ATM [autonomous topic maps] :Overview
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Lutz Maicher (maicher@informatik.uni-leipzig.de)
Last update: 2007-04-12
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Autonomous
Topic Maps contain workflow definitions. The execution of these workflows
produces new topic maps according to the modelling method defined by the
workflows. Broadcasting your Autonomous Topic Maps scales the omnipresence
production of new topic maps according to the construction plans you have
designed and you want to establish. Take a minute and learn more about Autonomous
Topic Maps. ATMs in one minute
Let’s start with an example: Creating a small
topic map containing the metadata for a website using the Dublin Core
vocabulary seems to be a simple task. Once this task has been started,
confusion arises rapidly. Should the creator of the website be represented as
an occurrence or as an association? If the association option is chosen,
which terms should be used for representing the roles? Furthermore, what is
the meaning of the Dublin Core term “contributor”? And is the Dublin Core term “title” the same as the FOAF term “title”? Can I use
them synonymously? In fact, most of the ad hoc projects of
creating small topic maps containing the metadata for a website or any other
information resource will fail. And the small number of topic maps which will
be created will be hardly mergeable, because all authors applied different modelling methods derived from the best
practice they assume. Querying the merged topic maps using tolog will become
really awkward: How to get the creator of a website? Querying the associations
(using which roles) or querying the occurrences? It might be better to stop
at this point. A tool is needed to broadcast one modelling method. Firstly, if the
modelling method can be applied and executed easily, a lot of topic maps will
be created. And secondly, all of the created topic maps will be instances of
the same modelling method which ease their integration and their integrated
use, like querying, enormously. Autonomous Topic Maps (ATM) are this tool. An ATM represents a modelling method (which
is internally a Petri net) as topic map. These topic maps can be broadcasted
and the best practice for creating topic maps (or any other kind of models)
will be broadcasted with them. An ATM contains a workflow which can be
executed by ATM interpreters. Each workflow can be executed by various ATM
interpreters; each of them might be best suited for different usage contexts
(stand alone, website, mobile environments, integrated in office production
systems). For all operations which will be executed in the workflows, only a
set of minimal requirements is defined an the ATM
Interpreters can implement these operators appropriate for the intended usage
context. The users interact conveniently with the interpreters and the
results are mergeable topic maps according to the modelling methods defined
by the ATM. One reference implementation of an ATM
interpreter is fluidS,
which can be downloaded here. Please be aware that FluidS
is a technology showcase and other ATM interpreters can be indiscernibly
integrated in applications (i.e. by using The vision
We
foresee the future of topic mapping in the decentralised creation of very
small topic maps, each of them comprising only some facts. ATM should support
the fast and easy creation of these small topic maps. In a
second step these small topic maps must be accessible in an integrated fashion.
The quantity creates a new quality due to the unseen linkage density we will
achieve by having an integrated view on these topic maps. Two
possible scenarios are imaginable. The “Google“ scenario
foresees large topic maps repositories where the small topic maps will be
registered, indexed and stored. Crawlers might support the search for
published small topic maps. Getting information about a subject will mean
querying the centralised repositories. In the “Ant” scenario there will be a
lot of small topic maps repositories, talking via web services in a P2P
fashion. Getting information about a subject will mean querying the network.
In the query results, both approaches will yield the linkage density we are
looking for. Both
scenarios will lift off if the creation of (mergeable) topic maps will be an
easy, very convenient task, applicable in any kind of usage contexts. When we
reach the point o mass creation of small topics, we can start to speak about
seamless knowledge. Getting Started
Getting started with Autonomous Topic Maps is
straightforward. Download and install fluidS, the Autonomous Topic Maps Engine, load an ATM into
fluids and start the workflow. 1. Download the latest fluids distribution from
here (fluids.zip) [5 MB]. Save the file into your
program folder (i.e. “C:\programs”) and directly unzip the file into this
folder. A new subfolder with name “fluids” will be created. 2. Make sure that the Java 2 Software
Development Kit (SDK) version 1.5 or newer is available at your system. Your
system variable JAVA_HOME must be set to the java home directory. 3. Create a system variable FLUIDS_HOME and set
the value of this variable to the name of the new subfolder (i.e.
