information while searching the Web in its current form. The reason for this unsatisfactory state of affairs is that existing Web is marked up for human consumption only. The notion of a Semantic Web [1] aims for machine-understandable Web resources, whose information can be accessed and correctly processed by intelligent automated agents. Machine processability of Web resources is achieved through annotation of its content with a semantic markup called meta-data. To make sure that different agents have common understanding of terms used in meta-data, one needs ontology in which the terms are described. Historically coming from a philosophy, term ontology gained a new sound in Artificial Intelligence community as means of conceptualizing and structuring knowledge of a particular domain of discourse. Literally several years ago the majority of research projects aimed to make the Semantic Web a possibility. As a result, the significant amount of academic research has been done to produce theoretical and practical basis for constructing the Semantic Web infrastructure. Nowadays, the goal of the research community is to make the Semantic Web a reality, ready to be applied to the industrial scope. The significant importance among the open questions on the way of realizing the Semantic Web belongs to the problem of dealing with multiple distributed ontologies. This is due to the fact that the actual situation on the Web is characterized by presence of multiple heterogeneous ontologies, each of which describes a specific domain from different perspectives and at different level of granularity, but still required to interoperate. While solving the interoperability problem, the aspects of preserving privacy and autonomy of ontologies should be taken into account. Indeed, development of well-founded ontology for the needs of particular business is expensive and time-consuming task, thus the desire of keeping the ontology autonomous and private can be a warrantable business requirement. 2 Ontologies in the Semantic Web Ontology definition. Research on ontologies is becoming a popular topic in various branches of computer science. The word ontology was borrowed from philosophy, where it means the discipline describing the nature of existence, and received a new sound and role in Artificial Intelligence. The comprehensive survey of various co-existing definitions of ontology can be found in [2]. Probably the most quoted formulation is that ontologies are formal specification of a shared conceptualization. In other words they provide a shared understanding of a domain that can be communicated between humans and across application systems. Ontologies have proven to be an essential element in broad spectrum of application areas. In particular, their importance was recognized in knowledge representation, natural language processing, knowledge management, multi-agent systems, intelligent integration of Web resources and databases, as well as cooperation of distributed enterprise applications and Web services, and etc. The special accent and the strong push for ontology development was given by the notion of Semantic Web. What ontology should be in the Web. Ontologies play a crucial role in the Web as means for adding semantics to Web documents and enabling that semantics to be used by Web applications and intelligent agents. The use of ontologies in this context requires a

5 DISTRIBUTED REASONING SERVICES FOR MULTIPLE ONTOLOGIES 3 well-designed syntax (i.e. language) compatible with Web technologies, in particular XML and RDF, well-defined semantics (i.e. theory) and finally efficient reasoning support. Well-designed syntax is a necessary condition for machine-readability of ontology, well-defined semantics is a condition for unambiguous machine-understandability, and finally reasoning support is essential for automated machine-processability. The disposition of ontology languages and underpinning theory is displayed on the envisaged stack of the Semantic Web layers given on the Figure 1. Figure 1: The Semantic Web layers. Languages. In order to satisfy the specified requirements several web ontology languages have been developed during the last few years [3]. In Europe funding has been heavily concentrated on the development of OIL (Ontology Inference Layer). In the United States, DARPA funded a similar project called DAML (Distributed Agent Markup Language). Lately these activities had been combined into a project to work on a merged ontology language, named DAML+OIL. Currently, the work on the syntactic standardization is underway to approve a ontology language for use on the WWW and based on DAML+OIL. This language received a name OWL (Web Ontology Language) [4]. OWL facilitates greater machine interpretability of Web content than that was supported by XML, RDF, and RDF Schema (RDFS). This is done by providing a set of additional constructions for describing properties and classes: among them, relations between classes, cardinality, equality, richer typing of properties, characteristics of properties, and enumerated classes. Theory. Web ontology languages have a well-founded semantics given in terms of Description Logics (DL) [5]. This enriches the ontology analysis with the instruments created in theoretical research particularly in DL and in logics in general. The most important is the presence of clear criteria for analysing reasoning processes. Reasoning support. The correspondence with DL, facilitates the use of the number of highly optimized DL reasoning algorithms for ontologies. Last decade of basic research in DL produced a number of efficient implementations of reasoning engines, like FaCT and DLP [6], Racer [7]. This chain of inference engines is recently filled up with the announced pure ontology reasoning systems, such as Pellet OWL reasoner 1. Ontology maintenance. In order to increase the usability of ontology technology while creating ontology-enhanced commercial applications, there were proposed several management systems received a collective name of ontology management system (OMS). In general OMS should utilize the access, store, modification, querying and reasoning processes with ontologies. As it is seen, the ontology management system for ontology is what a database management 1 Pellet - OWL DL Reasoner.

