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User Modeling for Personalized Web-Based Systems 239
Glue GLUE is a system that employs machine learning techniques to find ontology
mappings on the Semantic Web [24]. Given two ontologies, for each concept in one ontology,
GLUE finds the most similar concept in the other ontology. It calculates joint probability
distribution of the concept using multiple learning strategies, each of which exploits different
type of information present in data instances and ontology schema.
GLUE consists of Distribution Estimator, Similarity Estimator, and Relaxation Labeler. The
Distribution Estimator takes as input two taxonomies O 1 and O 2 together with their data
instances and compute the joint probability distribution for every pair 〈A ∈ O 1 ,B ∈ O 2 〉 of
concepts, i.e. it computes P (A, B), P (A, ¬B), P (¬A, B), P (¬A, ¬B).
To achieve it, it uses three distinct learners:
– The Content Learner – takes into account the frequencies of words in the textual content
of an instance.
– The Name Learner – makes predictions using the full name of the input instance, instead
of its content.
– The Meta Learner – combines predictions of individual based learner via a weighted
sum.
Results of Distribution Estimator are passed to Similarity Estimator, which applies a usersupplied
similarity function to compute a similarity for each pair of concepts
〈A ∈ O 1 ,B ∈ O 2 〉 .
The Relaxation Labeler takes the similarity matrix and domain-specific constraints and heuristics
to find the best mapping configuration.
GLUE was evaluated on several real-world domains and proved to accurately match
66-97 % of the nodes. The disadvantage of the approach is that it operates upon taxonomies,
which are only a subset of ontologies.
Mafra MAFRA [43] stands for Ontology MAapping FRAmework for distributed ontologies
in the Semantic Web. The framework consists of five horizontal modules describing
the fundamental phases of a mapping process. Four vertical components run along the entire
mapping process, interacting with horizontal modules.
The horizontal dimension consists of following modules:
– Lift & Normalization – assures that all data to be mapped are at the same representation
level. It copes with with syntactical, structural and language heterogeneity.
Elimination of syntax differences makes semantics differences between the source and
the target ontology more apparent.
– Similarity – establishes similarities between entities from the source and target ontology,
thus, it supports mapping discovery. MAFRA adopted a multi-strategy process
that calculates similarities between ontology entities using different algorithms (lexical
similarity, property similarity, bottom-up similarity and top-down similarity [43]).