Presentation on theme: "RDFBrowser A tool to analyse metadata Bernhard Schueler CSCI 8350, Spring 2002,UGA."— Presentation transcript:
RDFBrowser A tool to analyse metadata Bernhard Schueler CSCI 8350, Spring 2002,UGA
Bernhard Schueler - RDFBrowser 2 Overview Intention Implementation Demo Where are the semantics? Unfinished feature: Synonyms, Homonyms, Similarity between RDF-Graphs What is unique about RDFBrowser
Bernhard Schueler - RDFBrowser 3 Intention Provide a tool to analyze RDF-based metadata. This includes everything, which is or will be developed on top of RDF. This tool should allow for: Convenient browsing, Comparisons between files, focusing on helping the user find semantic similarities/differences, Synonyms, homonyms, graph similarity.
Bernhard Schueler - RDFBrowser 4 Implementation – Parsing Parsing of RDF files: ARP, Another RDF Parser, by Jeremy Carrol, HP. Uses XERCES XML-parser. I use this parser to extract the triples of the RDF data model.
Bernhard Schueler - RDFBrowser 5 Implementation – Knowledge base RDFBrowser uses AMZI! Prolog to store and query the RDF triples. AMZI! Prolog provides interfaces to Java, C, C++, Delphi and more. It runs under Windows and UNIX. Prolog can easily be abused as database. First-argument indexing provides a certain efficiency. I considered it to be convenient, especially for advanced inferences, such as finding synonyms (which unfortunately is unfinished)
Bernhard Schueler - RDFBrowser 6 Implementation – GUI The graphical user interface is realized using Java (JDK 1.3.1), especially the “Swing” library. All parts of the system run at least under Windows and UNIX.
Bernhard Schueler - RDFBrowser 7 Demo Not in here … but on the screen (hopefully).
Bernhard Schueler - RDFBrowser 8 Where are the semantics? RDFBrowser tries to overcome syntactic barriers to help the user retrieve the semantics. The ability to simultaneously browse files should highlight semantic relationships. A feature to find synonyms, homonyms, and similar structures in the underlying RDF graph would provide semantic analysis of Different descriptions of the same domain, Descriptions of different domains.
Bernhard Schueler - RDFBrowser 12 Search space – Example
Bernhard Schueler - RDFBrowser 13 Search space - Exponential The (sub-)graph isomorphism problem is in NP. The size of the search space is larger than n!. Precisely: n(1+ (n-1)(1+(n-2)(1+(n-3)…2(1+1). And that’s only exact matches! 9 Nodes: more than 362880 possible matchings.
Bernhard Schueler - RDFBrowser 14 Pruning: Ullmann vs. A*
Bernhard Schueler - RDFBrowser 15 My Approach Only querying of subgraphs of a small user defined length makes sense in a browser. Inexact matches are more likely than an exact match of 42 Nodes.
Bernhard Schueler - RDFBrowser 16 My Approach – Pruning Heuristics for pruning the search tree: Same labels of nodes and edges (URIs), Accuracy (percent of not matched nodes), Consider only reachable nodes.
Bernhard Schueler - RDFBrowser 17 *Indexing techniques There are advanced techniques for indexing graphs to speed up the average case, e.g. based on the number of adjacent nodes. But imagine the tree of your direct ancestors (excluding uncles and aunts). Everyone has 2 parents…
Bernhard Schueler - RDFBrowser 18 What is unique about RDFBrowser Regarding the semantic information contained in RDF files this browsers weakness is its strength: It does not consider anything on top of RDF, e.g. RDFS, DAML, OIL. Thus, it can work with any of them.
Bernhard Schueler - RDFBrowser 19 Literature Dennis Shasha, Jason T. L. Wang, Rosalba Giugno. Algorithmics and Applications of Tree and Graph Searching. Jeremy Carrol. Matching RDF Graphs. Draft, July 2001. Koebler, Schoening, Toran. The Graph Isomorphism Problem: Its structural complexity.Birkhaeuser, Boston, 1993.