A semantic engine for grey literature retrieval in the oceanography domain

Here we present the final results of MAPS (Marine Planning and Service Platform), an environment designed for gathering, classifying, managing and accessing marine scientific literature and data, making it available for search to Operative Oceanography researchers of various institutions by means of standard protocols. In previous publications the general architecture of the system as well as the set of metadata (Common Data Index) used to describe the documents were presented [3]; it was shown how individual oceanographic data-sets could be indexed within the MAPS library by types of measure, measurement tools, geographic areas, and also linked to specific textual documentation. Documentation is described using the current international standards: Title, Authors, Publisher, Language, Date of publication, Body/Institution, Abstract, etc.; serial publications are described in terms of ISSN, while books are assigned ISBN; content of various types on electronic networks is described by means of doi and url. Each description is linked to the document. Thanks to this, the MAPS library already enables researchers to go from structured oceanographic data to documents describing it. But this was not enough: documents may contain important information that has not been encoded in the metadata. Thus an advanced Search Engine was put in place that uses semantic-conceptual technologies in order to extract key concepts from unstructured text such as technical documents (reports and grey literature) and scientific papers and to make them indexable and searchable by the end user in the same way as the structured data (such as oceanographic observations and metadata) is. More specifically once a document is uploaded in the MAPS library, key domain concepts in documents are extracted via a natural language processing pipeline and used as additional information for its indexing. The key term identification algorithm is based on marine concepts that were pre-defined in a domain ontology, but crucially it also allows for the discovery of new related concepts. So for instance starting from the domain term salinity, related terms such as sea salinity and average sea salinity will also be identified as key terms and used for indexing and searching documents. A hybrid search system is then put in place, where users can search the library by metadata or by free text queries. In the latter case, the NLP pipeline performs an analysis of the text of the query, and when key concepts are matched, the relevant documents are presented. The results may be later refined by using other structured information (e.g. date of publication, area, ...). Currently a running system has been put in place, with data from satellites, buoys and sea stations; such data is documented and searchable by its relevant metadata and documentation. Results of quantitative evaluation in terms of information retrieval measures will be presented in the poster; more specifically, given an evaluation set defined by domain experts and composed of pre-defined queries together with documents that answer such queries, it will be shown how the system is highly accurate in retrieving the correct documents from the library. Though this work focuses on oceanography, its results may be easily extended to other domains; more generally, the possibility of enhancing the visibility and accessibility of grey literature via its connection to the data it describes and to an advanced full text indexing are of great relevance for the topic of this conference.