6 DISTRIBUTED REASONING SERVICES FOR MULTIPLE ONTOLOGIES 4 system is to data. Among others, the most notable are IBM Ontology Management System, KAON, Protégé/PROMPT, Ontolingua/Chimaera and others. 3 Emergent questions: combining multiple ontologies Today s landscape of ontology research is becoming more and more interested in the questions of dealing with multiple heterogeneous ontologies. This is due to the fact of growing number of publicly shared ontologies, each of which describes a specific domain from different perspectives and at different level of granularity. As it was pointed out earlier, the current ontology technology is mature enough to provide means for development, management and reasoning within the single ontology of particular organization. However, whenever the need of use of diverse ontologies arises, one faces with the necessity of combining heterogeneous ontologies and solving the interoperability problem on the ontology level. Integration as solution. The common approach for building an interoperability layer for accessing multiple heterogeneous ontologies is based on the notion of ontology integration [8]. The integration can be done by two ways, either by merging ontologies into a single global ontology, or by keeping ontologies separated. In both these cases, the ontologies has to be brought into the mutual agreement which can be done through the establishment of ontology mappings or precisely speaking semantic ontology mappings. The term semantic mapping defines a variety of strategies designed to show how concepts of one ontology are semantically related to concepts of other ontology. The comprehensive overview of existing approaches and systems claiming relevance to ontology mapping can be found in [9]. Requirements for mappings. As in the case of ontology technology itself in order to facilitate mapping processing and reuse there exist a strong need for having a well-designed representational, semantical model and efficient mapping-aware reasoning support. Language. The majority of languages proposed for defining ontology mappings are built on the exploitation of the notion of so called semantic bridge as a way of connecting semantically related entities. The most distinguished among them are RDF Transformation mapping meta-ontology, MAFRA s Semantic Bridge Ontology and proposed recently Contextual-OWL. The RDF Transformation mapping meta-ontology (RDFT) [10] represents a small language for representing mappings between RDF Schemas specially targeted for business integration tasks. The key role in RDFT is played by the notion of bridge which connects two concepts in different Schemas. The bridges can be of the form one-to-many and many-to-one. In the MApping FRAmework (MAFRA) for distributed ontologies in the Semantic Web [11], description and representation of ontology mappings is based on the use of Semantic Bridge Ontology (SBO). SBO is some how similar to RDFT, but it allows a wider range of semantic relations. In order to reuse the benefits proposed by OWL language for describing ontologies, recently the OWL successor language for specifying ontology mappings was proposed. This language received a name Contextual-OWL (C-OWL) [12], since it was mainly inspired by the experience accumulated in the theory of contexts. For its representational structure C-OWL inherited the syntax of OWL and added constructions for expressing so called bridge rules which allow relate concepts, roles and individuals in different ontologies. Theory. The question of formalization of mappings is not new due to ontologies. Among others, the most thoroughly studied are the ideas originated in theory of contexts, data integration, federated databases and cooperative information systems. Intuitively, the goal of data integration systems, federated databases and cooperative information systems is to provide uniform access to multiple heterogeneous information sources. The common approach is based on having a global schema which is used to provide a unified view for querying the set of local schemas. The semantic mappings between global and local schemas are expressed via views, operating the notion of Global as View (GAV), Local as View (LAV) or their mutual combination. Literally the same approach was applied for the needs

7 DISTRIBUTED REASONING SERVICES FOR MULTIPLE ONTOLOGIES 5 of ontology integration [13]. As an underpinning formalization there was used a description logics and mappings were presented in the form of conjunctive queries. The seminal work towards expressing and formalizing mappings directly between local schemas was proposed in [14]. The distinguishing feature of this work is description of inference mechanism that allow to reason on semantic interdependencies between the classes in different schemas. The most cited in the scientific literature formalizations of contexts are Propositional Logic of Context (PLC) [15] and Local Model Semantics/MultiContext Systems (LMS/MCS) [16, 17]. The distinctive feature of these works for the topic of thesis proposal is the accent on the problem of contextual reasoning. Later for the formalization distributed systems there was proposed a notion of Distributed First Order Logics (DFOL) [18]. DFOL formalizes the knowledge of particular