elektronická verzia publikácie - FIIT STU - Slovenská technická ...
elektronická verzia publikácie - FIIT STU - Slovenská technická ...
elektronická verzia publikácie - FIIT STU - Slovenská technická ...
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Semantic Web Services 191<br />
phases of web service composition. It is important to know that not each real problem can<br />
be described and solved using planning. Examples are environments where the future state<br />
can not be determined before it is reached. For instance, the n-body problem belongs into<br />
this category. It deals with the finding of motions of n bodies determined by Newton’s<br />
laws of motion and Newton’s law of gravity. The first contribution to the solution of n-<br />
body problem has been done by Poincaré in 1892 [40]. The first version of his contribution<br />
contained an error, described in [34]. The problem was finally solved by Karl Fritiof Sundman<br />
(for n =3) [42]. The result is that it is not possible to solve the n-body problem analytically.<br />
Only numerical methods can be used to find a solution with arbitrary precision (greater than<br />
zero). Consider a planning problem where we want to the fly to Mars and land on an exactly<br />
specified point. The existence of the n-body problem proves that there can not be found<br />
a plan which solves this problem.<br />
The subset of problems solvable by planning techniques is restricted. Consider all<br />
domains solvable using a common computer, more precisely by a Turing machine with<br />
final tape. These domains are also solvable using a STRIPS-like or HTN-like planners, whose<br />
expressiveness is equivalent [49]. Hence, the set of problems which can be solved by planning<br />
is restricted at least to this domain. Based on the concrete technique used, other restrictions<br />
can occur.<br />
Now we focus on more concrete problems we must deal with in Semantic web service<br />
composition. The first problem of the utilization of web service composition appears already<br />
at the first step. This is how to gather information about the initial situation and the goal state<br />
from the user. The web service composition methods already work with specific formalisms<br />
like OWL-S. Descriptions in these languages can not be directly obtained from the user.<br />
Different user-computer interactions can take place here.<br />
One example of a user interaction tool is described in [56, 57]. The gathering of the<br />
information is based on context detection. The context was detected based on user’s textual<br />
input of a note. A tool EMBET analyzes this input and finds relation between parts of this<br />
note and ontological concepts. After this a list of context suggestions are presented to a user<br />
who can select the relevant ones. Based on the context, those services which are closely<br />
related to it are suggested to be those producing the desired output for the user.<br />
Another approach to gathering information about the user’s goal is presented in [71].<br />
The described system is a smartphone interface to the Semantic web – SmartWeb. It constitutes<br />
from a PDA client, dialog server, and the Semantic web access system. In the PDA client<br />
a Java-based control unite takes care about the inputs and outputs. The dialog server includes<br />
modules for speech interpretation, natural language generation, and user context detection.<br />
The Semantic web access system contains modules for question-answering sub-system, semantic<br />
web service composer, semantic web pages, and knowledge server. The aim of the<br />
system is to make the Semantic web accessible for the user in convenient way. It assists the<br />
user during finding information taking into account its context and exploiting also Semantic<br />
web services. When web service composition is required, the system generates questions to<br />
acquire the necessary information for construction and execution of the composite service.<br />
It is not clear if the approach used in SmartWeb is sufficient to be used universally and<br />
how dependent it is on the domain. Very important role in this system plays the ontological<br />
knowledge base. Building a sufficiently large, consistent ontology which can be effectively<br />
processed by machines still presents a problem. Automatic approaches using for example