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Introduction:
The
Internet and the World Wide Web have brought a revolution to information
technology and the daily lives of most people. However, most of the
current forms of web content are designed and structured for use by
people but are barely understandable by computers. The goal of the
Semantic Web, with its vision by Berners-lee (1998),
is to develop expressive languages to describe information in forms
understandable by machines.
XML
(Extensible Markup Language, 1998) has brought great
features and promising prospects to the development of the Semantic Web.
Currently, there are numerous techniques and tools available for XML,
e.g., SAX (Simple API for XML), DOM (Document
Object Model, 1998), XSL (Extensible Stylesheet
Language), XSLT (XSL Transformation), XPath, XLink, and XPointer, and XML parsers are available in different
languages and for different platforms. Using XML, one can describe
document types for various domains and purposes. For example, XML
documents may represent multi-media presentations (Hoschka,
1998), and business transactions (XML/EDI-Group).
Applications can access XML documents via standard interfaces like SAX and DOM. A number of XML query
languages have been proposed, including XML-QL (Deutsch,
et al., 1998), X-QL (Robie, 1999), Lorel (Goldman, et al., 1999), and XQuery (Chamberlin,
et al., 2001). Furthermore, some researchers propose that these query
languages should be extended with an update capability so that an XML
document repository becomes an XML database (Tatarinov,
et al., 2001).
XML will
continue to play an important role in the development of the Semantic
Web. However, it does not provide a full solution to the requirements of
the Semantic Web. XML can represent only some semantic properties through
its syntactic structure, i.e., by the nesting or sequentially ordering
relationship among elements (XML tags). XML queries need to be aware of
this syntactic structure via the document type that is defined by a DTD
(Document Type Definition). Although one might derive some sort of
semantics from the structure of the documents within the context of the
document type, the semantics of each element (XML tag) is not defined and
its interpretation totally relies on the implicit knowledge hardcoded in
application programs. To develop a Web with semantics, resources on the
Web need to be represented in or annotated with structured
machine-understandable descriptions of their contents and relationships,
using vocabularies and constructs that have been explicitly and formally
defined with a domain ontology.
The most
acceptable definition of ontology seems to be the following one by Gruber
(1993): an ontology is a "formal specification
of a conceptualization", and is shared within a specific domain.
The world view that an ontology embodies is usually conceived as a
hierarchical description of a set of concepts (is-a hierarchy), a set of
properties and their relationships, and a set of inference rules.
Berners-lee (1998) outlined the architecture of the
Semantic Web in the following three layers:
- The metadata layer. The data model at this layer contains
just the concepts of resource and properties.
Currently, the RDF (Resource Description Framework) (Lassila
& Swick, 1999) is believed to be the most popular data model
for the metadata layer.
- The schema layer. Web ontology languages are introduced
at this layer to define a hierarchical description of concepts (is-a
hierarchy) and properties. Currently, RDFS (RDF Schema) (Brickley & Guha, 2002) is considered as a
candidate schema layer language.
- The logical layer. More powerful web ontology languages
are introduced at this layer. These languages provide a richer set
of modeling primitives that can be mapped to the well-known
expressive Description Logics (1999). Currently,
OIL (Ontology Inference Layer, 2000) and DAML-OIL
(Darpa Agent Markup Language-Ontology Inference
Layer, 2001) are two popular logical layer languages.
With the
creation and development of the Semantic Web, various web resources will
be able to be accessed by machines in a semantic fashion. The questions
are, what opportunities will this new technology bring to us and what
challenges and work are we facing now to get us from today's Web to the
Web of tomorrow - the Semantic Web? In this paper, we share our
understanding of the answers to these questions and we hope this will
shed some light on future research in this area.
Opportunities
Like other
technologies, the interest in creating and developing the Semantic Web is
motivated by the opportunities it might bring: either it can solve new
problems, or it can solve old problem in a better way. Here, instead of
enumerating all the opportunities enabled by the Semantic Web, we focus
our discussion on the following closely related aspects: web-services,
agent-based distributed computing, semantics-based web search engines,
and semantics-based digital libraries.
