Ontologies and the Semantic Web

1 Introduction

The word "ontology" seems to generate a lot of controversy in discussions about AI. It has a long history in philosophy, in which it refers to the subject of existence. It is also often confused with epistemology, which is about knowledge and knowing. Tom Gruber has given out his definition: An ontology is a specification of a conceptualization. We applied it to the context of knowledge sharing. And we use the term ontology to mean a specification of a conceptualization. That is, an ontology is a description (like a formal specification of a program) of the concepts and relationships that can exist for an agent or a community of agents.

As for metadata which is defined by “data about data”, it was used to describe data. In this sense, metadata is a low level ontology, and ontology extends metadata efforts. In this note we mainly introduce the semantic web and the ontologies, RDF, OWL, Topic Maps, ODM.

2 Semantic Web

The Semantic Web was thought up by Tim Berners-Lee, inventor of the WWW, URIs, HTTP, and HTML. The Semantic Web is a vision for the future of the Web in which information is given explicit meaning, making it easier for machines to automatically process and integrate information available on the Web.

There is a dedicated team of people at the World Wide Web consortium (W3C) working to improve, extend and standardize the system, and many languages, publications, tools and so on have already been developed. From the point of view of W3C, the Semantic Web will build on XML's ability to define customized tagging schemes and RDF's flexible approach to representing data. The first level above RDF required for the Semantic Web is an ontology language what can formally describe the meaning of terminology used in Web documents. If machines are expected to perform useful reasoning tasks on these documents, the language must go beyond the basic semantics of RDF Schema.

3 RDF

The Resource Description Framework (RDF) is a W3C standard for describing Web resources, such as the title, author, and modification date of a Web page, copyright and licensing information about a Web document, or the availability schedule for some shared resource. RDF is intended for situations in which this information needs to be processed by applications, rather than being only displayed to people. RDF provides a common framework for expressing this information so it can be exchanged between applications without loss of meaning.

It is based on the idea of identifying things using Web identifiers (called Uniform Resource Identifiers, or URIs), and describing resources in terms of simple properties and property values. This enables RDF to represent simple statements about resources as a graph of nodes and arcs representing the resources, and their properties and values. To make this discussion somewhat more concrete as soon as possible, the group of statements "there is a Person identified by http://www.w3.org/People/EM/contact#me, whose name is Eric Miller, whose email address is em@w3.org, and whose title is Dr." could be represented as the RDF graph as the following figure.

Figure 1: An RDF Graph Describing Eric Miller

The figure illustrates that RDF uses URIs to identify: (1) individuals, e.g., Eric Miller, identified by http://www.w3.org/People/EM/contact#me, (2) kinds of things, e.g., Person, identified by http://www.w3.org/2000/10/swap/pim/contact#Person, (3) properties of those things, e.g., mailbox, identified by http://www.w3.org/2000/10/swap/pim/contact#mailbox, and (4) values of those properties, e.g. mailto:em@w3.org as the value of the mailbox property.

RDF also provides an XML-based syntax (called RDF/XML) for recording and exchanging these graphs. The following example is a small chunk of RDF in RDF/XML corresponding to the graph in above figure,

Example 1: RDF/XML Describing Eric Miller

Note that this RDF/XML also contains URIs, as well as properties like mailbox and fullName (in an abbreviated form), and their respective values em@w3.org, and Eric Miller.

Like HTML, this RDF/XML is machine processable and, using URIs, can link pieces of information across the Web. However, unlike conventional hypertext, RDF URIs can refer to any identifiable thing, including things that may not be directly retrievable on the Web (such as the person Eric Miller). The result is that in addition to describing such things as Web pages, RDF can also describe cars, businesses, people, news events, etc. In addition, RDF properties themselves have URIs, to precisely identify the relationships that exist between the linked items. See http://www.w3.org/TR for links to more detailed introductions to RDF, and more advanced topics such as statement containers, reification, and schemas.

4 OWL

In short, OWL is a set of XML elements and attributes, with standardized meaning, that are used to define terms and their relationships. Owl superseded OIL and DAML. OIL and DAML started as research projects, and eventually merged into DAML+OIL. This was then taken up by the W3C, and is now being standardized as OWL, which builds on the older, and more basic, RDF Schema.

OWL provides three increasingly expressive sublanguages designed for use by specific communities of implementers and users.

• OWL Lite supports those users primarily needing a classification hierarchy and simple constraints. For example, while it supports cardinality constraints, it only permits cardinality values of 0 or 1. It should be simpler to provide tool support for OWL Lite than its more expressive relatives, and OWL Lite provides a quick migration path for thesauri and other taxonomies. Owl Lite also has a lower formal complexity than OWL DL.
• OWL DL supports those users who want the maximum expressiveness while retaining computational completeness (all conclusions are guaranteed to be computable) and decidability (all computations will finish in finite time). OWL DL includes all OWL language constructs, but they can be used only under certain restrictions (for example, while a class may be a subclass of many classes, a class cannot be an instance of another class). OWL DL is so named due to its correspondence with description logics, a field of research that has studied the logics that form the formal foundation of OWL.
• OWL Full is meant for users who want maximum expressiveness and the syntactic freedom of RDF with no computational guarantees. For example, in OWL Full a class can be treated simultaneously as a collection of individuals and as an individual in its own right. OWL Full allows an ontology to augment the meaning of the pre-defined (RDF or OWL) vocabulary. It is unlikely that any reasoning software will be able to support complete reasoning for every feature of OWL Full.

