Contexts in source publication

Context 1

... visual topic map contains two types of topics - ’standard’ topics and ’visual’ topics. Visual topics represent concepts as images. Thus a visual topic presents a repository image and has the image file name as its primary name and a resource of type ’File Path’ containing the path of the image file. In this way the primary topic name is not related to any specific natural language. The term for the concept reified by that topic, translated in different languages can be added as additional topic names, scoped with the corre- sponding language topic. The use of scopes allows displaying only the names scoped with a theme specified by the user when visualizing the topic map. Thus concept names can be displayed in different languages by specifying different scopes. Among the implemented additions in TM4L is the treatment of topic names and resources. In addition to the two resource types defined in the Topic Map standard - in- ternal resources that contain text included directly in the topic map and external resources that specify URLs of web resource, we have introduced a third type, different from both of them. It resembles the external resources in that it is not included in the TM and is specified by its address, which however is not a URL, but a path of a file residing on the local machine where TM4L is installed. The type of the file is one of: JPEG, GIF, or PNG. Topics represent concepts. In a conventional topic map, a concept is reified with a topic, which is named with the term that is used to name the concept. In a visual topic map, a concept is reified with an image, which is reified with a topic having as a name the name of the image file. Since a file name often doesn’t reveal the semantics of the concept (image), it is very important for the topic map authoring that the tool provides a mean for displaying the image. Thus in TM4L, topic name information is displayed differently for the standard and visual topics. If a topic is a standard topic, then the topic name string is displayed; if it is a visual topic, then in addition to the name, the image represented by the topic is displayed (see Figure 2). This way by seeing the image, the author can identify the concept and subsequently add additional name and/or annotate it. Similarly, in the visualization of the topic map, for all ’standard’ topics, their topic names are displayed; for the visual topics - icons of the images that they represent are displayed. For each icon, the corresponding full-size image can be displayed if the ”View image” option of the context- sensitive menu is selected. (See Figure 3 ). As it was already mentioned, the concept of visual topic maps was introduced to facilitate the structuring and use of large collections of images. Since the visual topics are represented in the topic map by the paths of the corresponding images, the creation of such a map is a time-consuming and unpleasant work for the author. From another side, such a presentation allows an automatic extraction of (a draft) of the topic map. In implementing this functionality we took in consideration the fact that in many cases images are already classified in subdirectories with meaningful names (indicat- ing the scope of the stored there images). Thus, in order to support the author, we implemented an automatic creation of a draft topic map by recursive extraction of the structure of a specified file directory, containing image files organized hierarchically in subdirectories. Figure 4 displays the dialog for extracting topics from a file directory. The author spec- ifies the directory, the name of the relationship type to be used in building the topic hierarchy and the root topic to which the extracted hierarchy should be attached. The extracted topics are added to the current (a newly created or an opened) topic map and after its reloading are displayed in the Topic panel (see Figure 5). The author can then use this draft map as a starting point to produce the desired map. For example, he can delete unwanted topics, restructure the topics hierarchy (by adding/deleting parent topics), annotate topics, create new relationships between topics. Figure 6 shows the visualization of the automatically extracted topic map from Figure 5. The ”Visual Topic Maps” concept introduces a new semantic layer between collections of learning objects and learning material. The topics link semantically related learning objects. As learning objects by definition are not restricted in size the links from the visual topic maps refer to specific topics within the learning objects to guide the reader precisely to relevant sections. Furthermore, visual topic maps provide the context and scope that are required for the specification and use of metadata. The visual topic maps descriptions can be seen as rich metadata that annotate the referred learning objects. Visual topic maps specific metadata and domain specific metadata allow for the customization of topic maps according to the needs and scope of individual users. The Vi- sual topic maps themselves form new learning objects that provide annotation to thematically linked learning objects stemming from large document collections. The metadata of visual topic maps are based on MPEG7 for the links to the multimedia document of the referenced learning objects and on the MPEG7 Semantic Descriptor for each node of the topic map. These metadata are complemented by attributes for instructional information and story semantics. We envis- age that the stories will be written by domain experts and used by teachers. The term ”Visual” was chosen to express the desire to communicate via a medium, the visual, that is familiar to everyone as there and therefore can be easily au- thored and easily understood. The visual topic maps provide the mechanism required expressing knowledge and interpretations in a natural way, the visual topic maps metadata deliver the precision for search and retrieval both of topics and underlaying documents. The visual topics concept aims at classifying large document collections like they are provided by the ”Memory of the past” project for the preser- vation of the knowledge and culture. Work is currently un- dertaken to specify multi-dimensional metadata under topic maps model and to collect related data from experts and pupils. The next step will be for experts and non-experts such pupils to enrich visual topic maps with that set the vast amount of single documents or learning objects in context to make them more easily accessible. We would like to thanks NII for this International Co- operation support and the Japanese Ministry of Education, Science and Technology for the support to the visual topic maps project under the Geomedia ...

