D3.2v1 Design, implementation and deployment of text mining and enrichment services Phase 1
Creators: name: Ortega, Raul
name: Garcia-Silva, Andres
name: Gomez-Perez, Jose Manuel
Publisher: name: Zenodo
Year: 2022
Subjects: RELIANCE
Research Objects
ROhub
RO-Crate
Data cubes
EOSC

description: This deliverable reports the progress in the design and development of the first version of the text mining and enrichment services for Research Objects and scientific documents in RELIANCE. In the early stage of the project, we gathered a corpus of scientific documents in the domains of interest for RELIANCE user communities that was used to customize the knowledge graph underlying the text mining and enrichment services. Once the knowledge graph was adapted to the scientific vocabulary used by RELIANCE users, we were ready to start the development of the text mining and enrichment services. Among such services is the semantic enrichment service of Research Objects and Scientific documents. The enrichment service processes the text from documents or Research objects and yields semantic metadata describing the text content. The semantic metadata is a synthesis of the text and includes concepts, lemmas, multi-word expressions, and topics. In addition, we are working on the generation of a Field of Research metadata that we plan to integrate in the enrichment service. To generate this type of metadata, we trained a classifier by fine-tuning a pre-trained RoBERTa language model on the corpus that we gathered for RELIANCE users where scientific papers are tagged with Field of Research categories (e.g., Geology, Oceanography, or Atmospheric Sciences). The enrichment service for documents is already onboarded in EOSC as a RESTful service, and the service that enriches Research Objects is integrated in ROHub. Next, we developed information retrieval tools exploiting the semantic metadata added to Research Objects along with their text content. First, a faceted search engine where users can search the Research Objects collection in ROHub using the traditional keywords, but also facets for each of the types of the semantic metadata. A faceted search engine allows complex queries and retrieving Research Objects more precisely. Second, a recommendation engine that suggests potentially relevant research objects based on the content of a selection of other research objects or users. If the reference is a research object, the recommendation engine uses the semantic metadata of the research object to recommend similar research objects. If the reference is a user, the recommendation engine first aggregates all the semantic metadata of the research objects owned by such user and then uses the semantic metadata to suggest relevant research objects. The search engine and the recommendation system are being onboarded in EOSC as RESTful services. During this stage of the project, we lay the foundations for the development in the next phase of the extended set of analytics services where we plan to address the Influence Network Extraction, the Novelty Score for Research Objects, Support to Reading Comprehension, and Text Mining and Enrichment Dashboard. While the design and development of such services will be covered in depth in the second version of this deliverable, in this version we have included a brief description of each of them. In the second version of this deliverable, we plan to increase the metadata produced by the enrichment service including titles, abstracts, authors, citations, and data cube references. Finally, in the second phase of the project we will work on the user interfaces for the recommendation system, the search engine, the visualization of the semantic enrichment results for research objects, and a prototype of a browser plugin that allows scientists to ingest scientific publications into their bibliographic research objects as they browse the internet searching for relevant literature for their research.
description: This is the draft version of the deliverable not yet approved by the European Commission.