subsystem with a first order theory, and describes the communication between subsystems through the relation between the formulas of their languages. The next step towards the evolution of context-related ideas was done with the introduction of Distributed Description Logics (DDL) [19]. There, distributed environments are represented as being composed of a set of distinct description logics interrelated between each other through a set of inference connectives. Relatively similar to DDL approach is exploited also in E- connections of Description Logics [20]. Reasoning support. When turning to the question of performing reasoning with multiple ontologies in the Semantic Web, it is possible to claim that all of the existing inference engines are based on the creation of one global ontology and performing reasoning in it. This approache, however, has two major drawbacks. First, it does not scale, since the size of particular ontologies to be integrated can be arbitrary large and thus making the logical inference mechanisms in general to be incapable to deal with it. Second, the reasoning procedure that have to be implemented in the integrated ontology should be general enough to apply to all translations of ontologies to be integrated. 4 Objectives and directions of the thesis work The problem statement. One of the distinctive characteristic of current ontology technology is that existing ontology management tools and inference engines treat ontologies as the monolithic entities. Whenever one needs to build the interoperability layer among multiple heterogeneous ontologies the common idea is implementing the global integrated ontology and performing reasoning in it. Literally the same approach is applied when one needs to reuse some of ontological knowledge while constructing new ontology. Reused ontologies must be entirely replicated in the newly created one and further reasoning is performed in such compiled ontology. In general, this approach does not scale, since the size of particular ontologies to be integrated or compiled can be arbitrarily large, thus making the logical inference mechanisms be incapable to deal with it. Development of reasoning techniques allowing to keep ontologies separated and perform reasoning in a distributed manner is emerging task on the way of realizing the scalable Semantic Web. The vision. The given situation can be reflected by envisaging a peer-to-peer ontology network, as shown on the Figure 2. In this architecture, each peer provides a set of reasoning services on a set of local ontologies and is capable to request reasoning services to other peers. The ontology manager of a peer p is capable to provide local and global ontology services. Local services involve only the ontologies local to p, while global services involve both ontologies in p and in other semantically related peers. While local services can be based on the invocation of the state of the art ontology inference engines, performing global services requires some questions to be answered. Among them are, how the established semantic mappings affects reasoning in mapped ontologies, how to formalize theoretically this affection, how to build a mapping-aware reasoning algorithm, how to implement it to be scalable?

8 DISTRIBUTED REASONING SERVICES FOR MULTIPLE ONTOLOGIES 6 Peer ontology manager Peer ontology manager Peer ontology manager Figure 2: P2P architecture for managing multiple ontologies. In each peer, circles represent maintained ontologies, arrows represent semantic relations (mappings) between ontologies. The objectives. This thesis work will aim to the following objectives: Development of logical model, algorithms, and verification tests for distributed reasoning services for multiple ontologies. The approach. In order to realize the thesis objectives a number of issues need to be addressed. In particular, the future thesis will evolve along the next main directions: Theoretical investigations. Since description logics proved itself as an appropriate formalization of ontology technology, it seems to be reasonable to select distributed description logics framework (DDL) for formalization of multiple interrelated ontologies. Starting from this logical framework, the primary goal is to formalize reasoning mechanisms in DDL and build sound and complete reasoning algorithms. Envisaged algorithms should be capable of resolving the next possible circumstances with the increasing level of complexity: 1. Simple case of cycle-free concept to concept mappings between pair of ontologies. 2. Generalization on cycle-free chains of pairwise concept to concept mapped ontologies. 3. Allowing the cyclic mappings between pair of ontologies. Requires introduction of cycle detecting and cycle resolving mechanisms. 4. Generalization on cyclic concept to concept mappings in ontology networks, i.e. case of multiple ontologies. 5. And finally, resolving the case of cyclic complex to complex concept mappings in the ontology networks. Implementation. The implementation part of the thesis can be divided into two main subtasks: 1. Realizing the distributed ontology inference engine on the base of the core DDL reasoning algorithms. 2. Developing an infrastructure as it was envisaged on the Figure 2. The initial data for the developing inference engine are ontologies and mappings. Since the OWL is on its way to become a standard for expressing ontologies on the Web, and C-OWL is the mapping-aware extension of OWL, the distributed inference engine to be developed will be oriented on these language formats. Whereas there are plenty of implementations of OWL processors, named parsers, the recently proposed C-OWL