Web-services
Among the
most important web resources on the Semantic Web are those so called web-services.
Here, web-services refers to "web sites that do not merely provide
static information but allow one to effect some action or change in the
world". The Semantic Web will enable users to locate, select,
employ, compose, and monitor web-services automatically (Ankolekar,
et al., 2001).
- Automatic web-service discovery. Since semantic descriptions of
web-services are registered with some public registries, intelligent
mobile agents might migrate from one service registry to another to
find desirable web-services as specified by a user. There has been
some work in agent-assisted browsing of the web. For example,
Letizia (Lieberman) is a user interface agent
that assists a user browsing the World Wide Web based on the user's
profile. Letizia can learn the profile of a user based on the user's
browsing history. This idea can be applied to web-service discovery
as well. For example, a server might provide web-services at
different levels or with different values towards different users,
and an agent might find a web-service based on the user's profile.
- Automatic web-service invocation. Currently most web-services require
human's intervention during their execution. For example, to buy a
book at http://www.amazon.com/,
the website requires a user to fill out a form, and then click a
button to execute the service. Usually, multiple interactions
between a user and a web-service are needed to complete the
execution of that web-service . The capability of automatic
web-service invocation implies that a software agent will be able to
perform these invocations on behalf of a user, who only needs to
tell the agent to "go to Amazon and buy a book titled The
Semantic Web with no more than $50", and the agent will
interact with the web-service with appropriate input data via
computer interpretable APIs.
- Automatic web-service composition and
interoperation. Given a
library of web-services and an objective, one might be able to
automatically select and compose a new web-service to achieve the
new objective. The prerequisites and consequences of individual
web-services need to be described in a formal way, and the
technology for automatic workflow generation (Lu, et
al., 2002) might be used to generate web-services automatically.
- Automatic web-service execution
monitoring. For
long-running complex web-services, it is desirable to be able to
track and query the status of the execution of each service as well
as its components.
The
industry has already seen the potential market enabled by web-services
and some efforts have been put to the development of standards for
electronic commerce, in particular for the description of web-services.
For example, Microsoft, IBM and Ariba proposed UDDI (Universal
Description, Discovery, and Integration, 2000) to describe a standard
for an online registry, and the publishing and dynamic discovery of
web-services offered by businesses; Microsoft and IBM proposed WSDL (Web Service Definition Language) as an XML language to
describe interfaces to web-services registered with a UDDI database; the
DAML Services Coalition proposed DAML-S (Darpa Agent
Markup Language - Service, 2001) as an ontology to describe
web-services; and OASIS and the United Nations developed ebXML (Electronic Business XML Initiative, 2000) to describe
business interactions from a workflow perspective. A number of communication
protocols have been developed for the invocation of web-services: Remote
Procedure Call (Birrell & Nelson, 1984) is
client/server infrastructure that allows a client component of an
application to employ a function call to access a server on a remote
system. The CGI (Common Gateway Interface) mechanism
is a standard for external gateway programs to interface with information
servers such as HTTP servers. CORBA (Common Object Request
Broker Architecture) uses a registry to store interfaces of
distributed objects so that a client can invoke a method of a remote
server object without knowing its location, programming language,
operating system and other system aspects that are not part of the
object's interface. SOAP (Simple Object Access Protocol)
is a protocol for the exchange of information in a distributed
environment, in particular, the Web. Using SOAP, one can describe the
content of a message and the way to process it, define
application-dependent data types, and represent remote procedure calls
and responses. SOAP can potentially be used together with a variety of
other protocols. Currently, its binding with HTTP is supported. Java RMI
(Java Remote Method Invocation) enables
programmers to develop Java applications in which a client can invoke a
method of a remote Java Object. The ACL (Agent Communication Language)
developed by SRI International in the framework of OAA (Open Agent
Architecture) allows one to define and publish the capabilities of agents
so that a request can be matched with a server agent by the facilitators
(brokers) (Martin, et al., 1999)
Agent-based distributed computing paradigm
The
Semantic Web will use ontologies to describe various web resources,
hence, knowledge on the Web will be represented in a structured, logical,
and semantic way. This will change the way that agents navigate, harvest
and utilize information on the Web (Payne, et
al., 2002). On one hand, the Semantic Web is a web of distributed
knowledge bases, and agents can read and reason about
published knowledge with the guidance of ontologies. On the other hand,
the Semantic Web is a collection of web-services described by ontologies
like DAML-S (Darpa Agent Markup Language - Services) (Ankolekar
et al., 2001) and this will facilitate dynamic matchmaking among
heterogeneous agents: service provider agents can advertise their
capabilities to middle agents; middle agents store these advertisements;
a service requester agent can ask a middle agent whether it knows of some
provider agents with desired capabilities; and the middle agent matches
the request against the stored advertisements and returns the result, a
subset of the stored advertisements (Sycara, et al.,
2002).
