New multiplexing scheme for monitoring fiber optic Bragg grating sensors in the coherence domain

A new multiplexing scheme for monitoring fiber optic Bragg gratings in the coherence domain has been developed. Grating pairs with different grating distances are distributed along a fiber line, and interference between their reflections is monitored with a scanning Michelson interferometer. The Bragg wavelength of the individual sensor elements is determined from the interference signal frequency

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

EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs

Many question answering systems over knowledge graphs rely on entity and relation linking components in order to connect the natural language input to the underlying knowledge graph. Traditionally, entity linking and relation linking have been performed either as dependent sequential tasks or as independent parallel tasks. In this paper, we propose a framework called EARL, which performs entity linking and relation linking as a joint task. EARL implements two different solution strategies for which we provide a comparative analysis in this paper: The first strategy is a formalisation of the joint entity and relation linking tasks as an instance of the Generalised Travelling Salesman Problem (GTSP). In order to be computationally feasible, we employ approximate GTSP solvers. The second strategy uses machine learning in order to exploit the connection density between nodes in the knowledge graph. It relies on three base features and re-ranking steps in order to predict entities and relations. We compare the strategies and evaluate them on a dataset with 5000 questions. Both strategies significantly outperform the current state-of-the-art approaches for entity and relation linking.Comment: International Semantic Web Conference 201

Why reinvent the wheel: Let's build question answering systems together

Modern question answering (QA) systems need to flexibly integrate a number of components specialised to fulfil specific tasks in a QA pipeline. Key QA tasks include Named Entity Recognition and Disambiguation, Relation Extraction, and Query Building. Since a number of different software components exist that implement different strategies for each of these tasks, it is a major challenge to select and combine the most suitable components into a QA system, given the characteristics of a question. We study this optimisation problem and train classifiers, which take features of a question as input and have the goal of optimising the selection of QA components based on those features. We then devise a greedy algorithm to identify the pipelines that include the suitable components and can effectively answer the given question. We implement this model within Frankenstein, a QA framework able to select QA components and compose QA pipelines. We evaluate the effectiveness of the pipelines generated by Frankenstein using the QALD and LC-QuAD benchmarks. These results not only suggest that Frankenstein precisely solves the QA optimisation problem but also enables the automatic composition of optimised QA pipelines, which outperform the static Baseline QA pipeline. Thanks to this flexible and fully automated pipeline generation process, new QA components can be easily included in Frankenstein, thus improving the performance of the generated pipelines

An analysis of observed daily maximum wind gusts in the UK

The greatest attention to the UK wind climatology has focused upon mean windspeeds, despite a knowledge of gust speeds being essential to a variety of users. This paper goes some way to redressing this imbalance by analysing observed daily maximum gust speeds from a 43-station network over the period 1980–2005. Complementing these data are dynamically downscaled reanalysis data, generated using the PRECIS Regional Climate Modelling system, for the period 1959–2001. Inter-annual variations in both the observed and downscaled reanalysis gust speeds are presented, with a statistically significant (at the 95% confidence interval) 5% increase across the network in daily maximum gust speeds between 1959 and the early 1990s, followed by an apparent decrease. The benefit of incorporating dynamically downscaled reanalysis data is revealed by the fact that the decrease in gust speeds since 1993 may be placed in the context of a very slight increase displayed over the longer 1959–2001 period. Furthermore, the severity of individual windstorm events is considered, with high profile recent events placed into the context of the long term record. A daily cycle is identified from the station observations in the timing of the daily maximum gust speeds, with an afternoon peak occurring between 12:00–15:00, exhibiting spatial and intra-annual variations

Improving Editorial Workflow and Metadata Quality at Springer Nature

Identifying the research topics that best describe the scope of a scientific publication is a crucial task for editors, in particular because the quality of these annotations determine how effectively users are able to discover the right content in online libraries. For this reason, Springer Nature, the world's largest academic book publisher, has traditionally entrusted this task to their most expert editors. These editors manually analyse all new books, possibly including hundreds of chapters, and produce a list of the most relevant topics. Hence, this process has traditionally been very expensive, time-consuming, and confined to a few senior editors. For these reasons, back in 2016 we developed Smart Topic Miner (STM), an ontology-driven application that assists the Springer Nature editorial team in annotating the volumes of all books covering conference proceedings in Computer Science. Since then STM has been regularly used by editors in Germany, China, Brazil, India, and Japan, for a total of about 800 volumes per year. Over the past three years the initial prototype has iteratively evolved in response to feedback from the users and evolving requirements. In this paper we present the most recent version of the tool and describe the evolution of the system over the years, the key lessons learnt, and the impact on the Springer Nature workflow. In particular, our solution has drastically reduced the time needed to annotate proceedings and significantly improved their discoverability, resulting in 9.3 million additional downloads. We also present a user study involving 9 editors, which yielded excellent results in term of usability, and report an evaluation of the new topic classifier used by STM, which outperforms previous versions in recall and F-measure

RuBQ: A Russian Dataset for Question Answering over Wikidata

The paper presents RuBQ, the first Russian knowledge base question answering (KBQA) dataset. The high-quality dataset consists of 1,500 Russian questions of varying complexity, their English machine translations, SPARQL queries to Wikidata, reference answers, as well as a Wikidata sample of triples containing entities with Russian labels. The dataset creation started with a large collection of question-answer pairs from online quizzes. The data underwent automatic filtering, crowd-assisted entity linking, automatic generation of SPARQL queries, and their subsequent in-house verification. The freely available dataset will be of interest for a wide community of researchers and practitioners in the areas of Semantic Web, NLP, and IR, especially for those working on multilingual question answering. The proposed dataset generation pipeline proved to be efficient and can be employed in other data annotation projects. © 2020, Springer Nature Switzerland AG.We thank Mikhail Galkin, Svitlana Vakulenko, Daniil Sorokin, Vladimir Kovalenko, Yaroslav Golubev, and Rishiraj Saha Roy for their valuable comments and fruitful discussion on the paper draft. We also thank Pavel Bakhvalov, who helped collect RuWikidata8M sample and contributed to the first version of the entity linking tool. We are grateful to Yandex.Toloka for their data annotation grant. PB acknowledges support by Ural Mathematical Center under agreement No. 075-02-2020-1537/1 with the Ministry of Science and Higher Education of the Russian Federation

Pervasive Growth Reduction in Norway Spruce Forests following Wind Disturbance

Background: In recent decades the frequency and severity of natural disturbances by e.g., strong winds and insect outbreaks has increased considerably in many forest ecosystems around the world. Future climate change is expected to further intensify disturbance regimes, which makes addressing disturbances in ecosystem management a top priority. As a prerequisite a broader understanding of disturbance impacts and ecosystem responses is needed. With regard to the effects of strong winds – the most detrimental disturbance agent in Europe – monitoring and management has focused on structural damage, i.e., tree mortality from uprooting and stem breakage. Effects on the functioning of trees surviving the storm (e.g., their productivity and allocation) have been rarely accounted for to date. Methodology/Principal Findings: Here we show that growth reduction was significant and pervasive in a 6.79?million hectare forest landscape in southern Sweden following the storm Gudrun (January 2005). Wind-related growth reduction in Norway spruce (Picea abies (L.) Karst.) forests surviving the storm exceeded 10 % in the worst hit regions, and was closely related to maximum gust wind speed (R 2 = 0.849) and structural wind damage (R 2 = 0.782). At the landscape scale, windrelated growth reduction amounted to 3.0 million m 3 in the three years following Gudrun. It thus exceeds secondary damage from bark beetles after Gudrun as well as the long-term average storm damage from uprooting and stem breakage in Sweden