A New Tool for Rectangular Dualization

OcORD is a software tool for rectangular dualization. Rectangular dualization is a dual representation of a plane graph introduced in the early seventies. It proved to be effective in applications such as architectural space planning and VLSI floorplanning. However, not all plane graphs admit a rectangular dual, which imposes severe limitations on its use in other applications. OcORD aims at freeing rectangular dualization from such restrictions and proving its effectiveness in graph visualization. This is achieved in two ways. Firstly, OcORD features a new linear-time algorithm creating a rectangular dual of any plane graph. Secondly, it shows how nice drawings of a graph can be easily obtained from its rectangular dual. Finally, the automatic generation of a Virtual World through rectangular dualization is described. [DOI: 10.1685/CSC09301] About DO

Entity Discovery and Annotation in Tables

International audienceThe Web is rich of tables (e.g., HTML tables, speadsheets, Google Fusion tables) that host a considerable wealth of high-quality relational data. Unlike unstructured texts, tables usually favour the automatic extraction of data because of their regular structure and properties. The data extraction is usually complemented by the annotation of the table, which determines its semantics by identifying a type for each column, the relations between columns, if any, and the entities that occur in each cell. In this paper, we focus on the problem of discovering and annotating entities intables. More specifically, we describe an algorithm that identifies the rows of a table that contain information on entities of specific types (e.g., restaurant, museum, theatre) derived from an ontology and determines the cells in which the names of those entities occur. We implemented this algorithm while developing a faceted browser over a repository of RDF data on points of interest of cities that we extracted from Google Fusion Tables. We claim that our algorithm complements the existing approaches, which annotate entities in a table based on a pre-compiled reference catalogue that lists the types of a finite set of entities; as a result, they are unable to discover and annotate entities that do not belong to the reference catalogue. Instead, we train our algorithm to look for information on previously unseen entities on the Web so as to annotate them with the correct type

Entity Discovery and Annotation in Tables

International audienceThe Web is rich of tables (e.g., HTML tables, speadsheets, Google Fusion tables) that host a considerable wealth of high-quality relational data. Unlike unstructured texts, tables usually favour the automatic extraction of data because of their regular structure and properties. The data extraction is usually complemented by the annotation of the table, which determines its semantics by identifying a type for each column, the relations between columns, if any, and the entities that occur in each cell. In this paper, we focus on the problem of discovering and annotating entities intables. More specifically, we describe an algorithm that identifies the rows of a table that contain information on entities of specific types (e.g., restaurant, museum, theatre) derived from an ontology and determines the cells in which the names of those entities occur. We implemented this algorithm while developing a faceted browser over a repository of RDF data on points of interest of cities that we extracted from Google Fusion Tables. We claim that our algorithm complements the existing approaches, which annotate entities in a table based on a pre-compiled reference catalogue that lists the types of a finite set of entities; as a result, they are unable to discover and annotate entities that do not belong to the reference catalogue. Instead, we train our algorithm to look for information on previously unseen entities on the Web so as to annotate them with the correct type

Confluent Drawing Algorithms Using Rectangular Dualization

The need of effective drawings for non-planar dense graphs is motivated by the wealth of applications in which they occur, including social network analysis, security visualization and web clustering engines, just to name a few. One common issue graph drawings are affected by is the visual clutter due to the high number of (possibly intersecting) edges to display. Confluent drawings address this problem by bundling groups of edges sharing the same path, resulting in a representation with less edges and no edge intersections. In this paper we describe how to create a confluent drawing of a graph from its rectangular dual and we show two important advantages of this approach

Uncovering the spatial relatedness in Wikipedia

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