Dr. Dobb's Journal - July 2008 - (Page 34)

Core Technology GRAPHS VERSUS OBJECTS // Now, how many items has John bought? int count = 0; for (Transaction t : transactions) { if (t.getBuyer() == buyer) { count++; } } System.out.println("John has purchased " + count + " item(s)"); // Now, what has John bought? count = 0; System.out.println("John has bought:"); for (Transaction t : transactions) { if (t.getBuyer() == buyer) { System.out.println (" " t.getContainsItem().getLabel()); } } resent classes (similar to OO classes), the thin named lines represent relationships, and the rectangles represent actual data. The numbers (1) and types (string) indicate restrictions placed on a relationship or type. Relationships can be represented in two ways—object properties that link two objects (classes) and datatype properties that link a data item with an object. Here is an extract of the ontology in abbreviated RDF/XML format. We’ve used TopBraid Composer (www.topbraidcomposer .com) but Protege (protege.stanford.edu) or other editors would work just as well: Graphs and Objects The resulting output is: John has purchased 1 item(s) John has bought: High-wolf shiny toaster Our objects quickly created a basic solution. Additional queries require additional coding. This works to constrain the variety and power of the questions. As we continue to code, we realize we created a proprietary solution. Integration becomes difficult in two ways—more of the same instances and different types or classes of instances. The former requires custom integration code and is subject to our chosen storage method (for this example, it is merely inmemory arrays). The latter, different type of classes, creates an N2 problem as we write custom code to combine objects from a different class. (Imagine that a similar solution did not create a transaction class but rather a user purchase class.) Cooking with Graphs Now let’s address the same problem with graphs. We implemented the graphs using the Web Ontology Language (OWL)—an expressive knowledge representation language based on the Resource Description Framework (RDF). Based on XML, RDF connects information using a “triple”—a subject, predicate, and object. This basic approach can represent all kinds of knowledge constructs such as the class structure (“Transaction hasBuyer Person”), instance data (“toaster hasCost $12”), and constraints (“PurchasableItem contains 1 Manufacturer”). Usually, the main data model, when expressed in OWL, is called an “ontology” . Figure 2 illustrates the graph. There is no drawing standard for graphs but the diagram adheres to common practices. The ovals repClass property Data property PurchasableItem hasSellDate hasCost 1 cost decimal Figure 2: Graph model. Switching to graphs, let’s apply our business acumen and create an online store where the knowledge representation is in the data instead of the code. There is no need to create the representation in the code itself. The Java code below relies on Jena (jena.sourceforge .net), the most popular open-source package for creating Semantic Web solutions currently available. First, we’ll load our graph model, which defines the classes and properties in Figure 2. To view the full ontology, go to graphsvsobjects .blogspot.com. OntModelSpec s = new OntModelSpec(OntModelSpec.OWL_DL_MEM_RULE_INF); ntModel m = ModelFactory.createOntologyModel(s); hasSeller hasBuyer Person Transaction containsItem Second, based on this graph, we can begin to add the items and related facts. Here we create instances; they could have been contained in the original ontology. This approach allows you to see both methods. Again, this is just a subset of the code. The full suite is available online: // Matt Resource seller = m.createResource(defaultNS + "mattFisher"); m.add(seller, RDF.type, m.getResource(defaultNS + "Person")); m.add(seller, RDFS.label, m.createTypedLiteral("Matt Fisher", XSDDatatype.XSDstring)); // is selling his special toaster Resource toaster = m.createResource(defaultNS + "shinyToaster"); m.add(toaster, RDFS.label, m.createTypedLiteral("High-wolf shiny toaster", XSDDatatype.XSDstring)); m.add(toaster, RDF.type, m.getResource(defaultNS + hasManufacturer 1 manufacturer shipDate string hasShipDate sellDate 34 Dr. Dobb’s Journal l www.ddj.com l July 2008 http://www.topbraidcomposer.com http://www.topbraidcomposer.com http://protege.stanford.edu http://jena.sourceforge.net http://jena.sourceforge.net http://graphsvsobjects.blogspot.com http://graphsvsobjects.blogspot.com http://www.ddj.com

Table of Contents Feed for the Digital Edition of Dr. Dobb's Journal - July 2008

Dr. Dobb's Journal - July 2008 Contents Friday Night Fish Fry Alia Vox Developer Diaries Developer’s Notebook Engineers Without Borders Conversations Patricia Tries Event-Based Architectures Graphs Versus Objects Lock-Free Queues Dr. Dobb’s Architecture & Design World Java and the Nokia N10 Internet Tablet Effective Concurrency The Agile Edge Swaine’s Flames

Dr. Dobb's Journal - July 2008

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