The Semantic Web: Yet Another Hip?

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1 to appear in Data and Knowledge Engineering, 2002, The Semantic Web: Yet Another Hip? Ying Ding, Dieter Fensel, Michel Klein, and Borys Omelayenko Division of Mathmatics & Computer Science, Vrije Universiteit Amsterdam, De Boelelaan 1081a, 1081 HV Amsterdam, NL Abstract. Currently, computers are changing from single, isolated devices into entry points to a worldwide network of information exchange and business transactions called the World Wide Web (WWW). For this reason, support in data, information, and knowledge exchange has become a key issue in current computer technology. The success of the WWW has made it increasingly difficult to find, access, present, and maintain the information required by a wide variety of users. In response to this problem, many new research initiatives and commercial enterprises have been set up to enrich available information with machine processable semantics. This semantic web will provide intelligent access to heterogeneous, distributed information, enabling software products (agents) to mediate between user needs and the information sources available. This paper summarizes ongoing research in the area of the semantic web, focusing especially on ontology technology. 1 Introduction The World Wide Web (WWW) has drastically changed the availability of electronically accessible information. The WWW currently contains some 3 billion static documents, which are accessed by over 300 million users internationally. At the same time, this enormous amount of data has made it increasingly difficult to find, access, present, and maintain the information required by a wide variety of users. This is because information content is presented primarily in natural language. Thus, a wide gap has emerged between the information available for tools aimed at addressing the problems above and the information maintained in human-readable form. Searching and presenting information: Even now, finding the right piece of information is often very difficult. Searches are imprecise,

2 to appear in Data and Knowledge Engineering, 2002, often yielding matches to many thousands of pages. Moreover, users face the task of reading the documents retrieved in order to extract the information desired. A related problem involves the maintenance of web sources, which has become very difficult. The burden on users to maintain consistency is often overwhelming. This has resulted in a vast number of sites containing inconsistent and/or contradictory information. Electronic commerce: The process of automating electronic commerce has been greatly hampered by current methods of presenting information. Shopping agents use wrappers and heuristics to extract product information from weakly structured textual information. However, development and maintenance costs are high, and the services provided limited. In response to this problem, many new research initiatives and commercial enterprises have been set up to enrich available information with machine-processable semantics. Such support is essential for bringing the web to its full potential. Tim Berners-Lee, Director of the World Wide Web Consortium, referred to the future of the current WWW as the semantic web -- an extended web of machine-readable information and automated services that extend far beyond current capabilities ([2], [16]). The explicit representation of the semantics underlying data, programs, pages, and other web resources, will enable a knowledge-based web that provides a qualitatively new level of service. Automated services will improve in their capacity to assist humans in achieving their goals by understanding more of the content on the web, and thus providing more accurate filtering, categorization, and searches of information sources. This process will ultimately lead to an extremely knowledgeable system that features various specialized reasoning services. These services will support us in nearly all aspects of our daily life -- making access to information as pervasive, and necessary, as access to electricity is today. A key enabler for the semantic web is on-line ontological support for data, information and knowledge exchange. Given the exponential growth of the information available online, automatic processing is vital to the task of managing and maintaining access to that information. Used to describe the structure and semantics of information exchange, ontologies will play a key role in areas, such as knowledge management, B2B e-commerce and other such burgeoning electronic enterprises (cf.

3 to appear in Data and Knowledge Engineering, 2002, Fensel in [10]). The contents of this paper are structured as follows. Section 2, presents an introduction to the basic infrastructure of the semantic web. It consists of: (1) web-enabled languages that allow machine-processable semantics of data and (2) tools capable of processing that data. Section 3, discusses one technology in more detail: ontologies that can be characterized as the backbone of the semantic web. This is because they provide formal and consensual domain models that can be used to interpret data both by humans and computers. Section 4 deals with potential areas of application, focusing on knowledge management and electronic commerce. Finally, Section 5 presents conclusions and future trends. 2 Semantic Web Technology In this section, we will sketch various languages and tools that provide the basic infrastructure for the semantic web. 2.1 Languages on the Web: Adding structure and meaning An important requirement for machine-processable information is structure in data. There are several ways to structure data. Databases, for example, use tables with attributes, such as structuring mechanisms. The main structuring technique used on the Web is document markup. This means that certain sequences of characters in a document contain information indicating the role of the document s content. The markup describes the document s data layout and logical structure and makes the information self-describing, in a sense. The markup often takes the form of words between pointy brackets, called tags for example, <title> or <h1>. A markup language specifies the markup tags that can used, how they can be combined, and what their meaning is. HTML is the best-known and most widely spread language on the web. It provides tags to specify abstract-rendering information for text documents. For example, text between <h1> and </h1> is considered as a heading of the first level, while text between <li> and </li> is regarded as a list item. Browsers use this information to format web pages appropriately. The only information that HTML provides, however, is about the rendering of content. Often, this is not sufficient for the advanced