9 DISTRIBUTED REASONING SERVICES FOR MULTIPLE ONTOLOGIES 7 language does not have any technical support. That is why the C-OWL parser should be implemented. The primary goal on the way of implementation of the infrastructure is developing the prototype of peer ontology manager. This manager should be capable of providing the range of services, like: 1. Loading/deleting ontologies and mappings. 2. Locating ontologies. 3. Checking local/global concept consistency. 4. Checking local/global concept subsumption. 5. Performing local/global classification. 6. Checking local/global entailment. Here, the locality assumes the invocation of core ontology reasoning functionality and globality assumes invocation of developed distributed inference engine. Evaluation. The goal of the evaluation part of the thesis will be to develop a set of test cases for the verification of feasibility of each version of developed distributed inference engine. To evaluate the usability of proposed architecture the ease of installation and management of peer ontology manager will be examined as well. Dissemination. Submit the theoretical results, the system description and testing to top level conferences and journals of the main area. Among them are International Semantic Web Conference 2004 and 2005, DL 2005, IJCAI-05, and Journal of Semantic Web. Final results. The final implementation result of the thesis work will represent an installation pack of prototype peer ontology manager ready to be set up on a particular computer and provide reasoning services. 5 Potential impacts This thesis work will potentially contribute to the state of the art of several research areas. Among them are formal techniques for knowledge representation and reasoning, ontologies and the Semantic Web. The impacts can be summarized in the following points: Distributed reasoning algorithm. Theoretical research of reasoning mechanisms in DDL and proposal of feasible implementation of the reasoning algorithm will make the DDL a full-fledged framework. Mechanism for scaling. Theoretical and practical results of this thesis work could provide the needed scaling formalism for ontology technology. In particular, DDL framework with reasoning support can serve as a basis for formalizing modularization of ontologies, providing the way of connecting ontological modules and giving a scalable reasoning through the distribution (dissemination) of reasoning questions among such modules. The research and the system developed in the thesis will explicitly contribute to the Knowledge Web project 2. Prototype of a distributed reasoning architecture for the Semantic Web. This thesis work will contribute to the realization of the architecture described above on the Figure 2 with the following main points: it will have the underpinning logical framework capable of capturing the behavior of the overall system, sound and complete reasoning algorithms will be proposed for the various topologies of peer-to-peer networks and finally the implementation will be done. 2 Knowledge Web - Realizing the Semantic Web is the Network of Excellence under European Commission 6 th FP. See

10 DISTRIBUTED REASONING SERVICES FOR MULTIPLE ONTOLOGIES 8 6 What has been done so far On the time of writing thesis proposal the set of preliminary results has been already done. Namely, The DDL framework was carefully revised and set of first results on the way of formalization the reasoning mechanisms were added. Sound and complete distributed reasoning algorithm was developed for the generalized case of cycle-free chains of pairwise concept to concept mapped ontologies. First version of C-OWL language format parser was written. It was built on the top of Jena 3 - A Semantic Web Framework for Java. On the base of developed algorithm the first prototype of distributed inference engine was implemented. The kernel of the engine is formed by Pellet OWL DL reasoner. Openness of its source code and its implementation in java made it a good candidate for our prototype. Extension of core Pellet with elements of developed reasoning algorithm transformed Pellet to its distributed successor which was named D-Pellet. The correctness of the algorithm was tested on the simple test case. As an input data for the test case there were taken two ontologies, among which one was weakly axiomatized (built of pure enumeration of concepts) and the other one had rich hierarchy. Manually establishing semantic mappings between these ontologies (the possibility of using automated tools is considered in the future) and further running global classification service of D-Pellet it was possibly to demonstrate how the concepts of weak ontology could be organized into the hierarchy by means of established semantic mappings. 7 Further work schedule The supposed work schedule diagram is given on the Figure Figure 3: Thesis work schedule.

