Visual Contextualisation of Digital Content

1. Visual Contextualisation of Digital Content Introduction VICODI (www.vicodi.org) is a collaborative RTD project carried out by seven partners from six European countries under the 5FP IST programme of the European Union. The main aim of this project is to develop a novel visual contextualisation environment for digital content on the Internet. The VICODI system will demonstrate the benefits of semantics by improving searching and navigation in historical databases. The development of a visualisation and contextualisation environment for digital content addresses the management of searching and retrieval as well the management of information presentation in two ways. First by creating an open knowledge space that can be enhanced by its users and, second, by providing an innovative interface that employs Scalable Vector Graphics (SVG) for the presentation of information. In order to achieve this the creation of a history ontology was required. • Historical Time: To deal with the complexity of time we have interval times and an event centric ontology. This means that instances with a time-dependent relation are connected using an event with an existence time which represents the validity of that connection. For the VICODI prototype the intervals are precisely defined, although a novel fuzzy temporal model has now been devised (reference) and its use is being explored for future follow-on projects. • Historical sources: To get round the lack of general repositories we decided to build our own ontology of history based upon our empirical deductive analysis of a 2000 document corpus. The second authoring tool is the expert annotation tool (see Fig. 4) which is used to insert and adjust the context settings of the VICODI historical resources. This tool also allows for every resource document to be described by ontology instances. The contextualisation engine will provide a starting set of relevant ontology instances which the expert historical editors can correct and/or add new instances if required. Fig. 4: The expert annotation tool interface >> Management System of Knowledge Space The Management System of Knowledge Space (MSKS) component is the core of the VICODI architecture. It provides for the continuous storage and management of both the ontology and the contextualized historical documents (repository). The MSKS is based on KAON for the ontology storage and management. Fig. 2: The six basic concepts (“flavours”) of our shallow concept hierarchy. Fig. 1: VICODI portal enhanced with a visual contextualisation system devoted to European history Context Engine, Transformation Engine and Multilinguality The context engine uses text categorization to build correlation scores between documents and the notions in the VICODI ontology. This allows the system to enhance the documents' visualization and linkage to give the users a faster and more intuitive understanding of a document's position among the notions represented in the ontology. The transformation engine processes the data of the relevant contextual information from the context engine and outputs it by either transforming it into SVG instances (dynamic maps) or by generating hyperlinked (contextualised) documents for display in the user interface. The transformation engine will support both graphics-based visualisation and text-based presentation of the contexts. Another novel aspect of VICODI is the multilinguality offered by Systran.BThe KAON framework provides a lexical layer on top of a language-independent ontology core for each language. Using that feature it is possible to translate the language-dependent part of the ontology without disturbing its logical structure. Historical information will be translated via an automatic translation tool into English, German, French and Latvian. SVG enabled GUI and Knowledge Portal VICODI has created a web portal with a graphical contextualisation interface, which uses Scalable Vector Graphics (SVG) to visualise digital content with the help of historical (period) maps. The portal's web application connects and integrates all the various system components. This interface is also the basis of a new knowledge portal entitled eurohistory.net (see Fig. 1). The portal contains a number of innovative elements stimulating user interaction with its contents. Users can paste their history-related texts in a special contextualisation box and have this information automatically processed and classified on the basis of LATCH (Location, Time, Category) (Wurman et al 2000). Moreover, textual information is visualised on a map of Europe from the corresponding historical period. European history terms (listed in VICODI ontology) are automatically highlighted and their contextual relevance is marked. The ontology search and browsing may be carried out either by Yahoo-type browsing or by Location (SVG maps), Time (decades from 1000-2000AD) and/or Subject (historical topics). The portal also provides web-based tools for the uploading and authoring context of new historical content. History ontology building The main purpose of