D3.3 Design, implementation and deployment of text mining and enrichment services Phase 2
Creators: name: Ortega, Raul
name: Garcia-Silva, Andres
name: Berrío, Cristian
name: Gomez-Perez, Jose Manuel
Publisher: name: Zenodo
Year: 2022
Subjects: RELIANCE
Research Objects
ROhub
RO-Crate
Data cubes
EOSC
OpenAire

description: This deliverable reports the work carried out during the second year of the project within work package 3. During this period the goal was increasing the robustness of the services deployed during the first year and extending the currently available text analytics and understanding services with the development of the Extended Analytic Services described in task T3.4. In the first year we deployed in EOSC an enrichment service to generate structured metadata about the content of a research object or a document including the main concepts and phrases, plus the entities and their type (e.g., people, location, organization), and topical information from domains according to the Expert.ai linguistic knowledge graph. To demonstrate the enrichment service capabilities, we published an enrichment web application.Most of the metadata produced by the first version of the enrichment service aims to increase the research object findability by providing structured information describing its content. We leveraged such metadata to feed a semantic search engine and a recommendation service for research objects that we also deployed in EOSC. The front-end of the recommendation service is the Collaboration Spheres web application where users can drag and drop research objects or their authors to the recommendation context and receive a recommendation of research objects based on that content. In the second year of Reliance, we move on from services supporting research object findability to services supporting research object comprehension and termed such services as the Extended Analytic Services. Here the goal is to assist researchers in the comprehension of the research work represented by a research object and how such research relates to other work in the scientific community, including other research objects and scientific publications. The new services include: i) the challenge and solution extraction<strong> </strong>service to extract the scientific challenges addressed by the work described in a research object and the corresponding solution or research proposal as long as this information is contained in the research object description, ii) the Question Generation<strong> </strong>service that produces questions from a research object description and highlights in the text the answer to such questions, iii) the claim analysis<strong> </strong>service, which identifies scientific claims in research objects and associates them with existing claims in the scientific literature, and iv) the novelty scoring service that assigns a numeric value between 0 (non-novel) and 1 (novel) based on the assumption that the novelty of the research proposed in a research object is inversely proportional to the amount of existing similar work. The extended analytics services require advanced text processing capabilities at the edge of the State of the Art in natural language processing and understanding, including machine-reading comprehension, natural language generation, text classification and semantic textual similarity. Even though we use state-of-the-art language models fine-tuned for the underlying task addressed by each service, the extended analytics services need to be considered as experimental since such tasks are still open research problems in NLP/U. To emphasize the maturity level difference between the production-ready enrichment and the new set of Extended Analytic services we deployed the latter as separate web services6. In addition, we enhance the enrichment service by increasing the types and formats of the documents it supports. We add support to Jupyter Notebooks and GitHub repositories. We also add new metadata such as lemmas, main sentences, categories from taxonomies including Fields of Research, NASA Scope and Subject, and IPTC, as well as entity types like Data Cubes identifiers, and time references. Finally, we have designed RELISH<strong> </strong>(RELIance DaSHboard), a dashboard where ROHub administrators can visualize the metadata extracted by the text mining services to have a high-level overview of the D3.3 Design, implementation and deployment of text mining and enrichment services – Phase 2 content of the research objects hosted in the platform. The dashboard is interactive and provides different widgets to visualize the aggregated information of the research object collection. We include charts to show the distribution of research objects according to the most representative metadata, including the categories of the taxonomies we use to classify them and tag-cloud visualizations among others to show concepts, lemmas, phrases, and entities.
description: This is the draft version of the deliverable not yet approved by the European Commission.