5 Topic Maps

The topic maps paradigm describes a way in which complex relationships between abstract concepts and real-world resources can be described and interchanged using a standard XML syntax. It was originally developed in the late 1990’s as a way to represent back-of-the-book index structures so that multiple indexes from different sources could be merged. However, the developers quickly realized that with a little additional generalization, they could create a meta-model with potentially far wider application. In January 2000, the International Organization for Standardization ISO published the standard ISO/IEC 13250 Topic Maps. The first edition of ISO 13250 included an interchange syntax based on SGML and the hypermedia linking language known as HyTime. The second edition added an interchange syntax based on XML and XLink. Today there are a number of implementations of the standard, both open-source and proprietary, for a number of languages and platforms including the .NET platform.

The core of topic maps can be summarized very succinctly: a topic map consists of a collection of topics, each of which represents some concept. Topics are related to each other by associations, which are typed n-ary combinations of topics. A topic may also be related to any number of resources by its occurrences.

The following figure shows the three fundamentals of topic maps. It also shows how the distinction between topic-to-topic and topic-to-resource relationships enables a partitioning of the model into a topic space that contains only topics and associations between topics and a resource space that contains the resources related to topics. This partitioning is interesting because it allows a topic map developed for one set of resources to be repurposed to index a different set of resources. In this way the topic map can be considered to be a portable form of knowledge.

Figure 2 Topics, associations, and occurrences

Topic maps have the many features, which are described as follows.

1. User Benefits. The topic maps model and the Topic Maps standard do provide a number of benefits that can be surfaced in applications and can be unique selling points.
2. Simple Organizational Metaphor. The core topic maps metaphor of topics, occurrences, and associations strikes a balance between being compact and easy to understand and providing enough basic infrastructure to allow users to translate their mental model of a domain into a topic map model. Other forms of data and information organization such as RDF and the relational model may have a simpler model still, but then require the user to create infrastructure for common procedures such as labeling an item with some names; defining a class structure or creating n-ary relationships between items.
3. Domain/Resource Separation. The topic maps model has a clear distinction between the domain model, expressed as topics and associations between topics, and the indexed resources, expressed as occurrences that link topics to resources. Three major benefits can be derived from this structure: (a) The topic map can act as a high-level overview of the domain knowledge contained in a set of resources; (b) A topic map can be easily partitioned depending on the resources to be made available; and (c) Topic maps that index different resource sets can be easily combined.
4. No Fixed Ontology. The Topic Maps standard does not come with a predefined ontology. There is no restriction on the domains to which topic maps can be applied and relatively few constraints even on the modeling approach taken.
5. XML Interchange Syntax. For many users, the fact that topic maps can be interchanged using a standard XML-based syntax provides a strong benefit in improving the portability of their data between applications and platforms. In addition, the XML interchange syntax allows easy integration of topic map information exchange within the Web services architecture.
6. Developer Benefits. There are three principal benefits that system architects and developers can gain from the application of the topic map paradigm, and they can be summed up as “Flexibility, Flexibility, and Flexibility.”
7. Flexible Schema. Topic maps provide the meta-model on which a completely flexible application model can be built. Creating new types of business objects can be achieved by adding data to the ontology that constructs the topic map. Because the ontology is itself expressed as topics and associations between topics, extension of the ontology becomes an issue of adding data, not an issue of redesigning the underlying storage schema.
8. Flexible Application Structure. With the application data stored in a standardized and extensible meta-model, the path is open to enable much simpler third-party application integration and extension.
9. Flexible Extension. In addition to allowing third-party extensions to the application schema, the flexibility of the topic map structure can be used to allow users to create his or her own extensions. This has two effects:

It enables applications to be highly customizable and development of horizontal applications that can be integrated more easily into existing environments.

6 ODM

Ontology PSIG is an ontology group of the OMG and its mission is to enable the OMG to rapidly develop ontology- related technology. It developed Ontology Definition Metamodel (ODM). ODM include six metamodels (five that are normative, and one that is informative). These are grouped logically together according to the nature of the representation formalism that each represents: formal first order and description logics, structural and subsumption / descriptive representations, and traditional conceptual or object-oriented software modeling.

At the core are two metamodels that represent formal logic languages: DL (Description Logics, which, although it is nonnormative, is included as informative for those unfamiliar with description logics) and SCL (Simple Common Logic), a declarative first-order predicate language. While the heritage of these languages is distinct, together they cover a broad range of representations that lie on a continuum ranging from higher order, modal, probabilistic and intentional representations to very simple taxonomic expression.

There are three metamodels that represent more structural or descriptive representations that are somewhat less expressive in nature than SCL and some DLs. These include metamodels of the abstract syntax for RDFS (RDF Schema), OWL, and TM (Topic Maps). RDFS, OWL and TM are commonly used in the semantic web community for describing vocabularies, ontologies and topics, respectively.

Two additional metamodels considered essential to the ODM represent more traditional, software engineering approaches to conceptual modeling: UML2 and ER (Entity Relationship) diagramming. UML and ER methodologies are the two most widely used modeling languages in software engineering today, particularly for conceptual or logical modeling. Interoperability with and use of intellectual capital developed in these languages as a basis for ontology development and further refinement is a key goal of the ODM. Since UML2 is an adopted OMG standard, we simply reference it in the ODM.

The following figure shows the organization of the metamodels, with the current and intended mapping components indicated, with RDFS and OWL grouped, as shown, for mapping purposes.

Figure 3 ODM Metamodels: Structure and Mappings