Hierarchical Term Clustering - Ontology learning from semi-structured Web documents

4.4 Conclusion

5.1.1 Hierarchical Term Clustering

As already described in chapter 4, a clustering for sibling relations can be conducted as a tagpath clustering where labelled clusters are obtained or as a term clustering, where the terms are constituting clusters. The best results regarding sibling relations have been achieved by means of term clustering. Our first experiment is, therefore, to apply agglomerative hierarchical clustering and Bi-Secting-K-Means for term clustering. This means that terms represented by vector of occurrence in sibling sets are clustered, yielding a binary tree where terms are finally the leafs of the tree. We also apply Bi-Secting-K-Means in a manner to produce a complete hierarchy, without stating a (not even roughly) known number of clusters in advance. Bi-Secting-K-Means can be applied to yield a fixed number of clusters. But in our scenario the number of clusters is not even roughly known nor do we know which is the suited strategy to decide for the clusters to be split until the desired number of clusters is achieved. By producing a complete hierarchy we avoid the need to decide for a K and for a strategy to choose the next cluster to split.

For the obtained hierarchies of clusters it is not possible to measure the achieved quality according to FMASO. The clusters are not partitions depicting sibling

5.1 Hierarchical clustering for Sibling Groups Hierarchies

groups in such a way that one set of sibling groups can be compared to the reference set of sibling groups. This could only be done by incorporating more

“heuristics” such as where the hierarchy is to be cut so that groups of siblings can be made explicit for agglomerative hierarchical clustering and for using Bi-Secting-K-Means with a fixed number of K cluster to be produced. But in both cases the hierarchical characteristic would get lost.

Next we will have a brief look at the results obtained while performing hierarchical term clustering. Figure 5.1 and 5.2 shows the resulting cluster hierarchies for agglomerative hierarchical clustering and Secting-K-Means clustering. Figure 5.3 shows a fraction of the hierarchy produced by Bi-Secting-K-Means clustering where details can be observed. We applied hierarchical agglomerative clustering with the Unweighted Pair Group Method with Arithmetic Mean - UGPMA [Jain and Dubes, 1988] heuristic which is supposed to prevent the creation of chains of clusters for the sake of a relatively high complexity of O(n²logn). But for the relatively smaller number of terms to be clustered this complexity is acceptable. The hierarchical agglomerative clustering shows only a relatively small chaining effect, whereas the Bi-Secting-K-Means clustering reveals that chaining is a more serious issue.

While one manually inspects that hierarchy, the disadvantage of the two exclusive clustering approaches applied as term clustering become apparent again. In an exclusive clustering approach such as K-Means (and hierarchical agglomerative clustering and Bi-Secting-K-Means) a membership in clusters is exclusive: an instance (term) can belong to only one cluster. This disadvantage was already pointed out while K-Means was applied for term clustering. But since the quality of the clustering cannot be measured according to the FMASO, we cannot quantify to which extent the clusterings represent siblings which are in accordance with the reference ontologies regarding the sibling relations. The rough impression is that a term clustering with exclusive cluster membership is too restrictive regarding providing sufficient suggestions of plausible siblings to the ontology engineer.

Concepts can have many siblings which are plausible and if one recognizes an error where the siblings depicted by the cluster hierarchy are not plausible, then one has no alterative suggestion. A term clustering which yields only a small number of information to be inspected by this cannot compensate the disadvantage of the limited number of siblings which can be observed for a concept. The idea is to perform hierarchical clustering as tagpath clustering. In a tagpath clustering, clusters need to be labelled and, therefore, the terms/features/concepts that take place constitute more than one sibling constellation. The circumstance that tagpath clustering is here more appealing is also the reason why we did not further investigate whether Bi-Secting-K-Means can be applied to produce a fixed number of clusters which could thus be compared. The disadvantage of exclusive clustering would still be prevalent.

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