When
agents are equipped with intelligence and mobility, the conventional
client/server computing paradigm might be replaced by an agent-based
distributed computing paradigm, in which agents can migrate from one site
to another, carrying their codes, data, running states (including
internal beliefs), and intelligence (specified by the users), and fulfill
their missions autonomously and intelligently. Many researchers have
speculated that mobile agents are inevitable for an open and distributed
environment like the Semantic Web and have seen the advantages of this
new computing paradigm (Lange & Oshima, 1999; Harrison, 1995) including:
- It reduces the network load.
- It overcomes the network latency.
- It supports the encapsulation of
protocols.
- Agents execute asynchronously and autonomously.
- Agents adapt dynamically.
- Agents are naturally heterogeneous.
- Agents are robust and fault-tolerant.
A number
of mobile agent systems have been developed (Mauldin,
1991; Luke, et al., 1997).
Semantics-based web search engines
Search
engines are among the most useful resources on the Web and currently
there are two types of search engines:
- Large-scale robot-based search engines. These systems rely on robots to retrieve Web
pages and store them in a centralized database. The advantage of
this mechanism is that it increases recall (the proportion of
relevant documents that are actually retrieved, Mauldin,
1991) since robots can almost retrieve all web pages on the Web,
while the disadvantage is the precision (the proportion of
retrieved documents that are actually relevant) of the search result
might be low.
- Small-scale reviewer-based search
engines. A category
hierarchy is created and each category is described by a set of
keywords. Reviewers will review each web page (submitted by web page
authors) and associate it with appropriate categories. The advantage
is that precision is increased, but the disadvantage is that recall
might be low since it is impossible to review and include every
single relevant web page on the Web.
Both types
of search engines are based on keywords, and hence are subject to the two
well-known linguistic phenomena that strongly degrade a query's precision
and recall: polysemy (one word might have several meanings) and synonymy
(several terms, i.e. words or phrases, might designate the same concept).
A number of stemming algorithms (Lennon, et al., 1981)
have been developed to address the synonymy issue including suffice
removal, strict truncation of character strings, word segmentation,
letter bigrams and linguistic morphology. The idea is that different
derivations of a word are similar to each other in their forms (e.g. they
have the same prefix) and can be traced back to the same root (stem)
using these stemming methods. However, these methods are subject to the
following stemming errors: words with different meaning might be reduced
to the same root. For example, words general, generous, generation,
and generic might be reduced to the same root. On the other hand,
different words with the same meaning cannot be reduced to the same root.
For example, automobile and car. The situation becomes
worse for the large-scale robot-based search engines. Only limited
semantics can be derived from the lexical or syntactic
content of the web pages.
Several
systems have been built to overcome these problems based on the idea of
annotating Web pages with special HTML tags to represent semantics,
including SHOE (Simple HTML Ontology Extensions) system (Luke, et al., 1997), GDA system (Ttiyama & Hasida, 1997). However the
limitation of these systems is that they can only process web pages that
are annotated with these HTML tags, and so far there is no agreement upon
a universally acceptable set of HTML tags.
XML is a
promising technique since it keeps content, structure, and representation
apart and is a much more adequate means for knowledge representation.
