Link Discovery Research Articles

Interlinking and enrichment of disparate organisational data with LOD at application run-time

The present work focuses on the semantic integration, enrichment and interlinking of data that is semi-automatically generated by documenting artworks and their creators. In this work, we have been experimenting with RDFization and links discovery tools, W3C standards and widely accepted vocabularies. This work has been already evaluated with museum data, emphasising the discovery of links between disparate data sets and external data sources at the back-end of the proposed approach. In this paper, while contributing a number of other new and extended features at the back-end of this approach, we emphasise links discovery at the front-end in order to interlink and enrich cultural data with LOD at application run-time and facilitate the real-time and up-to-date exploitation of semantically integrated and LOD-enriched data. This is achieved by implementing a custom links discovery method and evaluating it within a web application using LOD cloud data sources such as DBpedia and Europeana.

Recommending Geo-semantically Related Classes for Link Discovery

The growth of Web of Data led to the development of dataset recommendation methodologies, which automate the discovery of datasets that may contain same or related instances (i.e., objects), in order to be used as input for several tasks including Link Discovery. The recommendation process takes as input one dataset (or any tripleset) and proposes other datasets which are the most likely to contain related instances. Existing recommenders determine the relevance between datasets by comparing their textual and structural similarity or by examining existing links among them. In this paper, we determine relevancy by comparing the geospatial relatedness of triplesets containing instances belonging to spatial classes (that is, classes containing instances whose locations are georeferenced by point geometries) based on the hypothesis that pairs of classes whose instances present similar spatial distribution are likely to contain semantically related instances. The proposed methodology builds summaries that capture the spatial distribution of classes. It utilizes the summaries, first, to rule out irrelevant (to an input class) classes by applying spatial filters and, then, to rank the remaining classes by applying a geospatial relatedness measure, so as the top ranked classes are more probable to contain related instances. The methodology’s evaluation contains an exploration of Web of Data spatial classes characteristics and a discussion of the experiment results that validate our hypothesis. We show that the spatial filtering reduces effectively and efficiently up to 99% the search space for relevant classes in Web of Data and that the proposed geospatial relatedness measures generate ranked lists of recommended classes with 62% mean average precision, approximately 35% higher than simple baselines.

An Efficient Topology Discovery Protocol with Node ID Assignment Based on Layered Model for Underwater Acoustic Networks.

Underwater acoustic networks are widely used in survey missions and environmental monitoring. When an underwater acoustic network (UAN) is deployed in a marine region or two UANs merge, each node hardly knows the entire network and may not have a unique node ID. Therefore, a network topology discovery protocol that can complete node discovery, link discovery, and node ID assignment are necessary and important. Considering the limited node energy and long propagation delay in UANs, it is challenging to obtain the network topology with reduced overheads and a short delay in this initial network state. In this paper, an efficient topology discovery protocol (ETDP) is proposed to achieve adaptive node ID assignment and topology discovery simultaneously. To avoiding packet collision in this initial network state, ETDP controls the transmission of topology discovery (TD) packets, based on a local timer, and divides the network into different layers to make nodes transmit TD packets orderly. Exploiting the received TD packets, each node could obtain the network topology and assign its node ID independently. Simulation results show that ETDP completes network topology discovery for all nodes in the network with significantly reduced energy consumption and short delay; meanwhile, it assigns the shortest unique IDs to all nodes with reduced overheads.

Linking OpenStreetMap with knowledge graphs — Link discovery for schema-agnostic volunteered geographic information

Representations of geographic entities captured in popular knowledge graphs such as Wikidata and DBpedia are often incomplete. OpenStreetMap (OSM) is a rich source of openly available, volunteered geographic information that has a high potential to complement these representations. However, identity links between the knowledge graph entities and OSM nodes are still rare. The problem of link discovery in these settings is particularly challenging due to the lack of a strict schema and heterogeneity of the user-defined node representations in OSM. In this article, we propose OSM2KG - a novel link discovery approach to predict identity links between OSM nodes and geographic entities in a knowledge graph. The core of the OSM2KG approach is a novel latent, compact representation of OSM nodes that captures semantic node similarity in an embedding. OSM2KG adopts this latent representation to train a supervised model for link prediction and utilises existing links between OSM and knowledge graphs for training. Our experiments conducted on several OSM datasets, as well as the Wikidata and DBpedia knowledge graphs, demonstrate that OSM2KG can reliably discover identity links. OSM2KG achieves an F1 score of 92.05% on Wikidata and of 94.17% on DBpedia on average, which corresponds to a 21.82 percentage points increase in F1 score on Wikidata compared to the best performing baselines.

