# Graph-Based Taxonomic Semantic Class Labeling
## 1 Introduction
From the perspective of lexical semantics, syntactic dependencies can help in disambiguating words and phrases, as we have shown in our previous work on ConGraCNet, making it easier to determine the semantic potential of a word and its categorical structures. Although similar in function, knowledge databases and ontologies are a type of knowledge representation that contains top-down organized logical descriptions of the concepts and relationships in a given domain. Ontologies are typically used in the semantic web to provide a standardized way of representing and reasoning about the meaning of concepts and relationships. However, they do not represent the synchronic or diachronic dynamic knowledge description extracted from the usage. Thus, while ontologies can be used to infer the labeling of concepts and sense communities, syntactic dependency graphs may be more effective for analyzing dynamic, cross-cultural knowledge. In this paper, we introduce a sequential lexical graph analysis procedure that is employed both for (a) extracting the most fitting conceptual generalizations of a group of related lexemes and for (b) representing the taxonomic label for a semantic class of a given lexeme. In particular, the proposed method exploits the syntactic-semantic dependency relations coordination _and_ _or_ and categorization _is_a_ and projects their network structure into conceptual similarity and taxonomic relation embedding graph layers, respectively. The proposed approach capitalizes on syntactic-semantic dependency relations and projects them into graph layers to achieve its desired outcomes. The ConGraCNet coordination-based graph method for semantic association analysis [5] underlying the labeling task, reveals the polysemous nature of lexical concepts by using coordination _and/or_ syntactic construction. The obtained graph of associative lexemes for a source lexeme is clustered into subgraphs that reveal the semantic classes of the source lexeme in a corpus. Building from this informational structure of lexical subgraphs, two additional graphs are constructed to capture the taxonomic relations of the subgraph semantic domains: one based on the collocates of the _is_a_ dependency relation and the other based on the hypernym relation from available commonsense knowledge bases such as Wordnet [6]. Finally, graph analysis is performed to identify a set of corresponding label candidates representing the abstract semantic features of the associated lexemes in the semantic class. The proposed ConGraCNet multilayered method is an effective way of semantic analysis that reveals the polysemous nature of lexical concepts and provides a cognitive linguistic approach to understanding the semantic meanings of lexical concepts and their taxonomic relations. We present examples from different corpora and describe the construction of lexical graphs and the process of candidate selection based on collocation and centrality measures, particularly the Semi-Local Integration (\(SLI\)) centrality [7]. The accompanying ConGraCNet web app [8] integrates manually annotated lexicons and semi-automatic corpus resources and methods. The app is available as a semantic resource in the EmoCNet project [9]. The aim of this paper is to (i) present a graph-based method for labeling the taxonomic semantic class of associative concepts; (ii) present a semantic resource for web applications that integrates manually annotated lexicons and semi-automatic corpus techniques. We make the following contributions:  
* A graph-based approach to the lexical task of labeling semantic classes, which are formed as associative lexical communities related to a polysemous lexeme, using a combination of coordination and _is_a_ syntactic-semantic lexical relations;
* A related graph-based method that incorporates the knowledge base with a built-in hierarchical structure, such as the WordNet hypernym relation, to assign hypernym labels to semantic classes;
* A web app implementation of the graph-based labeling algorithm.