TechMiner: Extracting Technologies from Academic Publications

In recent years we have seen the emergence of a variety of scholarly datasets. Typically these capture ‘standard’ scholarly entities and their connections, such as authors, affiliations, venues, publications, citations, and others. However, as the repositories grow and the technology improves, researchers are adding new entities to these repositories to develop a richer model of the scholarly domain. In this paper, we introduce TechMiner, a new approach, which combines NLP, machine learning and semantic technologies, for mining technologies from research publications and generating an OWL ontology describing their relationships with other research entities. The resulting knowledge base can support a number of tasks, such as: richer semantic search, which can exploit the technology dimension to support better retrieval of publications; richer expert search; monitoring the emergence and impact of new technologies, both within and across scientific fields; studying the scholarly dynamics associated with the emergence of new technologies; and others. TechMiner was evaluated on a manually annotated gold standard and the results indicate that it significantly outperforms alternative NLP approaches and that its semantic features improve performance significantly with respect to both recall and precision

Human Breast Milk: Exploring the Linking Ring Among Emerging Components

Maternal breast milk (BM) is a complex and unique fluid that evolution adapted to satisfy neonatal needs; in addition to classical nutrients, it contains several bioactive components. BM characteristically shows inter-individual variability, modifying its composition during different phases of lactation. BM composition, determining important consequences on neonatal gut colonization, influences both short and long-term development. Maternal milk can also shape neonatal microbiota, through its glycobiome rich in Lactobacilli spp. and Bifidobacteria spp. Therefore, neonatal nourishment during the first months of life seems the most important determinant of individual's outcomes. Our manuscript aims to provide new evidence in the characterization of BM metabolome and microbiome, and its comparison to formula milk, allowing the evaluation of each nutrient's influence on neonatal metabolism. This result very interesting since potentially offers an innovative approach to investigate the complex relationship between BM components and infant's health, also providing the chance to intervene in a sartorial way on diet composition, according to the nutritional requests. Future research, integrating metabolomics, microbiomics and stem cells knowledge, could make significant steps forward in understanding BM extraordinary properties and functions

Klink-2: integrating multiple web sources to generate semantic topic networks

The amount of scholarly data available on the web is steadily increasing, enabling different types of analytics which can provide important insights into the research activity. In order to make sense of and explore this large-scale body of knowledge we need an accurate, comprehensive and up-to-date ontology of research topics. Unfortunately, human crafted classifications do not satisfy these criteria, as they evolve too slowly and tend to be too coarse-grained. Current automated methods for generating ontologies of research areas also present a number of limitations, such as: i) they do not consider the rich amount of indirect statistical and semantic relationships, which can help to understand the relation between two topics – e.g., the fact that two research areas are associated with a similar set of venues or technologies; ii) they do not distinguish between different kinds of hierarchical relationships; and iii) they are not able to handle effectively ambiguous topics characterized by a noisy set of relationships. In this paper we present Klink-2, a novel approach which improves on our earlier work on automatic generation of semantic topic networks and addresses the aforementioned limitations by taking advantage of a variety of knowledge sources available on the web. In particular, Klink-2 analyses networks of research entities (including papers, authors, venues, and technologies) to infer three kinds of semantic relationships between topics. It also identifies ambiguous keywords (e.g., “ontology”) and separates them into the appropriate distinct topics – e.g., “ontology/philosophy” vs. “ontology/semantic web”. Our experimental evaluation shows that the ability of Klink-2 to integrate a high number of data sources and to generate topics with accurate contextual meaning yields significant improvements over other algorithms in terms of both precision and recall

Climate Justice in the City: Mapping Heat-Related Risk for Climate Change Mitigation of the Urban and Peri-Urban Area of Padua (Italy)

The mitigation of urban heat islands (UHIs) is crucial for promoting the sustainable development of urban areas. Geographic information systems (GISs) together with satellite-derived data are powerful tools for investigating the spatiotemporal distribution of UHIs. Depending on the availability of data and the geographic scale of the analysis, different methodologies can be adopted. Here, we show a complete open source GIS-based methodology based on satellite-driven data for investigating and mapping the impact of the UHI on the heat-related elderly risk (HERI) in the Functional Urban Area of Padua. Thermal anomalies in the territory were mapped by modelling satellite data from Sentinel-3. After a socio-demographic analysis, the HERI was mapped according to five levels of risk. The highest vulnerability levels were localised within the urban area and in three municipalities near Padua, which represent about 20% of the entire territory investigated. In these municipalities, a percentage of elderly people over 20%, a thermal anomaly over 2.4 °C, and a HERI over 0.65 were found. Based on these outputs, it is possible to define nature-based solutions for reducing the UHI phenomenon and promote a sustainable development of cities. Stakeholders can use the results of these investigations to define climate and environmental policies

Semantic Modelling of Citation Contexts for Context-Aware Citation Recommendation