4 to appear in Data and Knowledge Engineering, 2002, automated services that are foreseen on the Semantic Web. Applications will need specialized markups that specify the role of the parts of the content relevant to them. For example, for a price comparison application on the Web, it is useful if all prices are annotated with a tag such as <price>. It is necessary to specify the meaning of the tags XML The extensible Markup Language (XML) is a meta language that meets the need to define application-specific markup tags. XML is a mechanism for standardized representation of other languages. In other words, XML only provides a data format for structured documents, without specifying an actual vocabulary. A Document Type Definition (DTD) or an XML Schema might be used to specify this vocabulary and to define the combinations of tags allowed. A language defined in XML is called an XML application. XML does not imply a specific interpretation of the data. Of course, on account of the tag's names, the meaning of a piece of XML - for example <name>john</name> - may seem obvious to human users, but it is not formally specified! The only legitimate interpretation for XML code is that it contains named entities with sub-entities and values; that is, every XML document forms an ordered, labelled tree. This generality is both XML s strength and its weakness. While it is possible to encode all kinds of data structures in an unambiguous syntax, XML does not specify the data s use and semantics. The parties that use XML for their data exchange must agree beforehand on their vocabulary, its use, and its meanings RDF and RDF Schema XML is a standard mechanism to structure data. The Resource Description Framework (RDF) is a mechanism to tell something about data, i.e., to give meaning to it. As its name indicates, it is not a language but a model for representing data about things on the Web. This type of data about data is called meta data. Things are resources in RDF vocabulary. Resources are all things that have an identifier on the Web, ranging from the specific, identifiable parts of a document to things like persons or companies.

5 to appear in Data and Knowledge Engineering, 2002, Basically, the RDF data model is quite simple. It consists of statements about resources, encoded as object-attribute-value triples. The objects are resources, and the values are resources or strings. For example, a statement that John is the author of the article in a specific URL, would be represented by the triple: author, John. Attributes, such as author, are called properties. RDF Schema is a simple type of system for RDF. It provides a mechanism to define domain-specific properties and classes of resources to which those properties can be applied. The basic modelling primitives in RDF Schema consist of: (1) class definitions and subclass-of statements (which together allow the definition of class hierarchies); (2) property definitions and sub-property-of statements (to build property hierarchies); (3) domain and range statements (to restrict the possible combinations of properties and classes); and (4) type statements (to declare a resource as an instance of a specific class). These primitives can be used to build a schema for a specific domain. Together, RDF and RDF Schema provides a simple knowledge representation mechanism for Web resources. However, RDF Schema is quite simple compared to full-fledged knowledge representation languages. It has only a few modelling primitives, and does not feature exact, logical, semantics. Richer languages are needed for more precise specifications of data meanings. Such languages will be discussed in the section about ontologies. 2.2 Tools In the discussion below, we will describe parsers and database technology of the semantic web. (For more details see [22]) RDF Parsers RDF parsers provide the basic support in parsing different RDF serializations, accessing RDF triples via programming interfaces or queries, and providing basic operations with the triples. The SiRPAC 1 RDF parser SiRPAC is a set of Java classes that can parse 1

6 to appear in Data and Knowledge Engineering, 2002, RDF/XML documents into the three-tuples of the corresponding RDF data model. The parser has evolved in several versions and has become a de-facto standard in Java-based RDF development. The Profium 2 tool targets the same objective as SiRPAC and provides similar functionality. It is available as a Perl script in addition to its Java implementation. The perllib 3 W3C library is born of a need to implement an RDF infrastructure at W3C. This is currently used for access control and annotations, but will be used for a more diverse group of applications as needs evolve. The library is implemented in Perl and is now under prerelease preparation. There are a number of other RDF-supporting tools and libraries, as well as a number of lists of those tools. (See Dave Beckett's RDF, page 4 or W3C RDF page 5 ). These parsers serve as a basic tool to build RDF repositories and databases. ICS-FORTH 6 Validating RDF Parser is a tool for parsing RDF Statements and validating them against an RDF Schema. The parser analyses syntactically the statements of a given RDF/XML document according to the RDF Model and Syntax specification. The tool checks whether both the RDF Schemata and related Metadata instances satisfy the semantic constraints implied by the RDF Schema Specification. The tool possesses the following features: Checks semantic and syntax for both RDF Schemata and Metadata instances Performs statement validations across several RDF/XML namespaces, Understands embedded RDF in HTML or XML, and Offers full Unicode support as it was entirely developed in Java. This parser creates a bridge between RDF Schemas and corresponding RDF instances and is used in RDF Schema repository projects

