1. Text Based Similarity Metrics and Delta for Semantic Web Graphs
Krishnamurthy Viswanathan and Tim Finin, University of Maryland, Baltimore County
Motivation
Case 3: Different versions of the same SW graph
In addition, when this case is detected, generate a delta between the two versions
Classification
Text similarity is very useful in information retrie-val for near duplicate and similarity detection
Similarity metrics computed for each candidate pair
Approach
Naïve Bayes/SVM classifier: Difference only in Base-URI
Naïve Bayes Classifier: Similarity in classes and properties
SVM Classifier: Versioning Relationship
Input: corpus of SWDs
Convert to canonical form
Convert to
n-triples format
Problem
Identify pairs of similar documents
Compute Text-Based Similarity Metrics
Create Reduced Forms
Generating Deltas
Given a collection of SW graphs as RDF doc-uments, identify pairs of graphs that are similar
Generate a delta for pairs of graphs identified as having a versioning relationship
Version1
Except Version2
Subtractive Delta
Version2
Except Version1
Additive Delta
Delta
Generate delta between versions
Identify ontology versions
Contributions
Defined text-based similarity metrics char-acterizing relations between SW graphs
Evaluated these metrics for three specific cases of similarity
SW Graph Canonicalization
<person:John> <a:livesIn> _:x .
_:x <a:IsPartOf> ”USA” .
<person:John> <a:likes> ”cheese” .
_:x <a:hasCapital> :y .
“~” <a:hasCapital> “~” . # _:x _:y
“~” <a:IsPartOf> ”USA” . # _:x
<person:John> <a:likes> ”cheese” .
<person:John> <a:livesIn> “~” . #_:x
Evaluation
Case 1: Same classes and properties used but differ only in literal content
Three datasets of 400+ semantic web documents for training and testing
17 combinations of similarity metrics tested: Jaccard, Containment, Cosine similarity, Hamming distance between Simhash fingerprints
BNode Table
_:g2 <a:hasCapital> _:g1 .
_:g2 <a:IsPartOf> ”USA” .
<person:John> <a:likes> ”cheese” .
<person:John> <a:livesIn> _:g2 .
Old bnode identifier
New bnode identifier
_:y
_:g1
_:x
_:g2
Assigns uniform identifiers to blank nodes
Provides a deterministic order to statements
Empirical method that works for most examples
Type of Similarity
True Positives
False Positives
Precision
Recall
Similarity in classes & properties
0.986
0.014
0.987
Difference only in base URI
0.988
0.012
Versioning Relationship
0.909
0.091
0.913
Four reduced forms
Case 2: Differ only in base-URI
Only literals from the original n-triple file
All non-literal content from original n-triple file
Base-URI of every node replaced by “”
Literals and base-URIs replaced by “”
UMBC AN HONORS UNIVERSITY IN MARYLAND