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The Web of Data has been fueled significantly by geospatial data over the last few years. In the current link discovery frameworks, there is still a lack of robust support for finding geospatial-aware links between geospatial data sources in the Web of Data. They are also limited in efficient association capabilities for large-scale datasets. This paper extends the data integration capability based on the spatial metrics in the open geospatial engine OGE. These metrics include topological relationships and spatial matching between geospatial entities within multiple geospatial data sources. Thus, the tool can be employed by data publishers to set geospatial-aware links to facilitate geospatial data and knowledge discovery in the Web of Data. Several geospatial data sources are used to demonstrate the usability and effectiveness of the approach and tool implementation. [ABSTRACT FROM AUTHOR]

Knowledge graphs (KGs) that follow the Linked Data principles are created daily. However, there are no holistic models for the Linked Open Data (LOD). Building these models(i.e., engineering a pipeline system) is still a big challenge in order to make the LOD vision comes true. In this paper, we address this challenge by presenting NELLIE, a pipeline architecture to build a chain of modules, in which each of our modules addresses one data augmentation challenge. The ultimate goal of the proposed architecture is to build a single fused knowledge graph out of the LOD. NELLIE starts by crawling the available knowledge graphs in the LOD cloud. It then finds a set of matching KG pairs. NELLIE uses a two-phase linking approach for each pair (first an ontology matching phase, then an instance matching phase). Based on the ontology and instance matching, NELLIE fuses each pair of knowledge graphs into a single knowledge graph. The resulting fused KG is then an ideal data source for knowledge-driven applications such as search engines, question answering, digital assistants and drug discovery. Our evaluation shows an improved $Hit \text{@} 1$ score of the link prediction task on the resulting fused knowledge graph by NELLIE in up to 94.44% of the cases. Our evaluation also shows a runtime improvement by several orders of magnitude when comparing our two-phases linking approach with the estimated runtime of linking using a naïve approach.

One of the main obstacles in publishing in a Linked Data way is to connect the dataset being published externally with related data sources in the cloud, known as Data Interlinking. This paper proposes LinkD, a new element-based interlinking approach. LinkD interlinks an RDF dataset, resulted from transformed semi-structured data, with its counterparts in the web of Linked Data. To provide similarity links, the existence of published data in the Linked Data cloud is done in the first place. Different algorithms for similarity measurement are employed while the domain of the dataset being interlinked is taken into account. The techniques utilised allow the processing of a large number of Linked Data datasets. The evaluation of LinkD shows high precision, recall and performance. [ABSTRACT FROM AUTHOR]

The era of the web has evolved and the industry strives to work better every day, the constant need for data to be accessible at a random moment is expanding, and with this expansion, the need to create a meaningful query technique in the web is a major concerns. To transmit meaningful data or rich semantics, machines/ projects need to have the ability to reach the correct information and make adequate connections, this problem is addressed after the emergence of Web 3.0, the semantic web is developing and being collected an immense. Information to prepare, this passes the giant data management test, to provide an ideal result at any time needed. Accordingly, in this article, we present an ideal system for managing huge information using MapReduce structures that internally help an engine bring information using the strength of fair preparation using smaller map occupations and connection disclosure measures. Calculations for similarity can be challenging, this work performs five similarity detection algorithms and determines the time it takes to address the patterns that has to be a better choice in the calculation decision. The proposed framework is created using the most recent and widespread information design, that is, the JSON design, the HIVE query language to obtain and process the information planned according to the customer's needs and calculations for the disclosure of the interface. Finally, the results on a web page is made available that helps a user stack json information and make connections somewhere in the range of dataset 1 and dataset 2. The results are examined in 2 different sets, the results show that the proposed approach helps to interconnect significantly faster; Regardless of how large the information is, the time it takes is not radically extended. The results demonstrate the interlinking of the dataset 1 and dataset 2 is most notable using LD and JW, the time required is ideal in both calculations, this paper has mechanized the method involved with interconnecting via a web page, where customers can merge two sets of data that should be associated and used. [ABSTRACT FROM AUTHOR]