“C:\programs\fluidS”). 4. To start the program run: %FLUIDS_HOME%\bin\fluids.bat (i.e. “C:\programs\fluidS\bin\fluids.bat”). 5. The welcome dialog appears and you have
successfully installed fluids.
Note: fluids is
only tested in Windows XP environments. Now you can start working with ATMs and
fluids. In a first step it is recommended to open an ATM which is included in
the distribution: 1. Open the file %FLUIDS_HOME%\ATMs\ATM_PersonTopic by clicking at the button load new file.
The ATM will be opened and simultaneously indexed by the system. If the file
is not an ATM, this topic map will only be indexed for the topic map index.
2. Choose the file in the box “Files containing
workflows”. Than choose the workflow “Add Person” and click the button “run
workflow”.
3. You will be asked for a name of a person.
Type a string into the text field an click proceed.
You will be asked for a topic map where the result of the
workflow have to be stored in. Choose an existing topic map or type
the name of a new topic map in. Press OK and you will have the result in the
specified topic map. Have a look in this topic map. 4. Try the other ATMs delivered in the fluids
distribution. And if you recognise a bug or you have a comment, please start
the ATM_bug.xtm, store the result in a topic map
and send the topic map to maicher@informatik.uni-leipzig.de 5. Create you own ATMs by following the
tutorial and broadcast these ATMs and fluidS. The Topic Map Index
All topic
maps loaded to fluidS
will be added to the default topic maps index. This index can be used to
extract appropriate PSIs for Persons, Organisations or any other subjects.
Using already seen PSIs increases the mergeability
of the created topic maps and should be enforced by all ATMs. To have a
large PSI repository available, we recommend loading all available topic maps
into fluidS.
We have already included the bibMap (available here)
into the topic maps index. You have PSIs for most authors in the community
available. Test it: start “ATM_QueryIndex.xtm” and
type in “Steve”. Tutorial
The
tutorial guides you to create your first ATMs. Learn more here. Comments and Bugs
We want
to propagate the idea of Autonomous Topic Maps (and fluidS)
to populate the world with reasonable topic maps. For this reason we need
powerful software. We permanently improve fluidS,
but we require your input. We are
always interested in you comments and thoughts to fluidS and the Autonomous
Topic Maps. Please send an email to us: maicher@informatik.uni-leipzig.de If you
want to report a bug, please start the ATM_bug.xtm
in fluidS
and describe your bug. Send the created Topic Map to us via email. If the bug
occurs in the context of a executed ATM, please send
this ATM to us, too. Download existing ATMs
You always need the MWP Ontology definition: · MWP_Ontology.ltm Productive ATM: · DC4TM-ATM.xtm (the graphical
notation as pdf) ATMs from the tutorial: · ATM_Bug.ltm
(ATM_Bug.xtm incl. Ontology) · ATM_HalloWelt.ltm
(ATM_HalloWelt.xtm incl. Ontology) · ATM_WessenWelt.ltm (ATM_WessenWelt.xtm incl.
Ontology) · ATM_WessenWeltQ.ltm (ATM_WessenWeltQ.xtm incl. Ontology) · ATM_PersonTopic.ltm (ATM_PersonTopic.xtm incl. Ontology) · ATM_QueryIndex.ltm
(ATM_QueryIndex.xtm incl. Ontology) · ATM_TologQuery.ltm
(ATM_TologQuery.xtm incl. Ontology) Formal Specification
A draft of the formal specification of the
fundament of ATMs (Petri Net Data Model and Petri Net Process Model) is
available here. Overview about implemented
Operators
Namespace: http://www.semports.org/mwp à mwp
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Latest
modification: Thursday,
12 April 2007 |
