home > library > publications > pidgin english for topic maps knowledge engineering

close subject identifiers for Pidgin English for Topic Maps Knowledge Engineering
  • http://www.topicmapslab.de/publications/pidgin_english_for_topic_maps_knowledge_engineering

Pidgin English for Topic Maps Knowledge Engineering

Presentation, was published by Robert Barta and Lars Heuer at 2007-03-20

This presentation introduces a new generation of the AsTMa= notation.

External Link: download slides

This paper introduces a new generation of the AsTMa= notation which has evolved from earlier versions based on usage patterns from novices and casual users alike. These experiences have shaped a low-barrier language which is optimized for human-centric encoding of semantically rich Topic Maps content.

1.1 Pidgin English
One objective in the development of AsTMa= was to provide a textual language, which can mimick natural language to a certain extent and so enable non-technical users to express assertions about their knowledge domain. Accordingly, knowledge fragments can be organized in a themed, block-oriented way. That supports effective long-term management of highly irregular information as it encourages to move away from the necessity to manage a high number of microscopic statements. As the language builds on a cleaned-up variant of AsTMa 1.x, we present this first.

At this level, the new language is quite comparable to the future ISO standard 13250-6 “Compact Topic Maps Syntax (CTM)” 1. More innovative (at least in a Topic Maps context) is the use of “natural-language” features. The following block

Paul-McCartney isa person and has born-date “18 June 1942”.
Paul-McCartney plays piano and is-member-of The-Beatles,
which isa pop-group and which originated in liverpool.

leads into a topic map with the topic “Paul-McCartney” being registered as an instance of “person” with an occurrence of type “born-date” which carries a date value. Also “piano”, “The-Beatles”, “pop-group” and “liverpool” with the appropriate associations are added.Further features are the detection of data values, a templating infrastructure to reduce syntactic noise, a telex style to keep AsTMa= code on a single line and a consistent subject identification syntax for topics.

1.2 AsTMa?
In a further step the language is generalized by allowing variables as interrogative pronouns in certain places. This results in AsTMa?, a query language which assists non-technical users to retrieve information from topic maps.The query

$who isa person
is internally translated into the TMQL 2 expression
select $who where $who isa person

The AsTMa? processor answers such an query with a more verbose answer than a TMQL engine would. A result set consists of several topic map fragments encoded in AsTMa=. Example answer:
Paul-McCartney isa person
John-Lennon isa person
The query results can then be merged into the query statement the user provided. Such responses can then easily be used in a text-to-speech environment or as input for other topic maps.


Robert Barta

No contact information available. 


Robert is project leader of Perl XTM Engine (superseeded.. and Perl TM.

Lars Heuer

No contact information available. 


Presented at

Topic Maps 2007

Conference from {{start}} to {{end}}


The First International Topic Maps Users Conference took place at the Oslo Conference Centre in Norway on March 20-21 2007. Attendees experienced …

Visit homepage of Topic Maps 2007


Follow us on Twitter


As a former information scientist, I am fascinated since 1999 by the capabilities for building Topic Maps-based knowledge systems having the potential to augment human mind. One can model arbitrary knowledge organization systems, deal with semantic heterogeneity, collocate all facts about one subject in one logical place, and with TMQL have semantic retrieval on federated semantic networks. Therefore I expect bright prospects for business concepts building on the exchange of such knowledge snippets via semantic knowledge services.

Alexander Sigel
Topic Maps Lab auf der Cebit 2011

Graduate from the Topic Maps Lab