However, XML can represent only some semantic properties through its
syntactic structure. XML queries need to be aware of this syntactic
structure. With the advent of the Semantic Web, resources on the Web will
be represented semantically in ontologies. Semantics-based web search
engines can be built in which each query is executed within the context
of some ontology. The guidance from ontologies will increase recall and
precision of the search result. For example, one might pose a query
"return all the reviewers for book 'The Semantic Web: an
Introduction'" to a semantics-based web search engine, then the
engine will return only reviewers for this book instead of returning web
pages that contain keyword "reviewer" and/or term "The Semantic
Web: an Introduction". For another example, if one pose query "return
all the chairs", with the guidance of a furniture ontology, only
those furniture chairs are returned; and with the guidance of a person
ontology, only people who are chairs of some organizations will be
returned. In contrast, Keyword-based search engines will return web sites
that contain keyword "chairs", including chairs that refer to
furniture and chairs that refer to people. It is worth mentioning that
some systems that use ontologies to enhance web search engines have been
developed (Barros, et al., 1998; Erdmann, et al., 2001). Since ontologies are built
on a domain basis, web search engines might be also built on a domain
basis, and hence metasearch engines, which interface with multiple remote
search engines and select and rank remote search engines intelligently,
might be very useful (Dreilinger & Howe, 1997).
Semantics-based digital libraries
Digital
multimedia data in various formats has increased tremendously in recent
years on the Internet. With the development of digital photography, more
and more people are able to store their personal photographs on their
PCs. Sharing of picture albums and home videos on the Internet becomes
more and more popular. Furthermore, many organizations have large image
and video collections in digital format available for online access. Film
producers want to advertise movies through interactive preview clips.
Travel agencies are interested in digital achieves of holiday resorts
photographs. Hospitals would like to build medical image databases. These
emerging applications for multimedia digital libraries require
interdisciplinary research in the areas of image processing, computer
vision, information retrieval and database management.
Semantics-based retrieval of multimedia digital content is important for
efficient use of the multimedia data repositories. Traditional
content-based multimedia retrieval techniques describe images/videos
based on low level features (such as color, texture, and shape) and
support retrieval based on these features (Smeulders,
et al., 2000). However, human typically does not view images/videos
in terms of low-level features. A semantics-based query capability is
highly desirable. For example, one might want to formulate a query like
"return all the scenes in clip 1 in which a boy is riding a
bicycles". Retrieving images/videos based on low-level features
cannot provide satisfactory results (Vaiulaya, et
al., 2001). Effective and precise multimedia retrieval by semantics
remains an open and challenging problem (Vaiulaya,
et al., 2001; Naphade, et al., 2001].
Recently,
ontologies begin to be used in the context of digital libraries. For
example, ScholOnto (Shum 2000) is an
ontology-based digital library that supports scholarly interpretation and
discourse, and ARION (Corcho, 2000), another
ontology-based digital library that supports search and navigation of
geospatial data sets and environmental applications.
We believe
that various digital libraries will become another major web resource of
the Semantic Web. The challenges here are: (1) The development of
efficient and effective classification and indexing mechanism for each
type of digital library, and (2) The semantic interoperability between
digital libraries of similar types and between a digital library and the
Semantic Web.
Challenges
New
opportunities impose new challenges. In the following, we focus our
discussion on the following challenges that we are facing now: the
development of ontologies, and the development of the formal semantics of
Semantic Web languages, and the development of trust and proof models.
The development of ontologies
It is well
recognized within the Semantic Web community that ontologies will play an
essential role in the development of the Semantic Web. Various effort has
been devoted to the research of different aspects of ontologies,
including ontology representation languages (Corcho,
2000), ontology development (Jones, et al, 1998),
ontology learning approaches (Maedche &
Staab, 2001), and ontology library systems (Ding
& Fensel, 2001), which manage, adapt, and standardize ontologies.
Management.
