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A Grailog Visualizer for Datalog Ruleml Via XSLT 2.0 Translation to SVG

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A Grailog Visualizer for Datalog Ruleml Via XSLT 2.0 Translation to SVG University of New Brunswick Faculty of Computer Science CS 6795 Semantic Web Techniques A Grailog Visualizer for Datalog RuleML via XSLT 2.0 Translation to SVG Project Proposal Team 1: Martin Koch Sven Schmidt [email protected] [email protected] October 29, 2012 Advisor: Dr. Harold Boley Contents 1 Introduction 1 2 Objectives 1 3 Methodology 2 4 Languages / Tools 4 4.1 Datalog RuleML . 4 4.2 Grailog . 5 4.3 Scalable Vector Graphics (SVG) 1.1 . 5 4.4 XSL Transformations (XSLT) Version 2.0 . 5 ii List of Figures 2.1 The translation takes place one at a time (normalization sequence) [3] . 2 3.1 (Current) Mapping of Datalog RuleML to Grailog, i.e. subset to subset [3] . 3 4.1 The left side shows a Datalog RuleML rule visualized as Order-Labeled (OrdLab) Tree. On the right side is shown the corresponding XML code of the rule. [1] . 4 iii 1 Introduction Datalog RuleML [1, 5] is a n-ary, rule based markup language that is a sublanguage of the RuleML family [4]. Because of its usage for artificial intelligence (AI) and the Semantic Web, it is built with a format that is more suitable for machine readability than for human read- ability. In spite of making the Datalog RuleML language more human readable, one natural approach is to transform its knowledge base, consisting of facts, rules and queries, in a suit- able way. One solution to that is to create a visualized representation of the knowledge base, because of the fact that humans naturally better understand a well developed visualization (abstraction) than a heavy bulk of text or code. Therefore, the final goal is to create a suf- ficient visualization, which is much easier to read, learn and remember. One of such well developed approaches for visualizing major (Semantic Web) logics is Graph inscribed logic invokes imagery for logic (Grailog) [3]. It describes the mapping for several defined graph constructs to each corresponding symbolic logic construct of the considered knowledge base, which leads to a better human readability. At this point, it seems likely, to try to automatize this transformation as far as possible, so that finally each Datalog RuleML knowledge base can be easily visualized as a Grailog representation. 2 Objectives The final result of the project should be an initial version of a so called ’Grailog Visual- izer’. This tool should handle the task described above, i.e. the transformation from Datalog RuleML to a visualized SVG Grailog representation. The tool will be open source in its final version and it will be available on the Web, accessible from the RuleML and the Grailog sites. The main objective of the project is to define a mapping from Datalog RuleML, written in Extensible Markup Language (XML) [9], to Scalable Vector Graphics (SVG) [6, 9], also written in XML. This will be realized by using eXtensible Stylesheet Language Transformations 2.0 (XSLT 2.0) [7], a language that permits translations from a XML source document to a newly generated XML document. By implementing different elements in the XSLT 2.0 XML document, each Datalog RuleML fact, (as resources permits) rule and query in XML, will be automatically translated to its corresponding SVG elements in XML. This will be done one element at a time, as shown in Figure 2.1, to avoid highly complex layouting issues in the SVG part of the project. So, for example, no ‘merging’ of box copies has to been handled. The resulting SVG elements, which are based on a distinct Grailog subset can than be used for rendering. To put it all in a nutshell, the Grailog Visualizer translates Datalog’s n-ary relationships to Grailog’s directed hyperarc arrows, that connect the argument-box nodes. The resulting Grailog representation of the Datalog RuleML source document can than finally be rendered by using suitable tools, for example one of the recent major web browsers. For this initial version, the assumption is made that the source documents only consist of Datalog RuleML knowledge bases and queries. The resulting visualization will be in a ‘fully 1 Figure 2.1: The translation takes place one at a time (normalization sequence) [3] node copied’ normal form, which means that the separation of clauses of the symbolic form in the Datalog RuleML source document is also kept in the resulting graphic form of the SVG Grailog representation. 