# Graph-Based Taxonomic Semantic Class Labeling
## 3 Graph-Based Taxonomic Semantic Associative Community Labeling
### Dependency-Based Taxonomic Semantic Class Labeling
#### 3.2.2 Dependency-Based Labeling: Implementation  
The above procedure of labeling is implemented in ConGraCNet web app [8] with the following default parameters: We set the default value to \(k=50\) and \(frequency\) as the default corpus collocation measure. The first two labels are selected. Only lexemes that occur in the FoF\([and|or]_{s}\) graph are taken as label candidates. The second-degree coordination-based graph FoF\([and|or]_{s}\) is constructed using the default settings: enTenTen13 corpus, n = 15 best-ranked coordination collocations in the first- and second-degree networks, and with pruning removing nodes with degree less than 2 and with betweenness centrality in the bottom 50%. Clustering is performed using the Leiden community algorithm [54; 55] with \(mvp\) partition type clustering. Graph calculations are performed using the Python library iGraph [55]. For text input, we used the pipeline that collects syntactic dependency data from the morpho-syntactically tagged corpora Sketch Engine API [56; 57]. The Sketch Engine API was used to extract a summary of various syntactic dependency co-occurrence data for each lemma. To illustrate dependency-based taxonomic semantic class labeling, consider the example analysis of the lexeme _ethics_. The label graph of the source lexeme _ethics_-_n_\(F[is_{a}]_{i}^{ethics}\)  
Figure 2: Schematic illustration of the taxonomic semantic class labeling propagation procedure associated with the coordination lexical graph. constructed using the large morpho-syntactically tagged English corpus enTenTen13, shows five associated lexical communities FoF\([and|or]_{i}^{ethics}\), as illustrated in Figure 1.  
The source lexeme _ethics_ is clustered in the third semantic community FoF\([and|or]_{3}^{ethics}\): _ethics-n_, _conduct-n_, _theology-n_, _compliance-n_, _philosophy-n_, _ethic-n_, _psychology-n_. The first cluster of associated lexemes FoF\([and|or]_{1}^{ethics}\) refers to the semantic class of famous philosophers. The corresponding _is_a_ dependency label graph \(F[is\_a]_{1}^{ethics}\) is shown in Figure 3.  
The label graph \(F[is\_a]_{3}^{ethics}\) is populated with lexemes that are _is_a_ collocates of lexemes from the third community, e.g., _plato_, _Aristotle_, _aristotle_, _theist-n_, _atheist-n_, _nietzche-n_ all point to the lexeme _philosopher-n_. The leaf nodes in the graph are populated with lexemes from diverse conceptual domains, e.g., _priest_, _grass_, _town_, _year_, _friend_. These lexemes will have less impact on the label importance score. For simplicity, in the illustration in Figure 3, neither the weights of the edges representing _is_a_ relation strength nor the out node \(SLI\) measure are displayed. However, the out nodes that are more important according to the \(SLI\) scores will project more priority to their collocates in the label selection summarization. Since, for example, the lexeme node _Aristotle_ has the highest \(SLI\) score among all the nodes in the corresponding community, its collocates will gain high priority as label candidates. Among them, some will gain additional weight from other important lexeme nodes and high weight from their respective edges. Since the labeling procedure is based on the presence of categorical relations in a corpus, if a lexeme from the semantic class does not have the relation _is_a_, the node can be omitted during processing. For example: _cicer-n_, _galen-n_, _euclid-n_, _pythagoras-n_, _conticus-n_, and _stotics-n_ are not represented in the label graph. All identified graph communities representing semantic classes related to the lexeme _ethics_ and the corresponding labels are listed in Table 1. Each of the associated communities representing semantic fields of a source lexeme _ethics-n_ is abstracted with two taxonomic labels.