## 2 Ontology Alignment Techniques
### Alignment Approaches
Constraint-based methods do not rely on textual descriptions, but exploit other information directly associated to the concept, such as the data types (integer, float, string, date, _etc._) of key properties, data type similarities (e.g., float and double are both real number representations), permitted value ranges of the attributes, _etc._ OLA [8] and COMA [6] are examples of system that use this kind of information for matching. Structure-based alignment methods differ from methods discussed above by not only considering a single concept at a time, but by utilizing the ontology structure information to compute the mappings. The fact that ontologies can be treated as graphs allows one to compare the sub-graphs belonging to different concepts using graph matching methods. For example, two concepts having similar child (or leaf) sets should be matched, while the confidence can be expressed as the ratio of equal children (or leaves). Taxonomy structure of the class hierarchy can also be considered, for example, by considering the ratio of mutual super-concepts. Similarity flooding [12], a technique based on the idea that similar nodes indicate the similarity of their neighbors, iteratively propagates similarity along the graph structure. Ontological structure is used by numerous alignment systems, such as Cupid [11], Anchor-Prompt [9], COMA [6], OLA [8], QOM [13], RiMOM [14], and many others. Methods based on reasoning reduce the graph matching problem to pairwise node matching problems solved through the validation of a logical formula using an SAT solver. Examples of systems using this classical AI approach are CtxMatch [15] and S-Match [10]. External knowledge can be used for alignment. For example, upper (or reference, global, top-level) ontologies, such as DOLCE [16], have been designed with integration in mind. They provide reference terminology by defining general concepts, which can be used across different domains. Alignment reuse is a technique which, given the existing alignments between Ontologies O and O1, and between O and O2, uses this information to match O1 and O2. Systems using this approach are, for example, COMA\(++\)[7] and OLA [8]. Alignment based on machine learning methods makes use of the statistical distribution of features that are used to describe a concept. Features are usually extracted from a concept's textual description, but may also include structural information and can even be extended using external resources, such as thesauri. When many different features and features of different types (symbolic, semantic, and structural) are used to describe concepts, computation of concept similarity becomes a non-trivial problem. Both supervised and non-supervised machine learning methods, using various similarity metrics, can be applied on the high-dimensional feature spaces to discover the matchings. Examples of systems based on the machine learning approach include GLUE [17], RiMOM [14], and others. Composite alignment methods are combinations of the methods described above. They are commonly used by well performing systems. Since different alignment methods operate on different information types (labels, text descriptions, structure, rules, _etc._) they use different similarity coefficients, which must be aggregated into a single composite coefficient. The main difficulty connected with this is that a composite method may undermine a very good single strategy. Therefore, composite methods typically include strategies to decide which alignment methods should be used and how their results should be combined (weighted). A simple example of such a strategy would be to assess the vocabulary similarity and the structural similarity of the ontology pair that will be aligned and, depending on these measures, decide whether to apply a string-based or a structure-based alignment algorithm. Examples of systems using composite alignment methods are Cupid [11], OLA [8], QOM [13], RiMOM [14], and many others.