11 DISTRIBUTED REASONING SERVICES FOR MULTIPLE ONTOLOGIES 9 Here on the figure, two milestones were emphasized with expected deliverables: D 1 -first version of implemented cycle-aware prototype, and D 2 - the final version of complete prototype capable of resolving cyclic complex concept mappings. 8 List of publications The preliminary results mentioned above in the Section 6 were described in more details in the article submitted recently to the International Workshop on Description Logics: [1] L.Serafini and A.Tamilin. Local tableaux for reasoning in distributed description logics. Submitted to International Workshop on Description Logics (DL 04), References [1] T. Berners-Lee, J. Hendler, and O. Lassila. The Semantic Web: A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities. The Scientific American, 284(5):34 43, [2] N. Guarino. Formal Ontology and Information Systems. In Proceedings of the International Conference on Formal Ontology in Information Systems (FOIS 98), Trento, Italy, [3] A. Gomez-Perez and O. Corcho. Ontology languages for the Semantic Web. IEEE Intelligent Systems, 17(1):54 60, [4] G. Antoniou and F. van Harmelen. Web Ontology Language: OWL. In S. Staab and R. Studer, editors, Handbook on Ontologies in Information Systems. Springer-Verlag, [5] F. Baader, D. Calvanese, D. McGuinness, D. Nardi, and P. F. Patel-Schneider. The Description Logic Handbook: Theory, Implementation and Applications [6]P.F.Patel-Schneider and I.Horrocks. DLP and FaCT. Lecture Notes in Artificial Intelligence, pages Springer-Verlag, June [7] V. Haarslev and R. Moller. RACER system description. In R. Goré, A. Leitsch, and T. Nipkow, editors, Automated Reasoning : First International Joint Conference on Automated Reasoning (IJCAR 2001), volume 2083 of Lecture Notes in Artificial Intelligence, pages Springer-Verlag, [8]H.S.Pinto,A.Gómez-Pérez, and J. P. Martins. Some issues on ontology integration. In Proceedings of the Workshop on Ontologies and Problem Solving Methods (IJCAI-99), [9] Y. Kalfoglou and M. Schorlemmer. Ontology mapping: the state of the art. The Knowledge Engineering Review, 18(1):1 31, [10] B. Omelayenko. RDFT: A Mapping Meta-Ontology for Business Integration. In Proceedings of the Workshop on Knowledge Transformation for the Semantic for the Semantic Web at the 15th European Conference on Artificial Intelligence (KTSW2002), pages 77 84, Lyon, France, 23 July [11] A. Maedche, B. Motik, N. Silva, and R. Volz. Mafra - a mapping framework for distributed ontologies. In Proceedings of Knowledge Engineering and Knowledge Management (EKAW-02), volume 2473 of Lecture Notes in Computer Science. Springer, 2002.

12 DISTRIBUTED REASONING SERVICES FOR MULTIPLE ONTOLOGIES 10 [12] P. Bouquet, F. Giunchiglia, F. van Harmelen, L. Serafini,, and H. Stuckenschmidt. C- OWL: Contextualizing ontologies. In D. Fensel, K. Sycara, and J. Mylopoulos, editors, Lecture Notes in Computer Science, volume 2870, pages Springer, June [13] D. Calvanese, G. De Giacomo, and M. Lenzerini. A framework for ontology integration. In Proc. of the First Semantic Web Working Symposium, pages , [14] T.Catarci and M.Lenzerini. Representing andusing interschema knowledge in cooperative information systems. International Journal of Intelligent and Cooperative Information Systems, 2(4): , [15] J. McCarthy. Notes on formalizing context. In Artificial Joint Conference on Artificial Intelligence (IJCAI-93), pages , [16] C. Ghidini and F. Giunchiglia. Local Model Semantics, or Contextual Reasoning = Locality + Compatibility. Artificial Intelligence, 127(2): , [17] F. Giunchiglia. Contextual reasoning. Epistemologia, special issue on I Linguaggi e le Macchine, XVI: , [18] C. Ghidini and L. Serafini. Distributed First Order Logics. In D.M. Gabbay and M. De Rijke, editors, Frontiers of Combining Systems 2, number 7 in Studies in Logic and Computation, pages , Hertfordshire, England, UK, Research Studies Press Ltd. Baldock. [19] A. Borgida and L. Serafini. Distributed Description Logics: Assimilating Information from Peer Sources. Journal of Data Semantics, (1): , [20] O. Kutz, C. Lutz, F. Wolter, and M. Zakharyaschev. E-connections of Description Logics. In Proceedings of the 2003 International Workshop on Description Logics (DL-03), 2003.