Impact of diet and the bacterial microbiome on the mucous barrier and immune disorders.

The prevalence of chronic immune and metabolic disorders is increasing rapidly. In particular, inflammatory bowel diseases, obesity, diabetes, asthma and chronic obstructive pulmonary disease have become major healthcare and economic burdens worldwide. Recent advances in microbiome research have led to significant discoveries of associative links between alterations in the microbiome and health, as well as these chronic supposedly noncommunicable, immune/metabolic disorders. Importantly, the interplay between diet, microbiome and the mucous barrier in these diseases has gained significant attention. Diet modulates the mucous barrier via alterations in gut microbiota, resulting in either disease onset/exacerbation due to a "poor" diet or protection against disease with a "healthy" diet. In addition, many mucosa-associated disorders possess a specific gut microbiome fingerprint associated with the composition of the mucous barrier, which is further influenced by host-microbiome and inter-microbial interactions, dietary choices, microbe immigration and antimicrobials. Our review focuses on the interactions of diet (macronutrients and micronutrients), gut microbiota and mucous barriers (gastrointestinal and respiratory tract) and their importance in the onset and/or progression of major immune/metabolic disorders. We also highlight the key mechanisms that could be targeted therapeutically to prevent and/or treat these disorders.

An Indirect Controller-Legacy Switch Forwarding Scheme for Link Discovery in Hybrid SDN

Software-defined networking (SDN) has predominantly become the defacto network standard for next generation networking due to its flexibility features; through a single-phase full deployment is unlikely owing to obvious tedious substitution and economic reasons. This has paved the path for hybrid SDN (h-SDN) as a practical alternative with a centralized SDN controller that connects a slow of OpenFlow switches (OFSs) and Legacy switches (LSs). However, such h-SDN imposes fresh challenges since the existing SDN solutions for key functions like topology discovery are rendered futile since LSs do not respond to the controller in the same way OFSs do. State-of-the-art SDN topology protocols, hence, leave out LSs undetected resulting in suboptimal performance. This article presents a novel indirect controller to legacy forwarding (ICLF) scheme that sets out by the controller sending a solitary PACKET_OUT (P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">out</sub> ) to an OpenFlow device, which then percolates through the slow of OFSs and LSs; by exploiting the way OFSs and LSs react to the forwarded P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">out</sub> , the controller can discover the network topology, except for terminal LSs configurations if any. Experiments performed on Mininet shows that the proposed scheme shows an improvement amounted between 34.6%-97.9% in the number of ports detected and reduction in number of messages of about 25%-58.9% under varying degree of legacy device densities, albeit at a slightly overhead of messages forwarded.

A Novel Hybrid Secure Routing for Flying Ad-hoc Networks

The recent technology development increases the opportunity to create valuable network services to user. Flying ad-hoc networks (FANET) in one among them which evolved recently with enhanced value added services with common features similar to its predecessor ad-hoc networks like vehicular ad hoc networks (VANET) and mobile ad hoc networks (MANET). Due to its distinctive features FANETs are widely preferred in recent telecommunication services which requires high quality of services, efficiency, environment adaptability and scalability. In order to achieve high efficiency multiple aerial vehicles are used in general architectures. The Communication in such vehicles are progressed directly between the nodes or through relay nodes. Routing is an important process to establish a connection link between the nodes in the architecture. This research work proposed a routing strategy suitable for dynamic and static environments as a hybrid optimization model which reduces the issues in link establishment. Nature inspired bee colony optimization is used along with conventional routing algorithms such as optimized link state routing protocol and Dynamic Source Routing Protocol to improve the link discovery. The proposed optimized routing outperforms well in reduced delay and communication overhead of the network.