7 to appear in Data and Knowledge Engineering, 2002, RDF Repositories RDF repositories are built on top of RDF parsers. They also feature additional functions for storing large amounts of triples in relational databases, reformulating of RDF queries to SQL database queries, and providing efficient RDF parsing and interchange functions. The rdfdb 7 project is intended to be a simple, scalable, open-source database for RDF. This project aims to build a database capable of: Supporting a graph-oriented API via a textual query language similar to SQL. Supporting different methods of filling the database. Operating in a scalable way. Supporting RDF Schemas. Support certain basic forms of inference. Providing both C and Perl access to the database. The rdfdb repository, which is now under intensive development, features the following basic RDF database operations: database creation, introduction of triples into databases, namespace manipulation and database querying. Query interface supports only one query command, which enables retrieval of a set of triples that satisfy a certain set of constraints. Support for RDF Schemas and inference is being planned, but has not yet been designed or implemented Sesame: an RDF Schema Repository and Querying Tool An RDF repository is normally targeted at queries of the type select the triples that specify property A for resource B, having value C, which belongs to a certain value range. An RDF Schema querying repository need to process queries of the type select subclasses of class A, which have property B attached to them, where B has a range equal to classes C and D. These queries deal with the schema information, not with the triples of instance documents. An integrated RDF and RDF Schema repository may answer queries that use both schema and instance 7

8 to appear in Data and Knowledge Engineering, 2002, Fig. 1 Querying Sesame. information, such as select instances of class a, which have property B defined, and property C with value D. These queries bring a qualitatively new functionality to RDF storage and retrieval facilities. Sesame 8 is an RDF schema-based repository and querying service, which is being developed by Aidministrator 9 in connection with the On-To- Knowledge 10 project [12], [37]. Using an object-oriented query language RQL, Sesame provides a substantial functionality in querying RDF instances (ontology instances) and RDF Schemas (ontologies). The ontologies and their instances are uniformly accessible by means of RQL queries. The RQL language implemented in Sesame makes it possible to query class definitions and class instances by their properties. That, in turn, supports an extensive set of querying expressions. Figure 1 presents an example of a query formulated to the Sesame service, which asks for all instances of a certain property

9 to appear in Data and Knowledge Engineering, 2002, Ontologies Ontologies are an essential backbone technology because they provide an important feature: they interweave formal semantics understandable to a computer with real world semantic understandable to humans. In light of that, we will take a closer look at them in this section. We will answer the question of what ontologies are, and describe representation languages, tools, and issues related to versioning and heterogeneity. 3.1 What are Ontologies? Ontologies were developed in Artificial Intelligence to facilitate knowledge sharing and re-use. Since the early nineties, ontologies have become a popular research topic investigated by several artificial intelligence research communities, including Knowledge Engineering, natural-language processing and knowledge representation. More recently, the concept of ontology is also becoming widespread in fields such as intelligent information integration, cooperative information systems, information retrieval, electronic commerce, and knowledge management. The reason ontologies are becoming so popular is largely due to what they promise: a shared and common understanding of some domain that can be communicated between people and application systems. Because ontologies aim at consensual domain knowledge, their development is often a cooperative process involving different people, possibly at different locations. People who agree to accept an ontology are said to commit themselves to that ontology. Many definitions of ontologies have been proposed over the last decade. In our opinion, however, the one that best characterizes the essence of an ontology is based on the related definitions given by [19]: An ontology is a formal, explicit specification of a shared conceptualization. A conceptualization refers to an abstract model of some phenomenon in the world, which identifies the relevant concepts of that phenomenon. Explicit means that the type of concepts used and the constraints on their use are explicitly defined. Formal refers to the fact that the ontology should be machine-understandable. Different degrees of formality are possible. Large ontologies, such as WordNet 11 provide a thesaurus for over 100,000 terms, which are explained in natural 11

