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

Definition, conceptualisation and measurement of trust

This report documents the program and the outcomes of Dagstuhl Seminar 21381 "Conversational Agent as Trustworthy Autonomous System (Trust-CA)". First, we present the abstracts of the talks delivered by the Seminar’s attendees. Then we report on the origin and process of our six breakout (working) groups. For each group, we describe its contributors, goals and key questions, key insights, and future research. The themes of the groups were derived from a pre-Seminar survey, which also led to a list of suggested readings for the topic of trust in conversational agents. The list is included in this report for references

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

Towards an interoperable ecosystem of AI and LT platforms : a roadmap for the implementation of different levels of interoperability

With regard to the wider area of AI/LT platform interoperability, we concentrate on two core aspects: (1) cross-platform search and discovery of resources and services; (2) composition of cross-platform service workflows. We devise five different levels (of increasing complexity) of platform interoperability that we suggest to implement in a wider federation of AI/LT platforms. We illustrate the approach using the five emerging AI/LT platforms AI4EU, ELG, Lynx, QURATOR and SPEAKER