Single-shot link discovery for terahertz wireless networks

Of the many challenges in building a wireless network at terahertz frequencies, link discovery remains one of the most critical and least explored. In a network of mobile receivers using narrow directional beams, how do the nodes rapidly locate each other? This direction information is crucial for beam forming and steering, which are fundamental operations for maintaining link quality. As the carrier frequency increases into the terahertz range, the conventional methods used by existing networks become prohibitively time-consuming, so an alternative strategy is required. Using a leaky-wave antenna with a broadband transmitter, we demonstrate a single-shot approach for link discovery which can be accomplished much more rapidly. Our method relies on measurements of the width of a broad spectrum, and does not require any information about the phase of the received signal. This protocol, which relies on a detailed understanding of the radiation from leaky-wave devices, offers a realistic approach for enabling mobility in directional networks.

Characterization of Mollivirus kamchatka, the First Modern Representative of the Proposed Molliviridae Family of Giant Viruses.

Microbes trapped in permanently frozen paleosoils (permafrost) are the focus of increasing research in the context of global warming. Our previous investigations led to the discovery and reactivation of two Acanthamoeba-infecting giant viruses, Mollivirus sibericum and Pithovirus sibericum, from a 30,000-year old permafrost layer. While several modern pithovirus strains have since been isolated, no contemporary mollivirus relative was found. We now describe Mollivirus kamchatka, a close relative to M. sibericum, isolated from surface soil sampled on the bank of the Kronotsky River in Kamchatka, Russian Federation. This discovery confirms that molliviruses have not gone extinct and are at least present in a distant subarctic continental location. This modern isolate exhibits a nucleocytoplasmic replication cycle identical to that of M. sibericum Its spherical particle (0.6 μm in diameter) encloses a 648-kb GC-rich double-stranded DNA genome coding for 480 proteins, of which 61% are unique to these two molliviruses. The 461 homologous proteins are highly conserved (92% identical residues, on average), despite the presumed stasis of M. sibericum for the last 30,000 years. Selection pressure analyses show that most of these proteins contribute to virus fitness. The comparison of these first two molliviruses clarify their evolutionary relationship with the pandoraviruses, supporting their provisional classification in a distinct family, the Molliviridae, pending the eventual discovery of intermediary missing links better demonstrating their common ancestry.IMPORTANCE Virology has long been viewed through the prism of human, cattle, or plant diseases, leading to a largely incomplete picture of the viral world. The serendipitous discovery of the first giant virus visible under a light microscope (i.e., >0.3 μm in diameter), mimivirus, opened a new era of environmental virology, now incorporating protozoan-infecting viruses. Planet-wide isolation studies and metagenome analyses have shown the presence of giant viruses in most terrestrial and aquatic environments, including upper Pleistocene frozen soils. Those systematic surveys have led authors to propose several new distinct families, including the Mimiviridae, Marseilleviridae, Faustoviridae, Pandoraviridae, and Pithoviridae We now propose to introduce one additional family, the Molliviridae, following the description of M. kamchatka, the first modern relative of M. sibericum, previously isolated from 30,000-year-old arctic permafrost.

How Computation Is Helping Unravel the Dynamics of Morphogenesis

The growing availability of imaging data, calculation power, and algorithm sophistication are transforming the study of morphogenesis into a computation-driven discipline. In parallel, it has become clear that mechanics govern many processes that determine the cell fate map. High-resolution microscopy and image processing provide digital representations of embryos that facilitate quantifying their mechanics with computational methods. Moreover, innovations in in-vivo sensing and tissue manipulation can now characterize cell-scale processes to feed larger-scale representations. A variety of mechanical formalisms have been proposed to provide mechanistic insight about cellular biophysics and its links with biochemical and genetic factors. However, there are still limitations derived from the dynamic nature embryonic tissue and its spatio-temporal heterogeneity. In addition, the increasing complexity and variety of implementations makes it difficult to harmonize and cross-validate models. The solution to these challenges could come from integrating novel experimental measurements of embryonic mechanical properties into the models. Machine learning also has great potential to improve these limitations by identifying morphomechanical domains, i.e., spatio-temporally connected groups of cells with similar evolution, and determining their relationship with biomechanical events. Emerging, model-driven, deep learning architectures can facilitate the discovery of causal links in developmental processes, and at the same time, are becoming transparent and interpretable. We anticipate that these new tools will lead to multi-scale models with the necessary accuracy and flexibility to formulate hypotheses for in-vivo and in-silico testing. These methods have promising applications for tissue engineering, stem cell research, disease, and drug studies, and synthetic life.

