Deliverables Contextualization engine The Context Engine is responsible for contextualising textual logical resources and SVG historical maps. It includes the transformation engine for rendering contextualised textual resources and supporting the contextualisation of visualisations that can utilise XML-based contextualisation data, such as the SVG Historial maps used in the VICODI project. The context engine includes both a server and client. Training data submitted to the server are used to create Context Index Vectors, which are correlation vectors, which describe relationships between entities (concepts or instances) known to the system. In the present system, only terms URIs (concept or instance) derived from any ontology are sent by the client to the server. The server records the ontology entities received during training and also their top level concept e.g. location, category, person, event, artefact, abstract notion, social organisation, etc - The server not only supports training, but also vector similarity functinalities: term-term, term-document, document-document. High-level functionality includes: - Provides automated context estimate for given resource term frequency vector. - Create training resource given expert term vector and frequency vector. The expert ranks each term in resource according to the importance to the resource. - Provides context ranking (IR) for given list of result context vectors and one comparison vector. The client also supports LATCH reseasoning utilising the context of the resource (dynamically derived or stored) and the ontology. LATCH context means that we predict the most important locations, categories and time intervals (narrow for historical map queries, etc. and a wider interval for user reference). MT module Based on the New Generation Machine Translation Technology, SYSTRAN has constructed a hybrid machine translation system from English into Latvian based on Analysis and Transfer modules. Moreover, customisation dictionaries have been constructed in order to enhance the MT engines English to French and English to German improving the raw Machine Translation output of the Vicodi corpus and the Ontology instances. Eurohistory.net portal During VICODI a web portal of European history was created. The basic functionality of it is as follows: Once the user has found (or written himself) an article, he/she can paste the text (or URL) into the on-line contextualisation tool and retrieve the same text with pontextual links in it (as underlined words, phrases, names, years, etc.). By recognizing certain parameters (keywords, years, names, etc.), the system generates appropriate context both for each hyperlinked word and the whole article. The user receives the contextual data also in the form of interactive graphical maps. So, if the user pastes (into contextualisation tool) an article about Elisabeth I, the system will automatically generate the 16th century Europes map from predefined SVG drawings (embedded in MSKS) and will put links on the map - in places where they belong - individual countries, regions, etc. The user is able to assess the 16th century situation both in England, the whole Europe and in individual countries. Thereby VICODI provides visually enriched interface to navigate both in place and time. MSKS subsystem The technology of Visual contextualization [TVC] developed during VICODI provides a context for digital resources which raises them to the knowledge level (we are referring to the well-known definition which states that knowledge is information in context). Moreover, the resource context is also visualized which makes it easier for users to comprehend the information conveyed by the resources. TVC is based on the perception that considering users context improves the results produced by an information system. Moreover, one of our most important insights is that by reading a document the user accepts the meaning of the document (its document context) as part of her user context. Therefore the context of the actual document can be used to filter and rank the results of subsequent queries. Another important insight is that visualizing the actual document context (which describes the meaning of the document) helps understanding the meaning of the document significantly. By combining visualization and context-based improvement of information system results we get the approach, which we term visual contextualization. It is important to note that our approach is not constrained to textual documents, but it is applicable generally to digital resources, like a pictures or videos. SVG maps The result "SVG maps" means that we have produced both a set of 105 historical SVG maps Europe, and developed a code to enable their usage to visualise XML information retrieved from the database. Both these results are offered for further use. The ultimate goal of the VICODI SVG-based visualization was to enable Web users to visualize the retrieved information and to provide them with innovative tools to perform dynamic queries. Therefore, for the context visualization we used choropleth SVG maps, which are one of the most common forms of mapping data today, because of their simple and comprehensive appearance. 105 historical maps of Europe were created. They cover a period from the year 1000 to 2003 by decades: one map per decade; and the period 500 to 1000 by centuries. The maps contain period country names, which are retrieved from the ontology. The SVG visualisation enables a big variety of interface controls such as dynamic query, dynamic classification, geographic object data identification, user setting adjusting, as well as panning and zooming. Ontology of European history The domain-specific background knowledge in VICODI is encoded in an ontology of European history. We developed a completely new ontology, which is available for interested parties on the VICODI website under the GNU Free Documentation License (GFDL)6. The ontology is used on one hand as a vocabulary for document context definitions. On the other hand it is used by heuristics during automatic contextualization and context-based search as a knowledge base. We implemented the ontology using the open-source KAON framework, which provides an extension of the W3C RDFS standard. In particular KAON provides multi-lingual features (see also Section 4.4) inverse, transitive and symmetric properties in addition to standard RDFS features. Moreover, the relational database based persistence layer of KAON allows it to handle huge ontologies like the VICODI ontology with acceptable performance. Searching for OpenAIRE data... There was an error trying to search data from OpenAIRE No results available