the history ontology for the VICODI project is to help machine algorithms in the automatic contextualisation task by storing relevant historical knowledge in machine processable form. In order to achieve this goal an ontology with a well-defined formal semantics is needed. The task of devising an ontology of history is very daunting. On the one hand, it is always challenging to build an ontology covering a broad and very complex area of knowledge. On the other hand, history has several unique features which are problematic from an ontological point of view. • Problems • The complexity of history is immense and requires an almost unlimited number of instances and property relations. To complicate matters historians do not focus only on “what” questions but also on “when”, “where”, “who”, “how” and most importantly on “why” questions. • Historical time is uncertain and often debated. It includes many unknown dates, imprecise intervals (ca., approximately from to etc.), and overlapping time (historical periods and events extending into each other without clear start and end dates). Moreover, many ontology relations are time-dependent. • Historical Sources: there are no comprehensive and large-scale thesauri of history. During the evaluation of related works in the area we realised that existing approaches to historical ontologies were not suitable for our purposes. Some used non-formal, "intuitive" taxonomies, which mixed various, semantically different hierarchical relationships ("is-a", "part-of", "member-of") which made them unsuitable for machine processing (like Hassett or the UNESCO thesaurus). Others covered only a tiny area of history (like the Getty location names) which was too limited for our goals. Finally, the CIDOC CRM ontology standard has a formal conceptual hierarchy. However it is too complex and inflexible for our domain experts to fill it with the necessary domain knowledge (instances) which it does not presently contain.A • Our solutions • Complexity: We use a shallow concept hierarchy starting from only six basic concepts (called flavours), which are meaningful for domain/history experts: person, artefact, group, event, abstract notion and location. The hierarchy below these concepts is shallow (2-3 levels), stops at an abstraction level which is already • meaningful for historians, but is still general enough to make the place of new instances in the ontology easy to find, which speeds up the population of the ontology with new historical knowledge. The complexity of history is represented by connecting instances of these flavours by various property relations. • Conclusions • VICODI will provide an example of how a visualisation and contextualisation environment for humanities digital content can be build. Some of the most important results are: • The creation of a usable and extensible European history ontology. • Complexity within the history ontology can most easily be achieved by a shallow concept hierarchy and property relations. • The capability of KAON to provide programmatic access to the ontology makes it possible to mass upload instances with the aid of textual glossaries or Excel sheets. Authoring tools VICODI has two authoring tools. The first is a revised ontology editor based on the KAON ontology. This is an extension of the W3C RDFS standard and developed by FZI, one of the project partners (Maedche et al, 2003). The tool allows scalability for editing ontologies, as well as incorporating some usability issues related to ontology management. The editor provides several windows for representing the ontology and tools for editing and adding concepts, instances and property relations (see Fig. 3). References Maedche, A. et al, 2002. “A Conceptual Modeling Approach for Semantics-Driven Enterprise Applications”, Proceedings of the First International Conference on Ontologies, Databases and Application of Semantics (ODBASE-2002), Springer, LNAI. Motik, B. et al, 2003. “A Fuzzy Model for Representing Uncertain, Subjective and Vague Temporal Knowledge in Ontologies”, Proceedings of the Second International Conference on Ontologies, Databases and Application of Semantics (ODBASE-2003), Springer, LNAI. Wurman, R.S. et al, 2000, Information Anxiety 2, Que, Indianapolis. Ahttp://cidoc.ics.forth.gr/official_release_cidoc.html BSYSTRAN, White Papers, MT Summit 2001, 2002, 2003, http://www.systransoft.com/Technology/WhitePapers.html Fig. 3: The user interface of the ontology editor. Thanks to KAON’s capability to provide programmatic access to the ontology it is also possible to add a huge number of instances and concepts to the ontology by processing textual glossaries or Excel sheets. This - together with our simple and intuitive concept hierarchy - significantly sped up the ontology populating process, as the history domain experts could use their preferred software tools while codifying their knowledge. The manual functions of the ontology editor are therefore only needed to carry out some advanced operations, like relocating existing concepts and instances, adding new connections and visualising the existing ontology structure.