Context 2

... visual topic map contains two types of topics - ’standard’ topics and ’visual’ topics. Visual topics represent concepts as images. Thus a visual topic presents a repository image and has the image file name as its primary name and a resource of type ’File Path’ containing the path of the image file. In this way the primary topic name is not related to any specific natural language. The term for the concept reified by that topic, translated in different languages can be added as additional topic names, scoped with the corre- sponding language topic. The use of scopes allows displaying only the names scoped with a theme specified by the user when visualizing the topic map. Thus concept names can be displayed in different languages by specifying different scopes. Among the implemented additions in TM4L is the treatment of topic names and resources. In addition to the two resource types defined in the Topic Map standard - in- ternal resources that contain text included directly in the topic map and external resources that specify URLs of web resource, we have introduced a third type, different from both of them. It resembles the external resources in that it is not included in the TM and is specified by its address, which however is not a URL, but a path of a file residing on the local machine where TM4L is installed. The type of the file is one of: JPEG, GIF, or PNG. Topics represent concepts. In a conventional topic map, a concept is reified with a topic, which is named with the term that is used to name the concept. In a visual topic map, a concept is reified with an image, which is reified with a topic having as a name the name of the image file. Since a file name often doesn’t reveal the semantics of the concept (image), it is very important for the topic map authoring that the tool provides a mean for displaying the image. Thus in TM4L, topic name information is displayed differently for the standard and visual topics. If a topic is a standard topic, then the topic name string is displayed; if it is a visual topic, then in addition to the name, the image represented by the topic is displayed (see Figure 2). This way by seeing the image, the author can identify the concept and subsequently add additional name and/or annotate it. Similarly, in the visualization of the topic map, for all ’standard’ topics, their topic names are displayed; for the visual topics - icons of the images that they represent are displayed. For each icon, the corresponding full-size image can be displayed if the ”View image” option of the context- sensitive menu is selected. (See Figure 3 ). As it was already mentioned, the concept of visual topic maps was introduced to facilitate the structuring and use of large collections of images. Since the visual topics are represented in the topic map by the paths of the corresponding images, the creation of such a map is a time-consuming and unpleasant work for the author. From another side, such a presentation allows an automatic extraction of (a draft) of the topic map. In implementing this functionality we took in consideration the fact that in many cases images are already classified in subdirectories with meaningful names (indicat- ing the scope of the stored there images). Thus, in order to support the author, we implemented an automatic creation of a draft topic map by recursive extraction of the structure of a specified file directory, containing image files organized hierarchically in subdirectories. Figure 4 displays the dialog for extracting topics from a file directory. The author spec- ifies the directory, the name of the relationship type to be used in building the topic hierarchy and the root topic to which the extracted hierarchy should be attached. The extracted topics are added to the current (a newly created or an opened) topic map and after its reloading are displayed in the Topic panel (see Figure 5). The author can then use this draft map as a starting point to produce the desired map. For example, he can delete unwanted topics, restructure the topics hierarchy (by adding/deleting parent topics), annotate topics, create new relationships between topics. Figure 6 shows the visualization of the automatically extracted topic map from Figure 5. The ”Visual Topic Maps” concept introduces a new semantic layer between collections of learning objects and learning material. The topics link semantically related learning objects. As learning objects by definition are not restricted in size the links from the visual topic maps refer to specific topics within the learning objects to guide the reader precisely to relevant sections. Furthermore, visual topic maps provide the context and scope that are required for the specification and use of metadata. The visual topic maps descriptions can be seen as rich metadata that annotate the referred learning objects. Visual topic maps specific metadata and domain specific metadata allow for the customization of topic maps according to the needs and scope of individual users. The Vi- sual topic maps themselves form new learning objects that provide annotation to thematically linked learning objects stemming from large document collections. The metadata of visual topic maps are based on MPEG7 for the links to the multimedia document of the referenced learning objects and on the MPEG7 Semantic Descriptor for each node of the topic map. These metadata are complemented by attributes for instructional information and story semantics. We envis- age that the stories will be written by domain experts and used by teachers. The term ”Visual” was chosen to express the desire to communicate via a medium, the visual, that is familiar to everyone as there and therefore can be easily au- thored and easily understood. The visual topic maps provide the mechanism required expressing knowledge and interpretations in a natural way, the visual topic maps metadata deliver the precision for search and retrieval both of topics and underlaying documents. The visual topics concept aims at classifying large document collections like they are provided by the ”Memory of the past” project for the preser- vation of the knowledge and culture. Work is currently un- dertaken to specify multi-dimensional metadata under topic maps model and to collect related data from experts and pupils. The next step will be for experts and non-experts such pupils to enrich visual topic maps with that set the vast amount of single documents or learning objects in context to make them more easily accessible. We would like to thanks NII for this International Co- operation support and the Japanese Ministry of Education, Science and Technology for the support to the visual topic maps project under the Geomedia ...