10 to appear in Data and Knowledge Engineering, 2002, language. On the other end of the spectrum is CYC 12, which provides formal axiomating theories for many aspects of common sense knowledge. Shared reflects the notion that an ontology captures consensual knowledge; that is, it is not restricted to some individual, but accepted by a group. 3.2 Languages RDF Schema is quite simple as compared to full-fledged knowledge representation languages. Richer languages are needed for more precise specifications of data meanings. OIL 13 [13] is such an enhanced language. OIL is the product of an initiative to integrate intuitive modelling primitives, Web languages, and formal semantics into one language. One of the central design ideas in OIL is its onion model (see Figure 2). No single language will ever satisfy all human requirements. OIL s onion model offers languages of varying complexity; this allows applications to select the degree of complexity they require. One of its dialects, called DAML+OIL 14, reflects a broad European and (US) American consensus on modelling primitives for the semantic web and will form the foundations for standardization by the W3C 15.BothOIL Fig. 2 The onion model to control complexity

11 to appear in Data and Knowledge Engineering, 2002, and DAML+OIL are defined as an extension to RDFS, which makes them largely compatible with plain RDFS [4]. Their main additions to RDFS are formal semantics, based on a Description Logic, and more advanced modelling primitives, such as Boolean expressions and some axioms. Further extensions to those languages have been devised. For example, Triple 16 is a recent development of an Open-Source Query and Inference Language and Engine for Semi-Structured Data (RDF and DAML/OIL). The language is a successor to SiLRI [6], a simple logic-based RDF interpreter. Triple has been proposed as a new RDF query and inference language, which provides full support for resources and their namespaces, models represented with sets of RDF triples, reification, RDF data transformation, and an expressive rule language for RDF. The language was intended for use with any Horn-based inference engine. 3.3 Tools Effective and efficient work with the semantic web must be supported by advanced tools that draw on the fullest potential of this technology. In particular, we need the following elements: Editors and semi-automatic construction to build new ontologies. Annotation tools to link unstructured and semi-structured information sources to meta data. Reasoning Service: Instance and Schema Inferences enable advanced query answering services, support ontology creation and help in plotting maps of different terminologies. Re-using and Merging Ontologies: Ontology library systems and Ontology Environments help to create new ontologies by re-using existing ones. The section below takes a brief look at examples of these technologies

12 to appear in Data and Knowledge Engineering, 2002, Fig. 3 The Protégé editor Editors and semi-automatic construction Ontology editors help human knowledge engineers to build ontologies. Ontology editors support the definition of concept hierarchies, the definition attributes for concepts, and the definition of axioms and constraints. They must provide graphical interfaces and must conform to existing standards in web-based software development. They enable inspecting, browsing, codifying and modifying ontologies and thus supports the ontology development and maintenance task. An example system is Protégé [18] (see Figure 3). Manually building ontologies is a time-consuming task. It is very difficult and cumbersome to derive ontologies manually from data. This appears to be true regardless of the type of data involved. Natural language texts exhibit morphological, syntactic, semantic, pragmatic and conceptual constraints that interact to convey a particular meaning to the reader. Tools that learn ontologies from natural language exploit the

13 to appear in Data and Knowledge Engineering, 2002, interacting constraints on the various language levels (from morphology to pragmatics and background knowledge) in order to discover new concepts and stipulate relationships between concepts. Thus, aside from offering editing support, semi-automated tools in ontology development help to improve the overall productivity. An example system is Text-To- Onto [29], which provides an integrated environment for the task of learning ontologies from text Annotation tools Ontologies can be used to describe a large instance population. Tools help the knowledge engineer to establish such links by: (1) linking an ontology to a database schema or in the case of structured data deriving a database schema from an ontology; (2) deriving an XML DTD, an XML schema, and an RDF schema from an ontology where semi-structured data is involved; (3) adding ontological annotation to unstructured data manually or semi-automatically. For further details see [28] Reasoning Service Inference engines for ontologies can be used to reason about instances of an ontology or ontology schemes. Reasoning about instances of an ontology, for example, involves deriving a certain value for an attribute applied to an object. These inference services are the equivalent of SQL query engines for databases, but provide stronger support (for example, recursive rules). An example system is Ontobroker ([11]) which has now been commercialized by Ontoprise 17 the company. Reasoning about concepts of an ontology, for example, automatically involves deriving the right position for a new concept in a given concept hierarchy. FaCT (Fast Classification of Terminologies) [24] can be used to derive concept hierarchies automatically. It is a Description Logic (DL) classifier that makes use of the well-defined semantics of OIL. As described below, both types of reasoners help to build ontologies and to use them for advanced information access and navigation. 17

