An approach to map geography mark-up language data to resource description framework schema

GML serves as premier modeling language used to represent data of geographic information related to geography locations. However, a problem of GML is its ability to integrate with a variety of geographical and GPS applications. Since, GML saves data in coordinates and in topology for the purpose to integrate data with variety of applications on semantic web, data be mapped to Resource Description Framework (RDF) and Resource Description Framework Schema (RDFS). An approach of mapping GML metadata to RDFS is presented in this paper. This study focuses on the methodology to convert GML data in semantics to represent in extended and enriched form such as RDFS as representation in RDF is not sufficient over semantic web. Firstly, we have GML script from case study and parse it using GML parser and get XML file. XML file parse using Java and get text file to extract GML features and then get a graph form of these features. After that we designed methodology of prototype tool to map GML features to RDFS. Tool performed features by features mapping and extracted results are represented in the tabular form of mapping GML metadata to RDFS. © 2020, Springer Nature Singapore Pte Ltd.E

Opinions, Intentions, Freedom of Expression, ... , and Other Human Aspects of Misinformation Online

As a wicked problem, limiting the harm caused by misinformation requires merging multiple perspectives to the design of digital interventions, including an understanding of human behaviour and motivations in judging and promoting false information, as well as strategies to detect and stop its propagation without unduly infringing on rights or freedoms of expression. Tools and online services are continuously being developed to support different stakeholders in this battle, such as social media users, journalists, and policymakers. As our studies have demonstrated, the expected impact of online solutions is hampered by limitations associated with lack of explainability, complex user interface, limited datasets, restricted accessibility, biased algorithms, among other factors that can confuse, overwhelm, or mislead users in their own ways. These ethical implications are typically neglected when new digital solutions to tackle misinformation are conceived. This hands-on workshop pro- poses to unpack the state-of-the-art on social, societal and political studies and socio-technical solutions to stop misinformation, challenging the participants to first critically refect upon limitations of existing approaches, to then co-create a future with integrating perspectives focusing on ethical aspects and societal impact

Evaluating Knowledge Anchors in Data Graphs against Basic Level Objects

The growing number of available data graphs in the form of RDF Linked Da-ta enables the development of semantic exploration applications in many domains. Often, the users are not domain experts and are therefore unaware of the complex knowledge structures represented in the data graphs they in-teract with. This hinders users’ experience and effectiveness. Our research concerns intelligent support to facilitate the exploration of data graphs by us-ers who are not domain experts. We propose a new navigation support ap-proach underpinned by the subsumption theory of meaningful learning, which postulates that new concepts are grasped by starting from familiar concepts which serve as knowledge anchors from where links to new knowledge are made. Our earlier work has developed several metrics and the corresponding algorithms for identifying knowledge anchors in data graphs. In this paper, we assess the performance of these algorithms by considering the user perspective and application context. The paper address the challenge of aligning basic level objects that represent familiar concepts in human cog-nitive structures with automatically derived knowledge anchors in data graphs. We present a systematic approach that adapts experimental methods from Cognitive Science to derive basic level objects underpinned by a data graph. This is used to evaluate knowledge anchors in data graphs in two ap-plication domains - semantic browsing (Music) and semantic search (Ca-reers). The evaluation validates the algorithms, which enables their adoption over different domains and application contexts

Quantum-Enhanced Magnetometry at Optimal Number Density

We study the use of squeezed probe light and evasion of measurement backaction to enhance the sensitivity and measurement bandwidth of an optically pumped magnetometer (OPM) at sensitivity-optimal atom number density. By experimental observation, and in agreement with quantum noise modeling, a spin-exchange-limited OPM probed with off-resonance laser light is shown to have an optimal sensitivity determined by density-dependent quantum noise contributions. Application of squeezed probe light boosts the OPM sensitivity beyond this laser-light optimum, allowing the OPM to achieve sensitivities that it cannot reach with coherent-state probing at any density. The observed quantum sensitivity enhancement at optimal number density is enabled by measurement backaction evasion

Measurement-induced, spatially-extended entanglement in a hot, strongly-interacting atomic system

Quantum technologies use entanglement to outperform classical technologies, and often employ strong cooling and isolation to protect entangled entities from decoherence by random interactions. Here we show that the opposite strategy—promoting random interactions—can help generate and preserve entanglement. We use optical quantum non-demolition measurement to produce entanglement in a hot alkali vapor, in a regime dominated by random spin-exchange collisions. We use Bayesian statistics and spin-squeezing inequalities to show that at least 1.52(4) × 1013 of the 5.32(12) × 1013 participating atoms enter into singlet-type entangled states, which persist for tens of spin-thermalization times and span thousands of times the nearest-neighbor distance. The results show that high temperatures and strong random interactions need not destroy many-body quantum coherence, that collective measurement can produce very complex entangled states, and that the hot, strongly-interacting media now in use for extreme atomic sensing are well suited for sensing beyond the standard quantum limit

Squeezed-Light Enhancement and Backaction Evasion in a High Sensitivity Optically Pumped Magnetometer

We study the effect of optical polarization squeezing on the performance of a sensitive, quantum-noise-limited optically pumped magnetometer. We use Bell-Bloom (BB) optical pumping to excite a Rb87 vapor containing 8.2×1012 atoms/cm3 and Faraday rotation to detect spin precession. The sub-pT/Hz sensitivity is limited by spin projection noise (photon shot noise) at low (high) frequencies. Probe polarization squeezing both improves high-frequency sensitivity and increases measurement bandwidth, with no loss of sensitivity at any frequency, a direct demonstration of the evasion of measurement backaction noise. We provide a model for the quantum noise dynamics of the BB magnetometer, including spin projection noise, probe polarization noise, and measurement backaction effects. The theory shows how polarization squeezing reduces optical noise, while measurement backaction due to the accompanying ellipticity antisqueezing is shunted into the unmeasured spin component. The method is compatible with high-density and multipass techniques that reach extreme sensitivity

Visualizing Readable Instance Graphs of Ontology with Memo Graph

International audienceIn the context of the Captain Memo memory prosthesis for Alzheimer’s patients, we want to generate the family/entourage tree of the user from data structured based on the PersonLink ontology. This graph ought to be accessible and readable to this particular user. In our previous work, we proposed an ontology visualization tool called Memo Graph. It aims to offer an accessible visualization to Alzheimer’s patients. In this paper, we extend it to address the readability requirement based on the IKIEV approach. It extracts the most important instances (key-instances) from ontology and generates a “summary instance graph” (middle-out browsing method). The extraction and visualization processes are undertaken incrementally. First, an “initial summary instance graph” is generated, then permitting iteratively the visualization of supplementary key-instances as required. Key-instances’ extraction is based on measures that take into account the semantic similarity between the ontological elements and the user’s navigation history