The Human Semantic Web

My thoughts on the work of Naeve http://kmr.nada.kth.se/wiki/Amb/HomePage and Enoksson on the Human Semantic Web and implementation of Concept Maps.

Enoksson (2006) explains the advantages for extensibility of an open standard language, he used RDF for Conceptual Browsing on the Semantic Web. Enoksson (2006) models things with concept maps that break an overall ontology down into concept sub-ontologies/taxonomies.

Naeve (2005) argue that “combining the human semantics of UML with the machine semantics of RDF enables more efficient and user-friendly forms of human-computer interaction.” Using UML for production of ontologies is as advocated by Baclawski et al. (2001) and Kogut et al. (2002), and Enoksson (2006). Naeve (2005) examines this strong separation between types (classes), and instances (objects) and considers this to be a weakness, which he rectifies for ULM (Unified Language Modeling) developed from UML.

Naeve (2005) gives an example of the need for “semantic mapping” between different words with the same meaning such as ‘author’ in one ontology and ‘creator’ in another ontology in order to establish interoperability and machine readability.

The Table below shows tools, technologies, and languages that can assist in this, and where they are based in a hierarchy from low level information centred interaction to high level user centred interaction (bottom to top), and computing focused to human focused representation (right to left). The Table also shows how each tool fits in with Naeve’s (2005) analysis based on “characteristics of the three different semantic stages” of “Semantic Isolation, Semantic Coexistence, and Semantic Collaboration” :-

Table - Language and Tool Mapping















Naeve (2005) describes Semantic Isolation where databases are available but hidden behind web portals, though the portals advertise their address. Semantic Coexistence is achieved by databases being structured in such a way that it is possible to search them without having to know their location. Naeve gives the example of RDF Schema - RDF(S), this standardises the structuring of the information across RDF(S) databases. RDF(S) provides standardised elements for the description of ontologies, so assisting to enable Semantic mapping. Semantic mapping enables Semantic Coexistence due to Semantic mapping enabling agreement on terms. For the table above the argument presented is that high level user centred interaction (bottom to top), and computing focused to human focused representation (right to left), enable Semantic Coexistence. The tools in the top left are built from those below and to the right of them so the Semantic Coexistence is built from Berners Lee’s (2000) Layered Architecture. Naeve (2005) argues the need for semantics that are understandable to humans as well as machines. That is an important objective of the research outlined in my thesis as without semantics that are understandable to humans, it is not possible for non programmer domain experts to undertake collaborative modelling. Naeve (2005) discusses a bottom up approach where there is a set process of deciding what can be agreed on, what cannot, and on documenting both.

Naeve (2005) argues that where knowledge is tacit it is vital to keep track of the individuals or groups who have this tacit knowledge, and that also the ‘Human Semantic Web’ can help elevate tacit knowledge to explicit.

References

Baclawski, K., Mieczyslaw, K., Kogut, P., Hart, L., Smith, J., Holmes, W., Letkowski, J., Aronson, M., 2001. Extending UML to Support Ontology Engineering for the Semantic Web. In: Proceedings of the 4th International Conference on The Unified Modeling Language, Modeling Languages, Concepts, and Tools, pp 342-360.

Berners-Lee, T., (2000) Semantic Web on XML – Slide 10
http://www.w3.org/2000/Talks/1206-xml2k-tbl/slide1-0.html

Enoksson, N. (2006) Serverside Solution for Conceptual Browsing on the Semantic Web. MSc. Dissertation, Stockholm University.

Kogut, P., Cranefield, S., Hart, L., Dutra, M., Baclawski, K., Kokar, M., Smith, J., 2002. UML for Ontology Development. The Knowledge Engineering Review Vol 17(1) pp 61-64.

Naeve, A., 2005, The Human Semantic Web – Shifting from Knowledge Push to Knowledge Pull. International Journal of Semantic Web and Information Systems (IJSWIS), Vol 1(3) (July-September 2005) pp 1-30.

PhD Objectives - User Driven Modelling

These are my PhD objectives, in order to make it clear what my research is about.

This research will examine creation of models and modelling systems, and how this can be eased so that a wider range of the population can achieve this. It will also identify ways that creation of models and modelling systems is similar to other types of programming, and how the research can be applied more generally. The main users of the system will be engineers; possibilities for wider application will be explored.

The purpose of user driven modelling/programming is to enable non programmers to crate and adjust models in order to maximise the maintenance, extensibility, ease of use, and sharing of information in models and modelling systems. The research will investigate the following areas in order to ease the process of model creation.

Maintenance
Maintenance of models and modelling systems will be improved by:-

* Use of open standards to represent information in a format available to the maximum range of maintainers without being dependant on the computer system or software they use.

* Ensuring the structure of the modelling/programming system and all its related information is visualised clearly.

* Minimising the amount of code necessary to create a model, and structuring the model so that all connections can be seen.

Extensibility
Extensibility will also be improved by the above means; this enables understanding of a model and so allows for easier re-use. A clear structure and visualisation can be edited with fewer worries about unintended consequences (side effects).

Ease of Use
* Maximising accessibility is important to this, use of open standards helps achieve this, together with enabling models to run on software and systems that people are widely familiar with. Ease of use should be facilitated without compromising structure.

Clear structuring and visualisation of information also assists in making a modelling system easier to use.

Sharing of Information
Maintenance, extensibility and ease of use are the key drivers for sharing of information. Achievement of the objectives in these areas would enable collaboration.

Methodology for Achievement of Objectives
Achievement of the above objectives can make possible creation of manageable, maintainable, and flexible models. To enable these objectives, a diagrammatic representation of models will be used as well as a taxonomy based visualisation. This will make it possible for engineers to use an interface that many of them are familiar with. Translation capabilities will be provided to enable better communication between computer systems, and between humans and computer systems. This will allow visualisation of chains of equations which are common in cost modelling. This visualisation will make it easier for people to add and manage information in large models, and identify cost information. A cost modelling example will be used throughout the thesis, but this work is relevant to modelling in general.

Information management and Interaction
This work will be based on information held in an Ontology, accessed using Semantic Web technology. Cost models will be constructed from information chosen by users through an interface that interacts with the user to establish what information is required, how it should be processed, what calculations should be made, and how should it be displayed, e.g. as a diagram or taxonomy.

More Information is available at http://www.cems.uwe.ac.uk/~phale/.