Mining Scholarly Data for Fine-Grained Knowledge Graph Construction

Knowledge graphs (KG) are large network of entities and relationships, tipically expressed as RDF triples, relevant to a specific domain or an organization. Scientific Knowledge Graphs (SKGs) focus on the scholarly domain and typically contain metadata describing research publications such as authors, venues, organizations, research topics, and citations. The next big challenge in this field regards the generation of SKGs that also contain a explicit representation of the knowledge presented in research publications. In this paper, we present a preliminary approach that uses a set of NLP and Deep Learning methods for extracting entities and relationships from research publications and then integrates them in a KG. More specifically, we i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, ii) describe an approach for integrating entities and relationships generated by these tools, iii) analyse an automatically generated Knowledge Graph including 10, 425 entities and 25, 655 relationships derived from 12, 007 publications in the field of Semantic Web, and iv) discuss how Deep Learning methods can be applied to overcome some limitations of the current techniques

Generating knowledge graphs by employing Natural Language Processing and Machine Learning techniques within the scholarly domain

The continuous growth of scientific literature brings innovations and, at the same time, raises new challenges. One of them is related to the fact that its analysis has become difficult due to the high volume of published papers for which manual effort for annotations and management is required. Novel technological infrastructures are needed to help researchers, research policy makers, and companies to time-efficiently browse, analyse, and forecast scientific research. Knowledge graphs i.e., large networks of entities and relationships, have proved to be effective solution in this space. Scientific knowledge graphs focus on the scholarly domain and typically contain metadata describing research publications such as authors, venues, organizations, research topics, and citations. However, the current generation of knowledge graphs lacks of an explicit representation of the knowledge presented in the research papers. As such, in this paper, we present a new architecture that takes advantage of Natural Language Processing and Machine Learning methods for extracting entities and relationships from research publications and integrates them in a large-scale knowledge graph. Within this research work, we i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, ii) describe an approach for integrating entities and relationships generated by these tools, iii) show the advantage of such an hybrid system over alternative approaches, and vi) as a chosen use case, we generated a scientific knowledge graph including 109,105 triples, extracted from 26,827 abstracts of papers within the Semantic Web domain. As our approach is general and can be applied to any domain, we expect that it can facilitate the management, analysis, dissemination, and processing of scientific knowledge

Mining Scholarly Publications for Scientific Knowledge Graph Construction

In this paper, we present a preliminary approach that uses a set of NLP and Deep Learning methods for extracting entities and relationships from research publications and then integrates them in a Knowledge Graph. More specifically, we i) tackle the challenge of knowledge extraction by employing several state-of-the-art Natural Language Processing and Text Mining tools, ii) describe an approach for integrating entities and relationships generated by these tools, and iii) analyse an automatically generated Knowledge Graph including 10,425 entities and 25,655 relationships in the field of Semantic Web

Synthesis and study of polyhydroxylated phenol derivatives with potential cosmetic and phytoiatric applications

Tyrosinase (polyphenol oxidase, E.C. 1.14.18.1) and laccase (phenol oxidase, E.C. 1.10.3.2) are multifunctional copper-containing enzymes, that are keys in melanin biosynthesis, melanisation in animals and browning in plants. Our study is aimed to prepare new monomer and dimer phenol derivatives as potential inhibitors of melanin production starting from natural hydroxylated aromatic units