In this article, we propose and demonstrate a spectrum-to-space mapping principle for localizing multiple wireless nodes in a simultaneous and single-shot fashion at terahertz (THz) frequencies. Spectrum-to-space mapping is achieved through two dual-port chip integrated waveguide (CIW)-based leaky-wave antennas (LWAs). Interfacing with the two LWAs, we integrate two transmitting and receiving chains operating between 360- and 400-GHz range in a single chip realized in a 65-nm bulk CMOS process. Utilizing the carefully engineered dispersive nature of the LWA and its frequency-dependent radiation patterns, we create unique spectrum-to-space calibration maps for both 1-D and 2-D angular localizations. The measured one-shot localization error variance is less than 1° in 1-D space with 200-Hz-resolution bandwidth (BW) and less than 2° in 2-D space for a 20-Hz-resolution BW. The high-resolution nature of the localization principle in a single-shot fashion makes this approach attractive for multi-wireless node localization, link discovery, beam-forming, beam-management, and beam-optimization methods. [ABSTRACT FROM AUTHOR]

Geospatial data constitutes a considerable part of Semantic Web data, but so far, its sources are inadequately interlinked in the Linked Open Data cloud. Geospatial Interlinking aims to cover this gap by associating geometries with topological relations like those of the Dimensionally Extended 9-Intersection Model. Due to its quadratic time complexity, various algorithms aim to carry out Geospatial Interlinking efficiently. We present JedAI-spatial , a novel, open-source system that organizes these algorithms according to three dimensions: (i) Space Tiling , which determines the approach that reduces the search space, (ii) Budget-awareness , which distinguishes interlinking algorithms into batch and progressive ones, and (iii) Execution mode , which discerns between serial algorithms, running on a single CPU-core, and parallel ones, running on top of Apache Spark. We analytically describe JedAI-spatial's architecture and capabilities and perform thorough experiments to provide interesting insights about the relative performance of its algorithms. [ABSTRACT FROM AUTHOR]

Linked data has been widely recognized as an important paradigm for representing data and one of the most important aspects of supporting its use is discovery of links between datasets. For many datasets, there is a significant amount of textual information in the form of labels, descriptions and documentation about the elements of the dataset and the fundament of a precise linking is in the application of semantic textual similarity to link these datasets. However, most linking tools so far rely on only simple string similarity metrics such as Jaccard scores. We present an evaluation of some metrics that have performed well in recent semantic textual similarity evaluations and apply these to linking existing datasets.

A central problem in the context of the Web of Data as well as in data integration in general is to identify entities in different data sources that describe the same real-world object. There exists a large body of research on entity resolution. Interestingly, most of the existing research focuses on entity resolution on dense data, meaning data that does not contain too many missing values. This paper sets a different focus and explores learning expressive linkage rules from as well as applying these rules to sparse data, i.e. data exhibiting a large amount of missing values. Sparse data is a common challenge in application domains such as e-commerce, online hotel booking, or online recruiting. We propose and compare three entity resolution methods that employ genetic programming to learn expressive linkage rules from sparse data. First, we introduce the GenLinkGL algorithm which learns groups of matching rules and applies specific rules out of these groups depending on which values are missing from a pair of records. Next, we propose GenLinkSA, which employs selective aggregation operators within rules. These operators exclude misleading similarity scores (which result from missing values) from the aggregations, but on the other hand also penalize the uncertainty that results from missing values. Finally, we introduce GenLinkComb, an algorithm which combines the central ideas of the previous two into one integrated method. We evaluate all methods using six benchmark datasets: three of them are e-commerce product datasets, the other datasets describe restaurants, movies, and drugs. We show improvements of up to 16% F-measure compared to handwritten rules, on average 12% F-measure improvement compared to the original GenLink algorithm, 15% compared to EAGLE, 8% compared to FEBRL, and 5% compared to CoSum-P. [ABSTRACT FROM AUTHOR]

