WebIsAGraph: A Very Large Hypernymy Graph from a Web Corpus
Authors: Stefano Faralli, Irene Finocchi, Simone Paolo Ponzetto, Paola Velardi (Università di Roma Unitelma Sapienza, Sapienza Università di Roma, University of Mannheim, 2019)
Topic: Lexical and Semantic Resources and Analysis

Abstract
In this paper, we present WebIsAGraph, a very large hypernymy graph compiled from a dataset of is-a relationships extracted from the CommonCrawl. We provide the resource together with a Neo4j plugin to enable efficient searching and querying over such large graph. We use WebIsAGraph to study the problem of detecting polysemous terms in a noisy terminological knowledge graph, thus quantifying the degree of polysemy of terms found in is-a extractions from Web text.

Introduction
Acquiring concept hierarchies, i.e., taxonomies from text, is a long-standing problem in Natural Language Processing (NLP). Much previous work leveraged lexico-syntactic patterns, which can be either manually defined (Hearst, 1992) or automatically learned (Shwartz et al., 2016). Pattern-based methods were shown by (Roller et al., 2018) to outperform distributional methods, and can be complemented with state-of-the-art meaning representations such as hyperbolic embeddings (Nickel and Kiela, 2017) to infer missing is-a relations and filter wrong extractions (Le et al., 2019). Complementary to these efforts, researchers looked at ways to scale hypernymy detection to very large, i.e., Web-scale corpora (Wu et al., 2012). Recently, (Seitner et al., 2016) applied Hearst patterns to the CommonCrawl1 to produce the WebIsaDb. Using Web corpora makes it possible to produce hundreds of millions of isa triples: the extractions, however, include many false positives and cycles (Ristoski et al., 2017). Methods for hypernym detection like, e.g., pattern-based approaches, have a limitation in that they do not necessarily produce proper taxonomies (Camacho-Collados, 2017): automatically detected is-a relationships, on the other hand, can be used as input to taxonomy induction algorithms (Velardi et al., 2013; Faralli et al., 2017; Faralli et al., 2018, inter alia). These algorithms rely on the topology of the input graph, and, therefore, cannot be applied ‘as-is’ to Web-scale resources like WebIsaDb, since this resource merely consists of a set of triples. Moreover, WebIsADb does not contain fully semantified triples, i.e., subjects and objects of the is-a relationships consist of potentially ambiguous terminological nodes. This is because, due to their large size, source input corpora like the CommonCrawl cannot be semantified upfront. Linking to the semantic vocabulary of a reference resource like DBpedia (Hertling and Paulheim, 2017) also barely mitigate this problem, since Wikipedia-centric knowledge bases have not, and cannot be expected to have, complete coverage over Web data (Lin et al., 2012). In this paper, we present an initial solution to these problems by building the first very large hypernymy graph, dubbed WebIsAGraph, built from is-a relationships extracted from a Web-scale corpus. This is a relevant task: although WordNet (and other thesauri) already provides a catalog of ambiguous terms, many nodes of WebIsAGraph are not covered in available lexicographic resources, because they are proper names, technical terms, or polysemantic words. Our graph – which we make freely available to the research community to foster further work on Web-scale knowledge acquisition – is built from the WebIsADb on top of state-of-the-art graph mining tools2: thanks to an accompanying plugin, it can be easily searched, queried, and explored. WebIsAGraph may represent an opportunity to researchers for investigating approaches to a variety of tasks on large automatically acquired term tuples. As an example, we use our resource to investigate the problem of identifying ambiguous terminological nodes. To automatically detect whether a lexicographic node is ambiguous or not, we use information from both the graph (topological features) and textual labels (word embeddings) as features to train a model using supervised learning. Our results provide a first estimate of the degree of polysemy that can be found among is-a relationships from the Web.

Conclusion
The main contribution of this work is the creation of a new resource by converting a large dataset of is-a (hypernymy) relations, automatically extracted from the Web (such as WebIsADb), into a graph structure. This graph, along with its accompanying search tools, enables both descriptive and predictive analytics of emerging properties of terminological nodes. In this paper, we used our new resource to investigate whether a node's polysemy can be predicted from its topological features (i.e., connectivity patterns) and textual features (meaning representations derived from word embeddings). The results of this preliminary study suggest that textual features are strong predictors of polysemy, while topological features appear to be weaker predictors, although they do show a significant correlation with the polysemy of the related node.