Time and Space Efficient Large Scale Link Discovery using String Similarities

This paper proposes and evaluates time and space efficient methods for discovering links between matching entities in large data sets, using state of the art methods for measuring edit distance as a string similarity metric. The paper proposes and compares three filtering methods that build on a basic blocking technique to organize the target dataset, facilitating efficient pruning of dissimilar pairs. The proposed filtering methods are compared in terms of runtime and memory usage: The first method exploits the blocking structure using the triangle inequality in conjunction to the substring-matching criterion. The second method uses only the substring-matching criterion, while the third method uses the substring-matching criterion in conjunction to the frequency-matching criterion. Evaluation results show the pruning power of the different criteria used, also in comparison to the string matching functionality provided in LIMES and SILK, which are state-of-the-art tools for large-scale link discovery.

A novel collision aware network assisted device discovery scheme empowering massive D2D communications in 3GPP LTE-A networks

With the proliferation of proximity based services/applications, Device-to-Device (D2D) communication has become an emerging technology in facilitating data transfer among proximal cellular User Equipments (UEs). However one of the most challenging issues in D2D communication is the need for a robust device discovery scheme that effectively discovers UEs without any performance bottlenecks. In a densely populated cellular network, the conventional device discovery schemes proposed in the literature suffer from one or more adversities. High signaling overhead, large number of collisions and inefficient use of resources are some of the major issues that severely limit the performance of a device discovery scheme. Based on the characteristics of the network-assisted and autonomous device discovery schemes, we propose a novel device discovery scheme that discovers proximal UEs without causing any significant performance degradation. The cellular UEs are first divided into clusters based on their social relationship status and common interests. Every cluster will then execute a device discovery algorithm which is composed of two stages. In the first stage, UEs discover their neighbors using the conventional beacon based discovery scheme. The second stage comprises of successful UEs reporting to BS by initiating the LTE random access procedure. In this way, we not only reduce the signaling overhead but also enable substantial number of device discoveries to happen simultaneously at any instant of time. Further, we present a stochastic geometry based framework to analyze the collision probability and link discovery probability of the proposed scheme. Through extensive simulations we find that our proposed device discovery scheme effectively results in higher discovery probability compared to a recently proposed discovery scheme, by reducing the amount of collisions.

Link Discovery Attacks in Software-Defined Networks: Topology Poisoning and Impact Analysis

Software Defined Networking (SDN) has become a popular technology that offers advantages of programmable and flexible network management over the legacy practice. The centralized SDN controller is an important enabler of these benefits. One of the most crucial tasks of the SDN controller is link discovery as it provides topology of the network essential for the controller to direct or create rule forwarding and routing mechanisms. Much research on SDN security has been studied but only recently that security of OpenFlow link discovery protocols and topology poisoning have been addressed. Existing work includes link fabrication attacks via compromised hosts and defense systems with authentication. This paper discusses SDN link discovery process and its vulnerability to link discovery attacks including new attacks via compromised switches. We present a simple but effective defense mechanism using active ports that can detect both host-based and switch-based link discovery attacks. Finally, the paper presents an analytical and empirical analysis of the impacts of topology attacks on routing. The paper discusses attack details, proposed methods and results of these analyses.

It's Not All Sunshine: Non-sun-related Melanoma Risk-factors.