3 Methodology Before the implementation part of the project can be started, it is essential to analyze and completely understand the mapping between the Datalog RuleML symbolic subset and the associated Grailog subset. At the same time, the necessary knowledge about SVG and XSLT 2.0 has to be acquired. The chosen subset of Grailog has to be represented in a suitable subset of SVG, using XML. This leads to combinations of several individual graphical elements, for example the different arrows and rectangles of the Grailog visualization. To create these different graphical objects, SVG provides many basic elements, like paths, lines or polylines, which can for example be used for parts of the arrows. To represent the nodes, different rectangle variants of SVG can be used. The so far considered subset of Grailog is shown in Figure 3.1. The subset that will finally be used by the tool may change to some extent. In the next step, the mapping from RuleML to SVG will be defined, using XSLT 2.0. Each element of Datalog RuleML, facts, (as resources permits) rules and queries will be translated in its corresponding graphical element, one at a time. This part of the project mainly depends on the SVG part before, but nevertheless it should be possible to implement both parts nearly in parallel, by using simple temporary graphical SVG elements for the transformation process, which can be replaced by its actual counterparts further in the project. For now, the mapping consists of five major parts, mapping individual constants and variables, mapping binary relations, mapping n-ary relations, mapping single-premise rules and mapping multi-premise rules. If resources permit, the mapping of queries will be considered, too. The current mapping can be seen in Figure 3.1. The final result of the implemented transformation process should be an automatically gener- ated SVG document, which can then be used for rendering the Grailog representation of the given Datalog RuleML knowledge base. To proof the reliability of the transformation process, different kinds of source documents (in the range of the made assumptions) will be translated, the results will be evaluated and possible further improvements will be discussed. 2 Figure 3.1: (Current) Mapping of Datalog RuleML to Grailog, i.e. subset to subset [3] 3 4 Languages / Tools For this project, the rule language Datalog RuleML will be used as the language of the con- sidered rules which should be visualized via Grailog. Grailog itself provides the systematics for visualizing the logic knowledge representation as generalized graphs. For the implemen- tation and the rendering of the visualization, the modularized language for describing two- dimensional vector and mixed vector/raster graphics in XML, SVG will be used. The trans- formation language XSLT 2.0 provides the necessary tools to perform the transformation from the Datalog RuleML knowlegde bases and queries to the SVG Grailog visualization. In the following, each language/tool will be illustrated shortly. 4.1 Datalog RuleML RuleML is a family of rule based languages, which are used for sharing rule bases written in XML and publishing them on the Web. Datalog RuleML is one rule sublanguage of RuleML, based on the Datalog logic. Moreover, Datalog is the semantic foundation of RuleML and can be seen as an intersection of SQL and Prolog [1]. Datalog is used to define facts and rules and therefore is used to create logic knowledge bases and queries. One simple Datalog RuleML logic relation can be seen in Figure 4.1. It describes the logic relation that a customer is premium, if the customer has spent at least 5000 euros in the previous year. The XML code, the rule is written in, is exactly the code that will be translated to Grailog by the Grailog Visualizer. The graph on the left side of the figure is an Ordered-Labeled (OrdLab) Tree. This representation of the rule is another visualization approach, prior to Grailog. Figure 4.1: The left side shows a Datalog RuleML rule visualized as Order-Labeled (OrdLab) Tree. On the right side is shown the corresponding XML code of the rule. [1] 4 4.2 Grailog Grailog describes systematics to visualize knowledge representations with graphical elements. Each of the introduced graphical elements is mapped to its corresponding symbolic logic construct. The benefit of Grailog is that its resulting visual representations of knowledge sources are much easier to read for humans than the original source code is. Moreover, the rules for the transformation are easy to learn and to remember. One principle of Grailog is that the used graphs should be natural extensions of Directed La- beled Graphs (DLGs). Another principle dictates that the used Graphs should allow stepwise refinements for logic constructs, like Description Logic constructors or general PSOA RuleML terms. These principles all support the main goal of Grailog, namley easy understanding and application of the systematics. Another major part of Grailogs visualization elements are the directed hypergraphs. These are used for dealing with n-ary relationships, Datalog RuleML (in contrast to Datalog) is capable of. Examples of the different Grailog elements can be seen in Figure 3.1. 4.3 Scalable Vector Graphics (SVG) 1.1 With the languague SVG, two-dimensional graphics, both static and dynamic (i.e., interactive or animated) can be described in XML.
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