14 to appear in Data and Knowledge Engineering, 2002, Ontology Libraries and Environments Assuming that the world is full of well-designed modular ontologies, constructing a new ontology is a matter of assembling existing ones. Instead of building ontologies from scratch, we would re-use existing ontologies. This requires two types of tools: (1) tools to store and retrieve existing Ontologies, and (2) tools that help manipulate existing Ontologies. Task of managing Ontologies in an efficient and proper way to fortify their re-use of knowledge is far from trivial. Ontology library systems are an important environment in grouping and re-organizing ontologies for further re-use, integration, maintenance, mapping and versioning. An ontology library system offers various functions for managing, adapting and standardizing groups of ontologies. It should be easily accessible and offer efficient support for re-using existing, relevant ontologies and standardizing them based on upper-level ontologies and ontology representation languages. For this reason, an ontology library system will, at the very least, feature a functional infrastructure to store and maintain ontologies, an uncomplicated adapting environment for editing, searching and reasoning ontologies, and strong standardization support by providing upper-level ontologies and standard ontology representation languages. In a nutshell, In order to facilitate ontology re-use, a library system must support the following: (see Figure 4): ontology re-use by open storage, identification and versioning; ontology re-use by providing smooth access to existing ontologies and by providing advanced support in adapting ontologies to certain domain and task-specific circumstances (instead of requiring such ontologies to be developed from scratch); ontology re-use by fully employing the power of standardization. Providing access to upper-layer ontologies and standard representation languages is one of the keys to developing knowledge sharing and re-use to its full potential. Examples of Ontology library systems include: WebOnto 18 [31], Ontolingua 19 [9], [8], DAML Ontology library system 20, SHOE 21 [21], 18

15 to appear in Data and Knowledge Engineering, 2002, Ontology A Ontology B Ontology C Ontology N Ontology Library Systems Management -Storage - Identification -Versioning Adaptation - Searching - Editing - Reasoning Standardization -Language - Upper-level ontology Fig. 4 Main functionality of the libary systems. Ontology Server 22, IEEE Standard Upper Ontology 23 (IEEE), Sesame 24, OntoServer 25, and ONIONS 26. ONIONS is a methodology for ontology integration and was successfully implemented in several medical ontology library systems [39]. For detailed description and comparison of these systems, see: [7]. We also require tools that provide support in adapting and merging existing ontologies to make them suited to new tasks and domains. Operations for combining ontologies are: ontology inclusion, ontology restriction, and polymorphic refinement of Ontology. When one ontology is included in another, for instance, the composed ontology consists of the union of the two ontologies (their classes, relations, axioms). A tool environment in this area is Chimaera, which provides support for two important tasks: (1) merging multiple ontologies, and (2) diagnosing (and evolving) ontologies [30]. This tool was developed at Stanford University. Figure 5 illustrates Chimaera

16 to appear in Data and Knowledge Engineering, 2002, Fig. 5 Chimaera. The PROMPT 27 tool [33] is available as a plug-in for Protégé-2000 and was intended as an aid in ontology merging. PROMPT takes two ontologies as its input and guides the user through the steps of creating a single merged ontology as the output. First, PROMPT creates an initial list of matches based on class names. Then it guides the user through the following integration cycle: 1) The user triggers an operation by either selecting one of PROMPT s suggestions from the list or by using an ontology-editing environment to specify the desired operation directly; and 2) PROMPT performs the operation, automatically executes additional changes based on the type of the operation, generates a list of suggestions for the user based on the structure of the ontology for the arguments in the last operation, determines conflicts that the last operation introduced in the ontology and finds possible solutions for those conflicts. The tool can be used to merge various ontologies to create the general ontology (e.g., a product catalog ontology from standard-specific cxml, xcbl, etc. catalog formats). This general ontology is stored in the marketplace and serves as a mediator between different document 27

17 to appear in Data and Knowledge Engineering, 2002, formats. However, for the B2B document integration task, ontology mapping (i.e. creating bridges between different non-changeable ontologies) seems to be more important than ontology merging (i.e. creating a single ontology from several other ontologies). PROMPT provides no support in this task. 3.4 Ontologies as dynamic networks of meaning As ontologies become more popular and are also used in real-life situations, new problems arise. Two important issues currently facing the ontology research community are: Evolving ontologies: how to manage ontologies that change over time. Ontologies are not unstable definitions that never change. One of the reasons for this is that a shared conceptualization of a domain has to be reached through a process of social communication. Other reasons for modifying ontologies include changes in the domain and adaptations to different tasks. The evolution of ontologies causes operability problems, which hamper their effective re-use. Solutions are required to allow changes to ontologies without making current use invalid. Combining ontologies: how to relate and align separately developed ontologies to use them together. At present, more and more (web) data are annotated with standard terminologies and other semantic meta data. This provides a great number of freely accessible domainspecific ontologies. However, to form a real web of semantics - which will allow computers to combine and infer implicit knowledge - those separate ontologies should be linked and related to each other. Adaptation of existing ontologies, and composition of new ontologies from existing ontologies are important issues in this area. In the remainder of this chapter, we will focus in more closely on these two areas Ontology versioning The need to cope with changing data structures is not new in computer science. Much of the research in database technology, has focused on the topic of database schema evolution. However, while there are quite a few