There is increasing evidence that the behaviour of naevi and melanoma is under significant genetic and/ or epigenetic control. Melanoma tumours behaves similarly all over the world. Many genes have now been implicated in melanoma risk and naevi number. Embryogenesis has also been important in the discovery of links between several neurological diseases and melanoma susceptibility. Telomere biology, which regulates cell senescence, is increasingly relevant in melanoma. Melanoma is often found in the context of family cancer syndromes and the identification of these families is important as screening for cancer will save lives. Melanoma is also one of the most immunogenic cancer as the behaviour of naevi and melanoma differ in patients with vitiligo or eczema. The search for non-sun-related melanoma risk factors should continue as it is likely to lead to important discoveries which will, in turn, have an impact on therapeutic targets for this tumour.

P4-MACsec: Dynamic Topology Monitoring and Data Layer Protection With MACsec in P4-Based SDN

We propose P4-MACsec to protect network links between P4 switches through automated deployment of MACsec, a widespread IEEE standard for securing Layer 2 infrastructures. It is supported by switches and routers from major manufacturers and has only little performance limitations compared to VPN technologies such as IPsec. P4-MACsec introduces a data plane implementation of MACsec including AES-GCM encryption and decryption directly on P4 switches. P4-MACsec features a two-tier control plane structure where local controllers running on the P4 switches interact with a central controller. We propose a novel secure link discovery mechanism that leverages protected LLDP frames and the two-tier control plane structure for secure and efficient management of a global link map. Automated deployment of MACsec creates secure channel, generates keying material, and configures the P4 switches for each detected link between two P4 switches. It detects link changes and performs rekeying to provide a secure, configuration-free operation of MACsec. In this paper, we review the technological background of P4-MACsec and explain its architecture. To demonstrate the feasibility of P4-MACsec, we implement it on the BMv2 P4 software switch and validate the prototype through experiments. We evaluate its performance through experiments that focus on TCP throughput and round-trip time. We publish the prototype and experiment setups on Github.

Margin Boost Clustering based Multivariate Dolphin Swarm Optimization for Routing and Reliable Data Dissemination in VANET

VANET is the Vehicular Ad hoc NETworks where the vehicle nodes communicate to others without any fixed infrastructure. Due to frequent topology changes, VANET suffers from challenges like routing and data dissemination between the vehicles. This research work develops an efficient technique called Mean shift Margin Boost Clustering Based Multivariate Dolphin Swarm Optimized Routing (MMBC-MDSOR) technique for improving the routing and reliable data dissemination in VANET. A mean-shift margin boost clustering is an ensemble clustering technique to divide the total network into a number of groups. Each group comprises the number of vehicle nodes by the ensemble method uses the iterative Gaussian kernelized mean shifted clustering technique to assign each vehicle towards the closest cluster centroid based on the different stability parameters such as vehicles density, direction, distance and velocity to form a cluster head. By using cluster head, the data communication is controlled between vehicle nodes as well as the end to end delay is reduced. Multivariate Dolphin Swarm Optimized Routing (MDSOR) is the cluster based optimization to select the optimal cluster head based on fitness function in terms of distance, signal strength and bandwidth. Then the optimal route path between source to destination is identified to disseminate the messages. The designed MDSOR in MMBC-MDSOR increases the reliability, throughput and minimizes packet drop ratio. The above technique can be applied wherever there is high congestion on the road due to the failure of optimal link discovery and data distribution between the vehicles in emergency condition. The simulation result shows that the MMBC-MDSOR Technique can enhance the reliability, throughput and also minimizes the end to end delay and packet drop ratio in VANET as compared to state-of-the-art works.

Repression of ferritin light chain translation by human eIF3.

A central problem in human biology remains the discovery of causal molecular links between mutations identified in genome-wide association studies (GWAS) and their corresponding disease traits. This challenge is magnified for variants residing in non-coding regions of the genome. Single-nucleotide polymorphisms (SNPs) in the 5' untranslated region (5'-UTR) of the ferritin light chain (FTL) gene that cause hyperferritinemia are reported to disrupt translation repression by altering iron regulatory protein (IRP) interactions with the FTL mRNA 5'-UTR. Here, we show that human eukaryotic translation initiation factor 3 (eIF3) acts as a distinct repressor of FTL mRNA translation, and eIF3-mediated FTL repression is disrupted by a subset of SNPs in FTL that cause hyperferritinemia. These results identify a direct role for eIF3-mediated translational control in a specific human disease.