Contents The four CSV files are the data used for the evaluation in: Saier T., Färber M. (2020) Semantic Modelling of Citation Contexts for Context-Aware Citation Recommendation. In: Advances in Information Retrieval. ECIR 2020. Lecture Notes in Computer Science, vol 12035. DOI: 10.1007/978-3-030-45439-5_15 Code: github.com/IllDepence/ecir2020 The evaluation was conducted in a citation re-prediction setting. CSV Format 7 columns divided by \u241E cited document ID for *_nomarker.csv: citation marker position ambiguous for *_withmarker.csv: citation marker position at 'MAINCIT' in citation context adjacent cited document IDs only given in citrec_unarxive_*.csv divided by \u241F order matches 'CIT' markers in citation context citing document ID citation context MAG field of study IDs divided by \u241F predicate:argument tuples generated based on PredPatt JSON noun phrases for *_nomarker.csv: divided by \u241F for *_withmarker.csv: divided by \u241D into noun phrases noun phrase directly preceding citation marker Data Sources citrec_unarxive_cs_withmarker.csv data set unarXive Paper DOI: 10.1007/s11192-020-03382-z Data DOI: 10.5281/zenodo.2553522 filter citing doc from computer science cited doc is cited at least 5 times citrec_mag_cs_en.csv data set Microsoft Academic Graph (MAG) Paper DOI: 10.1145/2740908.2742839 filter citing doc from computer science and in English citing doc abstract in MAG given cited doc is cited at least 50 times citrec_refseer.csv data set RefSeer Paper URL: ojs.aaai.org/index.php/AAAI/article/view/9528 Data URL: psu.app.box.com/v/refseer filter for citing and cited docs title, venue, venuetype, abstract, and year not NULL citrec_acl-arc_withmarker.csv data set ACL ARC Paper URL: aclanthology.org/L08-1005 Data URL: acl-arc.comp.nus.edu.sg/ filter cited doc has a DBLP ID Paper Citation @inproceedings{Saier2020ECIR, author = {Tarek Saier and Michael F{\"{a}}rber}, title = {{Semantic Modelling of Citation Contexts for Context-aware Citation Recommendation}}, booktitle = {Proceedings of the 42nd European Conference on Information Retrieval}, pages = {220--233}, year = {2020}, month = apr, doi = {10.1007/978-3-030-45439-5_15},

The practice of self-citations: a longitudinal study

In this article, we discuss the outcomes of an experiment where we analysed whether and to what extent the introduction, in 2012, of the new research assessment exercise in Italy (a.k.a. Italian Scientific Habilitation) affected self-citation behaviours in the Italian research community. The Italian Scientific Habilitation attests to the scientific maturity of researchers and in Italy, as in many other countries, is a requirement for accessing to a professorship. To this end, we obtained from ScienceDirect 35,673 articles published from 1957 to 2016 by the participants to the 2012 Italian Scientific Habilitation, that resulted in the extraction of 1,379,050 citations retrieved through Semantic Publishing technologies. Our analysis showed an overall increment in author self-citations (i.e. where the citing article and the cited article share at least one author) in several of the 24 academic disciplines considered. However, we depicted a stronger causal relation between such increment and the rules introduced by the 2012 Italian Scientific Habilitation in 10 out of 24 disciplines analysed

Predicting the results of evaluation procedures of academics

Background. The 2010 reform of the Italian university system introduced the National Scientific Habilitation (ASN) as a requirement for applying to permanent professor positions. Since the CVs of the 59,149 candidates and the results of their assessments have been made publicly available, the ASN constitutes an opportunity to perform analyses about a nation-wide evaluation process. Objective. The main goals of this paper are: (i) predicting the ASN results using the information contained in the candidates’ CVs; (ii) identifying a small set of quantitative indicators that can be used to perform accurate predictions. Approach. Semantic technologies are used to extract, systematize and enrich the information contained in the applicants’ CVs, and machine learning methods are used to predict the ASN results and to identify a subset of relevant predictors. Results. For predicting the success in the role of associate professor, our best models using all and the top 15 predictors make accurate predictions (F-measure values higher than 0.6) in 88% and 88.6% of the cases, respectively. Similar results have been achieved for the role of full professor. Evaluation. The proposed approach outperforms the other models developed to predict the results of researchers’ evaluation procedures. Conclusions. Such results allow the development of an automated system for supporting both candidates and committees in the future ASN sessions and other scholars’ evaluation procedures

Pathways of 4-hydroxy-2-nonenal detoxification in a human astrocytoma cell line

One of the consequences of the increased level of oxidative stress that often characterizes the cancer cell environment is the abnormal generation of lipid peroxidation products, above all 4-hydroxynonenal. The contribution of this aldehyde to the pathogenesis of several diseases is well known. In this study, we characterized the ADF astrocytoma cell line both in terms of its pattern of enzymatic activities devoted to 4-hydroxynonenal removal and its resistance to oxidative stress induced by exposure to hydrogen peroxide. A comparison with lens cell lines, which, due to the ocular function, are normally exposed to oxidative conditions is reported. Our results show that, overall, ADF cells counteract oxidative stress conditions better than normal cells, thus confirming the redox adaptation demonstrated for several cancer cells. In addition, the markedly high level of NADP+-dependent dehydrogenase activity acting on the glutahionyl-hydroxynonanal adduct detected in ADF cells may promote, at the same time, the detoxification and recovery of cell-reducing power in these cells