Enhancing semantic search through the automatic construction of a Biodiversity Terminological Inventory

NHUNG T.H. NGUYEN; GEORGIOS KONTONATSIOS; AXEL J. SOTO; RIZA BATISTA-NAVARRO; SOPHIA ANANIADOU

Santa Clara

Simposio; TDWG 2016 Symposium on Semantics for Biodiversity Science; 2016

Biodiversity Information Standards

The increasing growth of literature in biodiversity presents challenges to users who need to discover pertinent information in an efficient and timely manner. In response, text mining techniques offer solutions by facilitating the automated discovery of knowledge from large textual data. An important step in text mining is the recognition of concepts via their linguistic realisation, i.e., terms. However, a given concept may be referred to in text using various synonyms or term variants, making search systems likely to overlook documents mentioning less known variants, which are albeit relevant to a query term. Domain-specific terminological resources which include term variants, synonyms and related terms, are thus important in supporting semantic search over large textual archives. We describe the use of text mining methods for the automatic construction of a large-scale biodiversity term inventory. The inventory consists of names of species, amongst which naming variations are prevalent. We apply a number of distributional semantic techniques on all of the documents in the Biodiversity Heritage Library, to compute semantic similarity between terms and support the automated construction of the resource.With the construction of our biodiversity term inventory, we demonstrate that distributional semantic models are able to identify semantically similar terms that are not yet recorded in existing taxonomies. Such methods can thus be used to update existing taxonomies semi-automatically by deriving semantically related terms from a text corpus and allowing expert curators to validate them. We propose our inventory as a resource that enables automatic query expansion, which in turn facilitates improved semantic search. Specifically, we developed a visual search interface that suggests semantically related terms available in our inventory but not in other repositories, to incorporate into the search query. An assessment of the interface by domain experts reveals that query expansion based on related terms is useful for increasing the number of relevant documents retrieved. Its exploitation can benefit both users and developers of search engines and text mining applications.