Transfer entropy on symbolic recurrences.

Recurrence quantification analysis offers a powerful framework to investigate complexity in dynamical systems. While several studies have demonstrated the possibility of multivariate recurrence quantification analysis, information-theoretic tools for the discovery of causal links remain elusive. Particularly enticing is to formulate information-theoretic tools on symbolic recurrence plots, which alleviate some of the methodological challenges of traditional recurrence plots and offer a richer representation of recurrences. Toward this aim, we establish a probability space in which we ground a theory of information that encodes information in the recurrences of the symbols. We introduce transfer entropy on symbolic recurrences as a tool to guide the inference of the strength and direction of the interaction between dynamical systems. We demonstrate statistically reliable discovery of causal links on synthetic and experimental time series, from only two time series or a larger dataset with multiple realizations. The proposed approach brings together recurrence plots, information theory, and symbolic dynamics to empower researchers and practitioners with effective means to visualize and quantify interactions in dynamical systems.

GOF/LOF knowledge inference with tensor decomposition in support of high order link discovery for gene, mutation and disease.

For discovery of new usage of drugs, the function type of their target genes plays an important role, and the hypothesis of "Antagonist-GOF" and "Agonist-LOF" has laid a solid foundation for supporting drug repurposing. In this research, an active gene annotation corpus was used as training data to predict the gain-of-function or loss-of-function or unknown character of each human gene after variation events. Unlike the design of(entity, predicate, entity) triples in a traditional three way tensor, a four way and a five way tensor, GMFD-/GMAFD-tensor, were designed to represent higher order links among or among part of these entities: genes(G), mutations(M), functions(F), diseases( D) and annotation labels(A). A tensor decomposition algorithm, CP decomposition, was applied to the higher order tensor and to unveil the correlation among entities. Meanwhile, a state-of-the-art baseline tensor decomposition algorithm, RESCAL, was carried on the three way tensor as a comparing method. The result showed that CP decomposition on higher order tensor performed better than RESCAL on traditional three way tensor in recovering masked data and making predictions. In addition, The four way tensor was proved to be the best format for our issue. At the end, a case study reproducing two disease-gene-drug links(Myelodysplatic Syndromes-IL2RA-Aldesleukin, Lymphoma- IL2RA-Aldesleukin) presented the feasibility of our prediction model for drug repurposing.

SLDP: A secure and lightweight link discovery protocol for software defined networking

In Software Defined Networks (SDNs), the global view of the underlying network topology is created and maintained at the logically centralized controller. SDN achieves it by decoupling the data plane from the control plane. The up-to-date global view at SDN controller enables the applications (running on top of it) to innovate through dynamic network programmability. To establish a global view, a controller needs to discover a physical topology of the underlying SDN network infrastructure, which is challenging due to various reasons such as the lack of SDN protocols standardization and authentication mechanisms, use of sub-optimal link discovery protocols (e.g., OFDP and LLDP), dynamic topology due to movement of virtualized data centers, switches, and multi-tenant cloud networks, and lack of integration of security schemes for the topology discovery.In this paper, we propose a SDN Link Discovery Protocol (SLDP) for efficient discovery and extraction of topology information in SDN networks. The design of SLDP is motivated from the need of a secure, lightweight, and efficient link discovery protocol in SDN. SLDP aims to prevent, detect, and mitigate various security threats such as poison, replay, and flooding attacks, which are due to lack of source authentication, lack of packet integrity checks, and reuse of static packets. SLDP creates and maintains the global network topology at SDN controller by using smaller size and lower number of SLDP packets during the topology discovery process. Thus, it significantly minimizes the topology discovery overhead in the network. We implemented SLDP on Mininet emulator, and the results show the effectiveness and correctness of SLDP concerning topology discovery time, CPU computational time, and bandwidth overheads, when compared with the traditional OpenFlow Link Discovery Protocol (OFDP). Additionally, SLDP successfully prevent, detect, and mitigate various attacks (e.g., poison, replay, and flooding) in different SDN scenarios.