Cerebra Construct: Inferences for End Users
Authoring for the HTML web has become the daily work for millions of people. The Semantic Web is a new layer of the Internet that enables semantic representation of domain knowledge in a formalized, distributed and standardized way . The Semantic Web seems to be a great idea. The only question is, why is it not taking off. Why is virtually no one creating content for this new powerful web? Authoring for the Semantic Web is a much harder task than writing text, because it requires a formalization of the domain knowledge in a logical format, needed for machine-processing.
This paper shows, how two well established Semantic Web technologies are being combined, utilizing W3C recommendations, in order to exploit the full potential of the Semantic Web for business applications and end users. Cerebra Construct is a combination of the MS-Visio based modeling tool SemTalk , and Cerebra, a commercial inference engine originating from the well known FACT Reasoner . Together we are providing a modeling and inference framework which has the strength of a Description Logic Inference Engine but is as simple to use as MS-Visio.
Graphical modeling solutions have been built with SemTalk for Business Process Modeling , Data Warehouse modeling, SAP product configuration etc. These solutions are exploiting the idea of the Semantic Web simply by creating hyperlinked reference models with unique names, which are published on an intranet or on the internet. End users can select objects from those libraries in order to build consistent models by using drag & drop within MS-Visio. SemTalk covers the core RDFS modeling constructs like subclassing and properties. It comes with an internal inference engine which helps the user to keep a local model consistent. SemTalk is mainly used by process and product experts, which do not have a special education in ontology modeling.
Let us assume that we have two business processes modelled. One of them describes the order entry and the other one the order processing. Let us further assume that there is a consistency rule stating that only confirmed orders can be processed. Both processes may have been made independently of each other by their respective process manager, but refer to the global object order. The problem is, that the order processing department usually begins working on a prototype after they made a bid. Even if there are no local consistency problems in any of the models, there is a conflict in the union of the two models. This is a typical BPM problem that can be detected using Semantic Web inference engines such as Cerebra.
Cerebra is a Description Logic based inference engine with reasoning support for the Semantic Web recommendation OWL. Such an engine is required to support the creation and maintenance of large scale ontologies. The inferencing technology minimizes the complexity and the number of direct relationships needed to represent the business and data models. It also ensures consistency across multiple models, departments and business partners. The engine detects inconsistencies in respect to specified axioms like disjointness or equivalence.
"The software industry is building an alphabet but hasn't yet invented a common language" Hasso. Plattner SAP AG, 2002 . Plattner characterizes the typical use case for solutions in an EAI or SCM scenario where database schemas or business object models of various sources have to be mapped onto a common ontology. Semantic integration using a common vocabulary is one of the greatest challenges for current IT systems. Through Cerebra Inference Engine the enterprises will be able to process data based on semantics without restricting the vocabulary, allowing the identification of the available resources and services in their field. This will provide a dynamic environment where resources can be exchanged to maintain the integrity of the value-chain as new resources become available or existing resources become redundant.
Reasoning engines are used in non-graphical ontology modeling tools like OilEd , OntoEdit  or Protege , which rely on an Edit-Compile-Reasoning-Edit cycle. They are a great improvement to textual creation of ontologies in languages such as SHIQ, F-Logic and others. Even if some of these modeling tools can generate graphical representations of the ontology, they are not a WYSIWYG real-time, graphical modeling environment like mind mapping or BPM tools. Our experience shows that the process of creating ontologies is an active process of collaboration; discussions, argumentations, presentations and politics, involving domain experts with fairly divergent points of view. They need a real-time, graphical tool to arbitrate their interactions. Tool support for ontology creation should therefore follow the design-pattern of a white-board rather than a database or an Excel sheet.
In the collaboration between Semtation and Network Inference, SemTalk has been extended to support axioms according to the OWL specification using graphical symbols and advanced reasoning.
Complex logical expressions can be made in a graphical notation similar to nested blocks. The expressions are used in two ways: as assertions for the ontologies and as queries for testing and validating the ontologies. Traditionally the definition of logical queries is a task which can only be fulfilled by a few experts. We have described a similar approach for Ontobroker's F-Logic in . The queries are expressed in XQUERY and processed by Cerebra.
Figure 1 Construct User Interface
Through Cerebra Construct, ontologies can be mapped to database schemas (see Figure 1). This gives us the chance to specify queries against the databases using a common abstract ontology instead of taking the details of various database schemas into account.
SemTalk and Cerebra Construct are embedded in the MS-Office tool Visio on the MS-Windows platforms. The Cerebra product suite provides a reasoning web service which can be used from the MS-Office platform. Construct is communicating with the Cerebra Inference Engine via a SOAP based interface. This architecture ensures a highly scaleable system configuration, since Cerebra can be used on high-end hardware in order to consolidate large and distributed ontologies from multiple sources. The engine can also reflect instance data from databases or OLAP systems.
SemTalk maintains a personal view on the enterprise wide ontology in a Visio diagram. Some parts of this ontology will be public and commonly agreed upon, other parts might be overlapping. Cerebra ensures consistency of the user model with the corporate ontology, even in cases where it’s not obvious. It will detect:
· if another user has defined an equivalent concept even if he is using a different name
· cases in which a constraint like a conjunction has been violated.
Cerebra Construct will mark the detected classes in the user model, which can never be instantiated, overseeing the complete picture gained from multiple ontologies.
The presented work-in-progress greatly benefits from the availability of Semantic Web recommendations like OWL.
Cerebra is intended to be used as an inferencing middle-ware. It is used to integrate hybrid IT-systems and databases by the use of an ontology layer. Ontologies are the critical success factor for those systems, since they can only be as good as their ontologies are. In times of a constantly growing demand for ontologies Cerebra Construct allows us to move the responsibility for knowledge specification from highly skilled modeling experts to the end-users who have the domain knowledge.
SemTalk has introduced the idea of the Semantic Web to several common modelling scenarios like process models for E-Government, product configuration or project management. The technology made available through Cerebra Construct will add reasoning to those modelling applications and thereby moving them one step closer to the Semantic Web.
Fillies, C., Wood-Albrecht, G., Weichhardt, F., A Pragmatic Application of the Semantic Web Using SemTalk. WWW2002, May 7-11, 2002, Honolulu, Hawaii, USA ACM 1-5811-449-5/02/0005
I. Horrocks. Benchmark Analysis with FaCT. In Proc. TABLEAUX 2000, pages 62-66, 2000.
Fillies, C., Weichhardt, F., Towards the Corporate Semantic Process Web. BPM2003 (submitted) http://www.semtalk.com/pub/Towards.htm
OWL Web Ontology Language 1.0 Reference: W3C Working Draft 29 July 2002, 12 No-vember 2002. Mike Dean, Dan Connolly, Frank van Harmelen, James Hendler, Ian Horrocks, Deborah L. McGuinness, Peter F. Patel-Schneider, and Lynn Andrea Stein eds. Latest version is available at http://www.w3.org/TR/owl-ref/
Sean Bechhofer, Ian Horrocks, Carole A. Goble, Robert Stevens: OilEd: a Reason-able Ontology Editor for the Semantic Web. Description Logics 2001
S. Staab and A. Maedche. Ontology engineering beyond the modeling of concepts and relations. In Proc. of the ECAI'2000 Workshop on Application of Ontologies and Problem-Solving Methods, 2000.
W. E. Grosso et al. Knowledge Modeling at the Millennium (The Design and Evolution of Protege-2000). In Proc. of KAW99, 1999.
Fillies, C.; Sure,Y: On Visualizing the Semantic Web in MS Office, IV02 LONDON • ENGLAND