Generating Natural Language from Linked Data:Unsupervised template extraction

We propose an architecture for generating natural language from Linked Data that automatically learns sentence templates and statistical document planning from parallel RDF datasets and text. We have built a proof-of-concept system (LOD-DEF) trained on un-annotated text from the Simple English Wikipedia and RDF triples from DBpedia, focusing exclusively on factual, non-temporal information. The goal of the system is to generate short descriptions, equivalent to Wikipedia stubs, of entities found in Linked Datasets. We have evaluated the LOD-DEF system against a simple generate-from-triples baseline and human-generated output. In evaluation by humans, LOD-DEF significantly outperforms the baseline on two of three measures: non-redundancy and structure and coherence.

Romania: Subsistence Farming After Imposed Industrialisation

During the transition to the market economy, real earnings fell sharply. The existence of an underground economy generally deemed to have grown at a worryingly fast rate has had a strong effect on the (rising) level and distribution of income (increase in inequalities). The Communist regimes extensive housing estate building programme, mainly in the urban areas, gave virtually all Romanian citizens housing. These State-owned dwellings were sold to their tenants after 1989, such that 95% of households now own their own housing. However, the downturn in construction in the 1990s prompted a housing crisis. Housing conditions remain poor particularly in the rural areas. The frequency of poverty is particularly high in rural areas where a large proportion of households have low earnings, and sub-standard dwellings. A high percentage of urban households also have to deal with the pressure of growing financial difficulties due to unemployment and low pension payments.Multiple Dimensions of Poverty, Socialist Country in Transition, Romania

Measuring scientific impact beyond academia:An assessment of existing impact metrics and proposed improvements

How does scientific research affect the world around us? Being able to answer this question is of great importance in order to appropriately channel efforts and resources in science. The impact by scientists in academia is currently measured by citation based metrics such as h-index, i-index and citation counts. These academic metrics aim to represent the dissemination of knowledge among scientists rather than the impact of the research on the wider world. In this work we are interested in measuring scientific impact beyond academia, on the economy, society, health and legislation (comprehensive impact). Indeed scientists are asked to demonstrate evidence of such comprehensive impact by authoring case studies in the context of the Research Excellence Framework (REF). We first investigate the extent to which existing citation based metrics can be indicative of comprehensive impact. We have collected all recent REF impact case studies from 2014 and we have linked these to papers in citation networks that we constructed and derived from CiteSeerX, arXiv and PubMed Central using a number of text processing and information retrieval techniques. We have demonstrated that existing citation-based metrics for impact measurement do not correlate well with REF impact results. We also consider metrics of online attention surrounding scientific works, such as those provided by the Altmetric API. We argue that in order to be able to evaluate wider non-academic impact we need to mine information from a much wider set of resources, including social media posts, press releases, news articles and political debates stemming from academic work. We also provide our data as a free and reusable collection for further analysis, including the PubMed citation network and the correspondence between REF case studies, grant applications and the academic literature

Die Abdeckerei und die Hinrichtungsstätte in Kamienna Góra (Landeshut) und Złotoryja (Goldberg), Polen. Zwei Beispiele dargestellt an Hand archäologischer und historischer Forschungen

Cílem příspěvku je představení výsledků výzkumů, které byly prováděny na bývalých popravištích v Kamenné Hoře a Zlotoryji (Dolní Slezsko, Polsko). V průběhu archeologických výzkumů v obou lokalitách byly nalezeny pozůstatky zděných šibenic o průměru od 5,2 do 7,5 m a také soubor různých kosterních částí. Ty byly předány k antropologické a archeozoologické analýze. Nejvíce kostí bylo objeveno ve velké jámě u šibenice v Kamenné Hoře. V případě Zlotoryji se nejvíce pozůstatků nacházelo uprostřed konstrukce. Během prací nebyly ani na jednom stanovišti nalezeny anatomické hroby přesto, že o ukládání těl na těchto místech informovaly písemné zdroje. Po ukončení výzkumů se započalo s bádáním v historických archívech. Současně analýza kostí ukázala, že jak kosti z vnitřku šibenice tak i z okolí patří hlavně zvířatům. V případě Kamenné Hory bylo lidských pozůstatků méně než 5 % z celého souboru, v případě Zlotoryji méně než 6 %. Celkově dominovaly kosti koní, psů, koček a skotu. Výzkumy ukázaly velmi důležitou sanitární funkci bývalých popravišť, která byla v mnoha městech využívána k zakopávání padlých zvířat z okolních obcí.The aim of the paper is to present the results of the research that was carried out at the former execution sites in Kamienna Góra and Złotoryja (Lower Silesia, Poland). During the archaeological excavations in both localities, there were found remains of brick gallows with a diameter of 5.2 to 7.5 m and also a set of skeletal parts. These were passed on to anthropological and archeozoological analysis. Most bones were found in a large pit near the gallows in Kamienna Góra. In the case of Złotoryja, the most remains were in the middle of the structure. Even during the work, anatomical graves were not found at one site, despite the fact that written sources informed about the storage of these bodies. After the excavations, research in historical archives began. At the same time, bone analysis showed that bones from the inside of the gallows and the surrounding area belong mainly to animals. In the case of Kamienna Góra, human remains were less than 5% of the entire colection and less than 6% in the case of Złotoryja. Overall, the bones of horses, dogs, cats and cattle dominated. Research has shown a very important sanitary function of former scaffolds, which has been used in many cities to burrow fallen animals from nearby communities

Classifying patient and professional voice in social media health posts

BACKGROUND: Patient-based analysis of social media is a growing research field with the aim of delivering precision medicine but it requires accurate classification of posts relating to patients’ experiences. We motivate the need for this type of classification as a pre-processing step for further analysis of social media data in the context of related work in this area. In this paper we present experiments for a three-way document classification by patient voice, professional voice or other. We present results for a convolutional neural network classifier trained on English data from two different data sources (Reddit and Twitter) and two domains (cardiovascular and skin diseases). RESULTS: We found that document classification by patient voice, professional voice or other can be done consistently manually (0.92 accuracy). Annotators agreed roughly equally for each domain (cardiovascular and skin) but they agreed more when annotating Reddit posts compared to Twitter posts. Best classification performance was obtained when training two separate classifiers for each data source, one for Reddit and one for Twitter posts, when evaluating on in-source test data for both test sets combined with an overall accuracy of 0.95 (and macro-average F1 of 0.92) and an F1-score of 0.95 for patient voice only. CONCLUSION: The main conclusion resulting from this work is that combining social media data from platforms with different characteristics for training a patient and professional voice classifier does not result in best possible performance. We showed that it is best to train separate models per data source (Reddit and Twitter) instead of a model using the combined training data from both sources. We also found that it is preferable to train separate models per domain (cardiovascular and skin) while showing that the difference to the combined model is only minor (0.01 accuracy). Our highest overall F1-score (0.95) obtained for classifying posts as patient voice is a very good starting point for further analysis of social media data reflecting the experience of patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12911-021-01577-9