18 to appear in Data and Knowledge Engineering, 2002, similarities, there are also many differences. For a detailed discussion, see: [32]. An important difference is the fact that the distinction between data and schema is not as clear as it is in databases. Ontologies themselves - and not just the data - are often used in applications, e.g. as controlled vocabularies, or navigation structures. In addition, ontologies are even more distributed by nature than are databases. Often with distributed databases, we have a clear picture of the locations where changes might have effect. With ontologies used on the Semantic Web, however, we have absolutely no clue as to which applications use the ontology. It is possible to synchronize or fine-tune of changes. Due to these differences, the traditional distinctions [40] between evolution (new schemas that are backward compatible) and versioning (multiple views of the data via different versions), and between reading and updating compatibility are not very relevant to ontology versioning. Changes to ontologies will occur and some are likely to cause incompatibilities. A versioning methodology for ontologies cannot, therefore guarantee prevention of any information loss. However, it should make the effects of changes explicit. Ontology versioning is the ability to manage ontology changes and their effects by creating and maintaining different variants of the ontology. The management of changes is thus the key issue in support for evolving ontologies [27]. The mechanisms and techniques to manage those changes to ontologies should aim at achieving maximal interoperability with existing data and applications. This means that it should retain as much information and knowledge as possible, without deriving incorrect information. This type of methodology should feature the following: an identification mechanism: for every use of a concept or a relation, a versioning framework should provide an unambiguous reference to the intended definition; a change specification mechanism: the relation of one version of a concept or relation to other versions of that construct should be made explicit, both by specifying the ontological relation (e.g. subclass of) and the intention of the change (e.g. replacement). transparent access: methods for rendering a valid interpretation to as much data as possible, i.e. automatically translating and relating the versions and data sources to the maximum possible extent.

19 to appear in Data and Knowledge Engineering, 2002, Altogether, we see that ontology versioning is even more complicated than database versioning. However, it is an absolutely necessary element of the Semantic Web. After all, if it is not clear which version of the meta-data describes which version of the data, the reasoning about the data can no longer be trusted Aligning and relating To achieve the full promise of the Semantic Web, meta-data and knowledge from different sources should be combined to reason about data and perform automated data processing. Combining independently developed ontologies is, therefore, a central aspect of the Semantic Web. This is, however, not an easy task. In fact, it requires considerable effort [42]. Ontologies may differ in many dimensions, ranging from terminological and ontology language differences to paradigm differences [26]. To combine ontologies, many of these differences must be resolved. There are several methods for combining ontologies. These are sometimes summarized under the term, ontology integration. [38]. We make the following distinction, a discussion based on the extent of integration of the original ontologies. merging: creating one new ontology from two or more ontologies. In this case, the new ontology will unify and replace the original ontologies. This often requires considerable adaptation and extension. aligning: bringing the ontologies into mutual agreement. Here, the ontologies are kept separate, but at least one of the original ontologies is adapted such that the conceptualization and the vocabulary match in overlapping parts of the ontologies. However, the ontologies might describe different parts of the domain in different levels of detail. relating: specifying how the concepts in the different ontologies are related in a logical sense. This means that the original ontologies have not changed, but that additional axioms describe the relationship between the concepts. Leaving the original ontologies unchanged often implies that only a part of the integration can be done, because major differences may require adaptation of the

20 to appear in Data and Knowledge Engineering, 2002, ontologies. Notice that merging normally involves aligning as a first step. There are several tools available for this task, based on different types of heuristics [26]. A few of these tools are described in the section on ontology tools. The choice for merging, aligning or relating is often driven by the type of application. Applications with centralized control have a greater tendency to further integration than do distributed applications. Therefore, the process of combining ontologies on the Semantic Web will probably rely heavily on techniques for relating ontologies. Proven methodologies and techniques for relating and adapting ontologies are thus necessary. 4 Application Areas Our discussion below deals with two promising application areas for the semantic web and ontology technology. The first one involves information access as a basis for effective and efficient knowledge management. The second one concerns optimizing business relationships based on intelligent e-business. 4.1 Knowledge Management Knowledge Management involves with acquiring, maintaining, and accessing knowledge of an organization. It aims to exploit an organization s intellectual assets for greater productivity, new value, and increased competitiveness. Due to globalization and the impact of the Internet, many organizations have dispersed increasingly in geographical terms and are structured around virtual teams. With the large number of on-line documents, several document management systems have entered the market. However these systems have severe weaknesses: Searching information: Existing keyword-based searches retrieve irrelevant information, which features a certain word in a different context, or may miss information where different words about the desired content are used. Extracting information: Human browsing and reading is currently required to extract relevant information from information sources. This is because, automatic agents lack all common sense knowledge