This paper proposes and evaluates time and space efficient methods for matching entities in large data sets based on effectively pruning the candidate pairs to be matched, using 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 data set, facilitating efficient pruning of dissimilar pairs. The proposed filtering methods are compared in terms of runtime and memory usage: the first method clusters entities and exploits the triangle inequality using the string similarity metric, in conjunction to the substring matching filtering rule. The second method uses only the substring matching rule, while the third method uses the substring matching rule in conjunction to the character frequency matching filtering rule. Evaluation results show the pruning power of the different filtering methods used, also in comparison to the string matching functionality provided in LIMES and SILK, which are state of the art frameworks for large scale link discovery. [ABSTRACT FROM AUTHOR]

Traditional network architecture is inflexible and complicated. This observation has led to a paradigm shift towards software-defined networking (SDN), where network management level is separated from data forwarding level. This change was made possible by control plane transfer from the switching equipment to software modules that run on a dedicated server, called the controller (or network operating system), or network applications, that work with this controller. Methods of representation, storage and communication interfaces with network topology elements are the most important aspects of network operating systems available to SDN user because performance of some key controller modules is heavily dependent on internal representation of the network topology. Notably, firewall and routing modules are examples of such modules. This article describes the methods used for presentation and storage of network topologies, as well as interface to the corresponding Floodlight modules. An alternative algorithm has been suggested and developed for message exchange conveying network topology alterations between the controller and network applications. Proposed algorithm makes implementation of module alerting based on subscription to the relevant events. API for interaction between controller and network applications has been developed. This algorithm and API formed the base for Topology Tracker module capable to inform network applications about the changes that had occurred in the network topology and also stores compact representation of the network to speed up the interaction process.

One of the major issues encountered in the generation of knowledge bases is the integration of data coming from a collection of heterogeneous data sources. A key essential task when integrating data instances is the entity matching. Entity matching is based on the definition of a similarity measure among entities and on the classification of the entity pair as a match if the similarity exceeds a certain threshold. This parameter introduces a trade-off between the precision and the recall of the algorithm, as higher values of the threshold lead to higher precision and lower recall, and lower values lead to higher recall and lower precision. In this paper, we propose a stacking approach for threshold-based classifiers. It runs several instances of classifiers corresponding to different thresholds and use their predictions as a feature vector for a supervised learner. We show that this approach is able to break the trade-off between the precision and recall of the algorithm, increasing both at the same time and enhancing the overall performance of the algorithm. We also show that this hybrid approach performs better and is less dependent on the amount of available training data with respect to a supervised learning approach that directly uses properties' similarity values. In order to test the generality of the claim, we have run experimental tests using two different threshold-based classifiers on two different data sets. Finally, we show a concrete use case describing the implementation of the proposed approach in the generation of the 3cixty Nice knowledge base. [ABSTRACT FROM AUTHOR]

Large amounts of geo-spatial information have been made available with the growth of the Web of Data. While discovering links between resources on the Web of Data has been shown to be a demanding task, discovering links between geo-spatial resources proves to be even more challenging. This is partly due to the resources being described by the means of vector geometry. Especially, discrepancies in granularity and error measurements across data sets render the selection of appropriate distance measures for geo-spatial resources difficult. In this paper, we survey existing literature for point-set measures that can be used to measure the similarity of vector geometries. We then present and evaluate the ten measures that we derived from literature. We evaluate these measures with respect to their time-efficiency and their robustness against discrepancies in measurement and in granularity. To this end, we use samples of real data sets of different granularity as input for our evaluation framework. The results obtained on three different data sets suggest that most distance approaches can be led to scale. Moreover, while some distance measures are significantly slower than other measures, distance measure based on means, surjections and sums of minimal distances are robust against the different types of discrepancies. [ABSTRACT FROM AUTHOR]

