Definitions. Reasons for Change. Basic Operations. Automatic Ontology Evolution with the Aid of Graphs and Semantics 21/09/2011.

Transcription

1 Ontology A formal, explicit specification of a shared conceptualisation Automatic Ontology Evolution with the Aid of Graphs and Semantics Artemis Parvizi Ontology Evolution Ontology Learning Timely adaptation of an ontology to change while maintaining consistency Forming an ontology by knowledge acquisition from text Definitions 2 A well-established ontology evolution application should be able to perform these operations: Changes in the domain Changes in the shared conceptualisation Changes in the specification Domain Adding Concepts Adding Relations Deleting Concepts Deleting Relations Merging Concepts Replacing Relations Renaming Concepts Specification Conceptualisation Reasons for Change 3 Basic Operations 4 1

2 Konstantinidis et al[1] stress the importance of determining what has to be changed and how by: Model Selection Supported Operations Consistency Model Inconsistency Resolution Action selection Metadata Driven Ontology Evolution A Software Engineering Approach Community-Based Ontology Evolution Information Extraction Evolutionary Approaches * Applications of some exists [1] A framework and its application to rdf, 2007 Ontology Change Process 5 Ontology Evolution 6 Visualisation (Necib 2003, Horridge 2009, Rhee 2007, Bohm 2009) Structured data sources (databases) Semi-structured data sources (WordNet, Cyc, ) Unstructured data sources (documents, web pages) Representation (Nebot 2009) * Applications of all exists Ontology Learning 7 Graph Theoretic Ontology Modelling 8 2

3 Taxonomic subclassof disjointwith An ontology graph is a directed multigraph defined by: O = { ζ, R} ó G(E,V) Non-taxonomic hascolour ismemberof G(E,V) is a directed graph ζ is the list of all concepts R is the list of all binary relations Ontology Graph 9 Predicting taxonomic relations Predicting non-taxonomic relations Predicting complex non-taxonomic relations Converting ontology graph into OWL Maintaining consistency Evaluation (precision, recall, and F-measure) Automatic Ontology Evolution System CheapBook Book hasprice max 2 Different Relations 10 Ontology graph Semantic similarity (WordNet) a(a1, b1 ) + a(a1, b2 ) + + a(an, bm ) n!m Rule-based pattern recogniser (9 rules) Framework s(a, b) = 11 Taxonomic Relations 12 3

4 Sparse ontologies Lack of disjointwith Presence of individuals Multiple parents Some restrictions (min or max) Equivalent Presence of non-taxonomic relations Adding affixes Ambiguous concepts Framework Ontology Travel * Human and Pets * University * Career * Movie * Vehicle * Particle ** Amino-Acid ** 13 First approach Ontology Hierarchy filtering!(1! s(an, bm )) * log(1! s(an, bm )) Semantic similarity Semantic similarity ratio Apple hascolour Green Banana hascolour Yellow Second approach Frequent patterns Hierarchy filtering Results Input concept: Orange Mandarin hascolour Orange Non-Taxonomic Relations 15 F-score Without framework With framework Unknown * General purpose domain ** Domain specific F-score First approach Second approach Travel * Human and Pets * University * Career * Movie * Vehicle * Unknown Amino-Acid ** Results 14 * General purpose domain ** Domain specific 16 4

5 Complex Relations Conjunction Disjunction Inclusive Exclusive Inclusive disjunction (some) Restrictions Some Only Exactly Min Max Apple hascolour Yellow or Apple hascolour Red Exclusive disjunction (only and exactly) Table hasleg only Three Or Table hasleg only Four Apple hascolour some Green Banana hascolour only Yellow Complex Relations 17 Examples 18 Ternary comparison FrameNet Analysing a series of change Individuals Remaining Points 19 5