21 to appear in Data and Knowledge Engineering, 2002, required to extract such information from textual representations. Moreover, they fail to integrate information distributed over different sources. Maintaining weakly structured text sources: It is a difficult and time-consuming task when such sources become large. Keeping such collections consistent, correct, and up-to-date requires a mechanized representation of semantics and constraints that help to detect anomalies. Automatic document generation: Adaptive web-sites that enable a dynamic re-configuration according to user profiles or other relevant aspects would be very useful. The generation of semi-structured information presentations from semi-structured data requires a machine-accessible representation of the semantics of these information sources. Using ontologies, semantic annotations will allow structural and semantic definitions of documents and offer entirely new options: intelligent searches instead of keyword matching, query answering instead of information retrieval, document exchange between departments via ontology mappings, and definitions of views on documents. Ontobroker [11] applies artificial intelligence techniques to improve access to heterogeneous, scattered and semi structured information sources as presented in the World Wide Web or organization wide intranets. It relies on the use of ontologies to annotate web pages, formulate queries, and derive answers. Essentially, it seeks to define an ontology and use it to annotate/structure/wrap web documents. This enables users to work with Ontobroker s advanced query and inference services to consult the information they need. To achieve this goal, Ontobroker provides three interleaved languages and two tools. It provides a broker architecture with three core features: a query interface for formulating queries, an inference engine for deriving answers, and a webcrawler for collecting the required information from the Web. It provides a representation language for formulating ontologies. A subset of it is used to formulate queries, i.e. to define the query language.italso features an annotation language to enable knowledge providers to enrich web documents with ontological information. Ontobroker s strength lies in its close coupling of informal, semi-formal, and formal information

22 to appear in Data and Knowledge Engineering, 2002, and knowledge. This supports their maintenance and provides a service that can be used more generally for integrating knowledge-based reasoning with semi-formal represented documents. The concepts and techniques used by Ontobroker have been further developed in the Ontoknowledge project [37]. The Ontoknowledge architecture and all its major components are shown in Figure 6. To illustrate these components and their interactions, we present a simple querying scenario, where a user poses a query to the system that must be answered on the basis of a set of weakly structured data sources in a repository. The sequence of numbers in Figure 1 indicates the steps that must be taken in order to perform any of the above queries. Step 1. The system interacts with a user in order to elicit a specific query. Both the interaction with the user and the resulting query proceed entirely in terms of a domain-specific ontology, expressed in the OIL language, which is developed within the consortium (see Sections 2 and 3). The required ontologies are constructed using tools, such as OntoEdit developed by the University of Karlsruhe. O I L o n t o l o g y r e p o s i t o r y User Interface " RQL XQL % # QL engine XML < > </ > RDF! & User Annotated Data Repository pers0 tel 731 par0 abou $ $ Data Repository Τηισ τεξτ ισ αβουτ χαρσ εϖεν τηουγη ψου χαν τ ρεαδ ιτ car Fig. 6 The Ontoknowledge toolset for ontology-based knowledge management.

23 to appear in Data and Knowledge Engineering, 2002, The main advantage to this type of ontology-based user interaction is that it shields the user from any document-specific representation details, and can communicate in meaningful, domain-specific terms. Moreover, it makes the system much more robust against changes and variability in the formats of the underlying documents. Step 2. The user interaction results in a specific query to be answered by the data repository layer. We rely on the Resource Description Framework currently being developed by the World-Wide Web consortium (W3C) to structure the data repository and to express queries over this repository. The required translation from OIL-based user interaction to RDF-based queries is feasible because OIL itself is definable in terms of RDF-Schema definitions. Searching the full text of documents along with any associated RDF annotations can ensure high recall, which is desirable in the early stages of the retrieval process. To accommodate this information- seeking behaviour, RDFferret combines full text searching with RDF querying. RDFferret cooperates closely with BT s full text search engine, WebFerret. RDFferret can function like a conventional Internet search engine by entering and refining a set of search terms, or by entering a natural language query. Step 3. The consortium has developed an RDF Schema query engine to process queries efficiently over medium-sized data-repositories (containing up to a million RDF triples). Sesame is an RDF Schemabased Repository and Querying facility. It is being developed by Aidministrator. Sesame supports highly expressive querying of RDF data and schema information, using an OQL-style query language called RQL. This language is being developed by the ICS-FORTH institute in Greece. Sesame supports storage of large quantities of RDF and RDF Schema information. The RDF is parsed, using the SiRPAC parser, and stored in the Object-Relational DBMS PostgreSQL. Step 4. Clearly, the steps outlined above are all based on the assumption that the data repository is filled with data annotated with sufficiently rich semantic information. Furthermore, the annotations must be related to the ontological vocabulary that formed the basis for the original user query. Different technologies will be used to provide these annotations, depending on whether we are dealing with weakly structured data sources, or data sources that consist of free

