Data-driven Coreference-based Ontology Building (2024)

Abstract: While coreference resolution is traditionally used as a component in individual document understanding, in this work we take a more global view and explore what can we learn about a domain from the set of all document-level coreference relations that are present in a large corpus. We derive coreference chains from a corpus of 30 million biomedical abstracts and construct a graph based on the string phrases within these chains, establishing connections between phrases if they co-occur within the same coreference chain. We then use the graph structure and the betweeness centrality measure to distinguish between edges denoting hierarchy, identity and noise, assign directionality to edges denoting hierarchy, and split nodes (strings) that correspond to multiple distinct concepts. The result is a rich, data-driven ontology over concepts in the biomedical domain, parts of which overlaps significantly with human-authored ontologies. We release the coreference chains and resulting ontology under a creative-commons license.

## introduction ontologies categorize concepts into groups and arranges them in a hierarchy and are essential for researchers in the biomedical domains @xcite @xcite , as evidenced by the vast number of ontologies available in repositories such as bioportal 3 . these ontologies are predominately human curated, they each contains a collection of concepts arranged in a hierarchy, and for each concept a list of aliases, which are different equivalent names for this concept. while useful, such ontologies 1 https://huggingface.co/spaces/biu-nlp/ data-driven_coreference-based_ontology 2 https://github.com/shirapp/ coreference-based-ontology-building 3 https://bioportal.bioontology.org/ have deficiencies: being manually curated they are both expensive to create and maintain and also noncomprehensive; they do not cover all areas of interest a researcher may be interested in, especially for long-tail interests (for example, bioportal does not contain an ontology containing a comprehensive list of peptides); and the concept names and their aliases may not be aligned with how the concepts appear in text, reducing their utility for text mining applications @xcite (for example, the umls ontology entry for "alzheimer's disease" does not contain the string "alzheimer", although it is a very common way to refer to this condition in text). thus, a data-driven, text-based ontology derived directly from the scientific literature can be of immense value: (a) it will provide coverage of all (or most) the concepts that appear in the text, including long-tail ones, arranged in hierarchies based on their actual use in scientific texts; (b) concepts names and aliases will be naturally aligned with their text appearances; and (c) they can aid manual creation, extension and maintenance of existing ontologies by surfacing areas of deficiencies in coverage, and suggesting alternative hierarchies and potentially missing entries. in this work we propose to create such a datadriven ontology from text, using a novel signal: the topology of a graph created by running indocument coreference resolution over scientific documents, and creating a graph where nodes are textual strings and edges represent that the two strings participated in a coreference chain. we show that his graph's topology contains rich information which allows to identify concepts, aliases and hierarchies like the ones in the figure 1 . we exploit the dynamics of phrase co-occurrence within the graph, observing a correlation between a phrase's contribution to information flow and its level of generality. therefore, our approach centers on a single centrality measure, specifically betweenness centrality @xcite , aimed at understanding information flow. this measure guides the transformation of the graph into a directed structure, establishing the framework for ontology construction. ## evaluation and results evaluating the quality of the resulting graph is challenging, as there is no ground-truth to compare to @xcite . still, we compare our results to existing human curated ontologies in the biomedical domain (umls @xcite and snomedct @xcite ), and assess how well we manage to capture concepts from them. umls provides aliases for identity nodes, while snomedct provides hierarchical relations and directions between concepts. if these resources were perfect, we wouldn't need to create the data-driven one to begin with. we thus combine automatic metrics with human evaluations. ## related work ontology learning, essential for many applications, has traditionally relied on linguistic approaches, using pattern extraction and syntactic analysis combined with statistical methods. while syntactic patterns can be effective, they often require human intervention. a well-known example is @xcite , which designed a process to extract patterns for specific relations from phrase pairs. some works apply syntactic analysis by parsing dependency trees to extract relations and build ontologies automatically, as seen in @xcite . statistical methods like @xcite use hierarchical clustering for taxonomy building and conditional probabilities for non-taxonomic relations, while @xcite applies conceptual clustering to automatically acquire and organize subcategorization frames. these techniques differ from ours by focusing on local relations, while we make use of global signals-cross-document-level coreference relations-to identify relationships more globally within the corpus. recent studies have explored using large language models (llms) for ontology building due to their ability to capture contextual information @xcite @xcite . for example, @xcite applied a zero-shot prompting approach to term typing, taxonomy discovery, and relation extraction, while @xcite used ordered prompts to construct concept hierarchies. both studies show that while llms can aid ontology learning, they cannot yet construct ontologies independently. ## conclusions we demonstrated that a text-based, data-driven biomedical ontology foot_3 can be created by considering the topology of a coreference graph obtained from a large corpus. furthermore, we achieved this primarily through the use of a single centrality measure. a significant contribution of this approach is its generality, allowing for easy adaptation to other fields. additionally, our method is scalable and can be implemented for networks of varying sizes. compared to existing ontologies, we obtain very accurate directionality and high recall of hierarchical structure. we also find accurate hierarchical relations that are not reflected in the human-curated ontologies. in future work, our automatically constructed ontology could be applied to downstream tasks.