The main purpose of
ontologies is to enable knowledge sharing and re-use, hence a typical
ontology library system supports open storage and organization, identification
and versioning. Open storage and organization address how ontologies are
stored and organized in a library system to facilitate access and
management of ontologies. Identification associates each ontology with a
unique identifier. Versioning is an important feature since ontologies
evolve over time and a versioning mechanism can ensure the consistency of
different versions of ontologies.
Adaption. Since ontologies evolve over time, how to
extend and update existing ontologies is an important issue. This
includes the searching, editing and reasoning of ontologies in an
ontology library system.
Standardization. Integration and interoperability is always
the concern of any open system. This is especially the concern of the
Semantic Web, an open system that has to be scalable at the Internet
level. Currently, a number of ontology representation languages have been
proposed (Corcho, et al., 2000) and various
ontology library systems have been built (Ding
& Fensel, 2001). The question is what would be the standardized
ontology representation language. Each of them seems to have its
advantages and disadvantages, and has its proponents and opponents. This might
be a feature of our human being society: each of us has his/her
preference. Since the Semantic Web is still at its early stage, it might
be too early to enforce any standardization. Each representation language
can grow on its own and the one or a few ones who win will become the de
facto standards. XML might serve as the meta-languages of these
representations to facilitate future interoperation and integration.
Formal semantics of the Semantic Web languages
The
functional architecture of the Semantic Web has three layers: the
metadata layer, the schema layer and the logical layer. Currently, the
RDF (Resource Description Framework, 1999) is believed
to be the most popular data model for the metadata layer. Although it is
believed that the RDF data model is enough for defining and using
metadata, the semantics of reification (statement about statement) is yet
to be defined. RDFS (RDF Schema) extends RDF and is
currently a popular schema layer language. It has been recognized that
RDFS lacks a formal semantics and one proposal is to define a
metamodeling architecture for RDFS similar to the one for UML (Universal
Modeling Language), and hence defines a formal semantics (Pan & Horrocks, 2001). This approach,
although formal, is complicated and not intuitive. Although RDFS has been
blamed for its semantic confusion and some apparent paradox, it has not
been shown that a formal semantics is impossible. To smooth the way of
developing the Semantic Web, we believe that the semantics of RDFS need
to be resolved: either a formal semantics is defined for it, or the
problem of RDFS is pinned down so that the semantic issue can be
resolved.
Proof and trust
As an open
and distributed system, the Semantic Web bears the spirit that
"anybody can say anything on anybody". People all over the
world might assert some statements which can possibly conflict. Hence,
one needs to make sure that the original source does make a particular
statement (proof) and that source is trustworthy (trust).
- Proof. Digital signatures will play an important role in proof.
The source has to sign the statement he makes so that agents can
check if information really comes from the source it claims to be.
In addition, other security technologies like encryption and access
control can be used to ensure confidentiality of information.
- Trust. Everybody should be able to define a trust model for
himself, i.e., one can define how much trust he would put on each
source on the Semantic Web. Since it is unrealistic to define the
extent of trust for each source, a mechanism is necessary to derive
the degree of trust for each new source. One solution is the notion
of "web of trust": when one trusts a source A, he also
trusts all other sources that are trusted by source A, but to a
lower extent, in this way, a huge and hierarchical network is
created which facilitates agents infer information based on their
trusted knowledge.
Currently,
the notion of proof and trust have yet to be formalized, and a theory
that integrates them into inference engines of the Semantic Web is yet to
be developed. However, these technologies are very important and are the
foundation of building real commercial applications (e.g., B2B and B2C
systems).
Conclusion
In this
paper, we reported a survey of recent research on the Semantic Web. In
particular, we presented the opportunities that this new revolution will
bring to us, and the challenges that we are facing during the development
of the Semantic Web. We hope that this paper will shed some light on the
direction of future work.
The
Semantic Web is still a vision. We believe that the Web will grow towards
this vision in a way like the development of the real world: Semantic Web
communities will appear and grow first, and then the interaction and
interoperation among different communities will finally interweave them
into the Semantic Web.
Acknowledgements
We thank
Dr. Terry Brooks and the two anonymous reviewers for their helpful
suggestions and comments.
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