24 to appear in Data and Knowledge Engineering, 2002, text only. In the first case, we would use wrapper technology. In the second case, the Corporum technology from CognIT [3] would be the main platform for concept extraction from free text. Other tools are based on the automated summarization technology that BT developed for ProSum [5]. Steps 5,6. Once the RDF query has been executed over the data repository, the resulting information is communicated to the user. Again, this must be done using an ontology-based vocabulary. Powerful graphical visualizations of query results in the context of large data sets are also developed. Examples of such visualizations are the semantic sitemaps produced by the WebMaster tool, AIdministrator. (See Figure 7 and [20]). On-To-Knowledge is conducting out three industrial case studies to evaluate the tool environment for ontology-based knowledge management and the associated web inference layer, OIL. These case studies were selected with a view to covering a broad spectrum consisting of three different industries (insurance, telecom, energy) in Fig. 7 Automatically generated semantic structure maps of a website.

25 to appear in Data and Knowledge Engineering, 2002, three countries, industries that face different knowledge management problems. In addition to the tool set and the OIL language, On-To-Knowledge is developing an associated methodology for ontology-based knowledge management. The input to this includes: (1) existing European research results, such as the CommonKADS approach to knowledge engineering and management [41]; (2) findings from experience with knowledgebased software engineering and tool development; (3) ontology composition and information retrieval techniques; (4) and feedback from the industrial case studies. In the meantime, new projects have emerged that have continued down this path of research. [23], for instance, applies semantic web technology to knowledge management solutions for technology-intensive areas. 4.2 E-commerce Today s e-business includes thousands of companies that offer countless products for electronic markets. Forecasts for the dollar value of Business-to-Business (B2B) e-commerce in US range between $634 billion and $3.9 trillion. Already, roughly 3 out of every 5 companies are using e-commerce to some extent. An additional 20 percent have indicated intentions to do so in the future. By 2003, some 80 percent of all business-to-business transactions could occur on-line [43]. Unlike traditional markets, electronic markets make it easy for participants to compare the offers and perform business negotiations faster. This is made possible by the electronic information interchange provided to market participants by B2B marketplaces. B2B marketplaces bring together up to tens of thousands of on-line suppliers and buyers, and mediate their negotiation and purchase transactions. Different studies estimate that up to 10,000 B2B marketplaces will appear in the very near future. However, a simplified view of the mission and required functionality of marketplaces has led to recent bankruptcies and general disappointment in B2B mediation as a whole. To be successful and to impact businesses, B2B marketplaces must be able to: Translate between various document representations used in B2B procurement.

26 to appear in Data and Knowledge Engineering, 2002, Mediate different product and service ontologies, which provide formal description and hierarchies of products and services. Mediate different e-business ontologies, which specify the business processes, the roles of market participants, and value transformation chains. Each of these tasks is vital to e-business, and could be performed by applying Semantic Web technologies B2B Document Transformation Most of the B2B participants encode their documents in XML. However, numerous XML serializations for conceptually equivalent documents have been developed so far. They differ in their document structures and information decomposition, complexity, and usage scenarios. Traditional and ad-hoc transformation technologies have proven insufficient for performing the necessary transformations in an efficient, scalable way. (For a relevant discussion, see: [35] and [36]). The solution may well lie in ontology technology, one of the most promising technologies for e- business [10]. Ontology-mediated information integration provides an elegant framework for document integration, bringing conceptual structure to the documents, and allowing further integration with knowledge-based document search and retrieval. B2B document transformation tasks are quite similar to those involved in building the Semantic Web. They include: Constructing RDF conceptual models from XML documents, which is known in the Semantic Web as ontology-based data annotation; Aligning different document ontologies; Performing ontology-based information querying and retrieval to store and align document ontologies; Performing complicated and knowledge-intensive information transformation, including generation of conceptually different views regarding the same knowledge. These Semantic Web technologies are needed to resolve the B2B document integration problems; the latter may provide the guidelines for Semantic Web development.