## 2 Ontology Alignment Techniques
### Alignment Approaches  
Simple symbolic (or string-based) methods rely only on a concept's name (label) to compute the similarity between a pair of concepts. Strings are normalized (case folding, use of a standardized encoding, blank normalization, _etc._) and compared syntactically. The comparison may be either exact, _i.e._, concepts are matched only when the strings are equal, or approximate, where a confidence value is computed using a string of similarity metrics. Techniques for comparing a pair of strings are, for example, prefix/suffix comparison, edit distance (the number of changes required to transform one string into another), soundex index (based on pronunciation similarity in English), and n-grams (ratio between equal and all n-character sub-sequences). While approximate string matching allows for the successful matching of concepts--even when the strings are not equal--a pure string matching approach has obvious limitations. For example, equivalent concepts described by different terms (synonyms) cannot be detected, while different concepts described by equal terms (homonyms) will mistakenly be detected as a complete match. Also, string-based matching techniques perform poorly when comparing complex strings, such as phrases, sentences, or descriptions. Systems using string based comparison for matching of concepts include COMA [6] and COMA++ [7], OLA [8], Anchor-Prompt [9], S-Match [10], and others. Methods using language-based text analysis introduce additional techniques capable of improving on some limitations of the previous category. These include tokenization, elimination of stopwords (articles, prepositions, conjunctions), and the performing of morphological analysis for the reduction of each term (token) to its basic or stem form. Resulting terms belonging to one concept are compared to  
Figure 1: Alignment of two ontologies, mappings between related concepts, are shown in red. terms belonging to other concepts using string-based matching. The confidence of the matching can be computed as the ratio between the number of matching terms and the total number of terms describing both concepts. While an improvement on the simple string comparison, this approach does not match concepts on a semantic level, and will fail when, for example, correct handling of synonyms or homonyms is required. Examples of systems using language-based text analysis include COMA [6] and COMA\(++\)[7], OLA [8], S-Match [10], Cupid [11], _etc._  
The employment of linguistic resources in the matching process introduces matching on a semantic level, as opposed to matching on a syntactic level. Linguistic resources used in the matching discovery process include, for example, domain specific thesauri or WordNet ([[http://wordnet.princeton.edu](http://wordnet.princeton.edu)]([http://wordnet.princeton.edu](http://wordnet.princeton.edu))) - a lexical database for the English language, which includes a thesaurus and a dictionary. Lexical relationships, such as synonyms, antonyms, hyponyms, or hypernyms can be exploited, which not only improves matching quality, but also allows for the establishment of the type of relationship, such as equivalence or generalization. The structure of the linguistic resource can be used to compute the similarity between two terms, for example, by measuring the distance between the words in the linguistic data structure (which is typically a hierarchy or a graph). The main difficulty with this approach is that domain specific thesauri will be required for specialized application domains. Also, thesauri for languages other than English are often of lesser quality or not available at all. Examples of a system employing linguistic resources are OLA [8], Cupid [11], COMA [6], and others.