Open Science @ UNIBO: il servizio di supporto a rete per le comunità di ricerca

Le forti spinte globali a favore dell’Open Access (OA) e dell’Open Science (OS) hanno sollecitato i sistemi bibliotecari delle università a rivedere servizi e competenze in funzione dei nuovi bisogni delle loro comunità di riferimento. Il Sistema Bibliotecario dell’Università di Bologna ha risposto a questi stimoli definendo un modello a rete a supporto dell’Open Access avviato sperimentalmente nella seconda metà del 2018. L’obiettivo strategico condiviso e co-gestito dall’intera comunità accademica è la promozione di prassi che consentano il libero accesso e il riuso delle pubblicazioni e dei dati della ricerca scientifica. Il servizio si struttura come una rete decentrata di punti di supporto collocati nelle biblioteche con il coordinamento centrale a cura della Biblioteca Digitale di Ateneo, AlmaDL. AlmaDL si occupa della formazione dei bibliotecari del servizio di supporto, fornisce loro assistenza specialistica anche in materia di diritto d’autore, coordina, monitora e sostiene il servizio con personale dedicato, oltre a offrire assistenza per la gestione FAIR dei dati di ricerca nel data repository di Ateneo e a garantire il raccordo istituzionale partecipando al Gruppo di lavoro Open Science di Ateneo. I punti di servizio offrono alle loro comunità scientifiche consulenza e orientamento, validano le pubblicazioni scientifiche depositate nel repository istituzionale, organizzano campagne di sensibilizzazione e rispondono alle esigenze specifiche delle comunità scientifiche. Ad oggi i bibliotecari coinvolti nel servizio sono 61; quasi 24.000 le pubblicazioni in OA e oltre 200 i dataset depositati nei repository istituzionali; 4830 le consulenze e 178 ore di formazione a cui hanno partecipato 1307 utenti. Il modello adottato ha presentato numerosi vantaggi rivelandosi sostenibile e attento alle specificità dei diversi ambiti disciplinari. Inoltre il continuo scambio di informazioni tra i nodi della rete permette lo sviluppo delle competenze e delle conoscenze in una continua ridefinizione del modello organizzativo e dei contenuti del servizio

Nuovo dato corologico su Canthydrus diophthalmus (Reiche & Saulcy 1855) nella Sardegna sud orientale (Coleoptera, Noteridae).

Riassunto: Canthydrus diophthalmus (Reiche & Saulcy, 1855) viene segnalato per una nuova località, sita nel comune di Villaputzu della Sardegna sud orientale (Quirra, Stagno di Murtas), grazie al ritrovamento di diversi esemplari reperiti durante il biomonitoraggio degli Hydroadephaga dell’area

Nuovi dati biologici e corologici sull’endemismo sardo Acroneuroptila puddui Cadeddu, 1970 (Orthoptera, Ensifera, Grillidae, Gryllomorphinae)

Riassunto: vengono forniti nuovi dati corologici sull’endemismo sardo Acroneuroptila puddui Cadeddu, 1970. Una specie finora conosciuta per poche cavità dell’isola, classificata come specie vulnerabile (VU) a rischio di estinzione nella Red List europea degli ortotteri. Vengono altresì fornite alcune osservazioni inedite relative al suo regime alimentare

Antamanide Analogs as Potential Inhibitors of Tyrosinase

The tyrosinase enzyme, which catalyzes the hydroxylation of monophenols and the oxidation of o-diphenols, is typically involved in the synthesis of the dark product melanin starting from the amino acid tyrosine. Contributing to the browning of plant and fruit tissues and to the hyperpigmentation of the skin, leading to melasma or age spots, the research of possible tyrosinase inhibitors has attracted much interest in agri-food, cosmetic, and medicinal industries. In this study, we analyzed the capability of antamanide, a mushroom bioactive cyclic decapeptide, and some of its glycine derivatives, compared to that of pseudostellarin A, a known tyrosinase inhibitor, to hinder tyrosinase activity by using a spectrophotometric method. Additionally, computational docking studies were performed in order to elucidate the interactions occurring with the tyrosinase catalytic site. Our results show that antamanide did not exert any inhibitory activity. On the contrary, the three glycine derivatives AG9, AG6, and AOG9, which differ from each other by the position of a glycine that substitutes phenylalanine in the parent molecule, improving water solubility and flexibility, showed tyrosinase inhibition by spectrophotometric assays. Analytical data were confirmed by computational studies