Semantico – Toolchain for literature classification
Creators: name: Koubaa, Mohamed Anis
name: Süß, Wolfgang
name: Stucky, Karl-Uwe
name: Schmidt, Andreas
Publisher: name: Zenodo
Year: 2023
Subjects: Ontology
Literature classification
Ontology learning

description: Semantico - A toolchain to find and analyse relevant literature. The important increase in efficiency of perovskite-based solar cells (PSCs) in the last decade<br> is a result of scientific work, which produced a huge quantity of literature and data-sets (between<br> 2014 and 2022 almost 30,000 research articles were published). The aim of this work is to elaborate<br> an ontology which can primarly be used to classify literature paragraphs according to the subject<br> discussed within. Furthermore, the annotation of data-sets using terms of the envisaged ontology<br> can, also, help to better structure them, making them more understandable, interoperable, and<br> reusable.<br> The approach is to firstly develop an ontology with a team of domain experts and then elaborate<br> concepts for knowledge extraction from literature based on it.<br> From a technical point of view an ontology is a representational artifact, comprising a taxonomy<br> as proper part, whose representations are intended to designate some combinations of universals,<br> defined classes, and certain relations between them. A taxonomy on its role is a hierarchy definition<br> between terms denoting types (or classes) linked by subtype relations.<br> The knowledge extraction process consists of the following steps: (1) acquiring relevant text<br> resources, (2) processing the text into individual terms (also known as tokenization), (3) docu-<br> ment segmentation and paragraph classification, (4) recognizing tokens as classes of information,<br> (5) entity relation extraction, and (6) named entity linking. The ontology being developed will<br> facilitate some of the tasks in the knowledge extraction process (mainly entity recognition and<br> disambiguation (4) and predicate determination (to build (subject, predicate, object)-triplets) by<br> using link prediction during realization of (5)) by strengthening the context to an ontological com-<br> mitment and by making the correspondence between sign, concept, and thing (or symbol, thought<br> or reference and referent) more robust, especially when named entities and their corresponding<br> classes in the ontology should be precisely recognised.<br> On the other hand both metadata and content of several scientific articles are represented as<br> RDF triplets. Metadata of newly published scholarly papers are almost structured and based on<br> unique identifiers (such as ORCID or ROR), which make the task of retrieving them much easier.<br> But the knowledge acquisition from textual content reserve some problems and pitfalls during its<br> transformation to RDF triplets. Using an ontology for this purpose helps overcoming ambiguity<br> issues by applying scales of similarity between graphs defined through ontological facts, and ones<br> extracted from texts by means of token-neighborhood criteria.<br> The ontology being developed and the content, augmented with its metadata, are then repre-<br> sented in one property graph. In property graphs it is allowed a set of property–value pairs, called<br> attributes and a label to be associated with nodes and edges, offering additional flexibility when<br> modelling data.<br> The result is a knowledge graph (KG), a graph of data whose nodes represent entities of<br> interest and whose edges represent potentially different relations between them. Both factual and<br> ontological knowledge are existing side-by-side in this KG. In this data-space, classification using<br> link prediction (CULP) can utilize the graph representation of the data and transforms recognition<br> problems to those of link prediction (where we try to find the link between an unlabeled node and<br> the proper class node for it).
description: Helmholtz Metadata Collaboration