Software-defined networking simplifies network management by decoupling the control plane from the data plane and centralizing it to the controller. As the brain of the network, the controller gains up-to-date holistic network visibility via topology discovery. However, as a key service of topology discovery, the link discovery service opens problems on efficiency and security. On the one hand, sending link discovery packets to all ports wastes not only the limited controller resources (such as CPU and memory) but also control channel bandwidth. On the other hand, attackers may use these packets to create fake links and perform link fabrication attack. Because of the centralized control paradigm, wasting controller resources may degrade network performance, and all the fake links may severely poison the network topology, even causing the denial of service or man-in-the-middle attack. In this paper, we propose an efficient and secure link discovery scheme to improve link discovery performance and resist link fabrication attack caused by the software-defined networking link discovery service. By adopting port classification technique and directionally transmitting packets to appropriate ports, our approach can reduce or eliminate redundant packets and improve link discovery performance. Meanwhile, we adopt the directional packet transmitting approach and the time-marked hash-based message authentication code authenticate scheme to resist the link fabrication attack. A prototype system is implemented on the basis of POX controller and Mininet simulator to evaluate our scheme. Simulation results demonstrate that our scheme can solve the link fabrication problems with less overload of both the control plane and the data plane. [ABSTRACT FROM AUTHOR]

Abstract Link discovery is central to the integration and use of data across RDF knowledge bases. Geospatial information is increasingly represented according to the Linked Data principles. Resources within such datasets are described by means of vector geometry, where link discovery approaches have to deal with millions of point sets consisting of billions of points. In this paper, we study the effect of simplifying the resources' geometries on runtime and F-measure of link discovery approaches. In particular, we evaluate link discovery approaches for computing the point-set distances as well as the topological relations among RDF resources with geospatial representation. The results obtained on two different real datasets suggest that most geospatial link discovery approaches achieve up to 67x speedup using simplification, while the average loss in their F-measure is less than 15%. Our implementation is open-source and available at http://github.com/dice-group/limes. [ABSTRACT FROM AUTHOR]

Objective: The goal of this work is to map Unified Medical Language System (UMLS) concepts to DBpedia resources using widely accepted ontology relations from the Simple Knowledge Organization System (skos:exactMatch, skos:closeMatch) and from the Resource Description Framework Schema (rdfs:seeAlso), as a result of which a complete mapping from UMLS (UMLS 2016AA) to DBpedia (DBpedia 2015-10) is made publicly available that includes 221 690 skos:exactMatch, 26 276 skos:closeMatch, and 6 784 322 rdfs:seeAlso mappings.Methods: We propose a method called circular resolution that utilizes a combination of semantic annotators to map UMLS concepts to DBpedia resources. A set of annotators annotate definitions of UMLS concepts returning DBpedia resources while another set performs annotation on DBpedia resource abstracts returning UMLS concepts. Our pipeline aligns these 2 sets of annotations to determine appropriate mappings from UMLS to DBpedia.Results: We evaluate our proposed method using structured data from the Wikidata knowledge base as the ground truth, which consists of 4899 already existing UMLS to DBpedia mappings. Our results show an 83% recall with 77% precision-at-one (P@1) in mapping UMLS concepts to DBpedia resources on this testing set.Conclusions: The proposed circular resolution method is a simple yet effective technique for linking UMLS concepts to DBpedia resources. Experiments using Wikidata-based ground truth reveal a high mapping accuracy. In addition to the complete UMLS mapping downloadable in n-triple format, we provide an online browser and a RESTful service to explore the mappings. [ABSTRACT FROM AUTHOR]

Over the last years, time-efficient approaches for the discovery of links between knowledge bases have been regarded as a key requirement towards implementing the idea of a Data Web. Thus, efficient and effective measures for comparing the labels of resources are central to facilitate the discovery of links between datasets on the Web of Data as well as their integration and fusion. We present a novel time-efficient implementation of filters that allow for the efficient execution of bounded Jaro- Winkler measures. We evaluate our approach on several datasets derived from DBpedia 3.9 and LinkedGeoData and containing up to 106 strings and show that it scales linearly with the size of the data for large thresholds. Moreover, we also show that our approach can be easily implemented in parallel. We also evaluate our approach against SILK and show that we outperform it even on small datasets. [ABSTRACT FROM AUTHOR]

Traditional network architecture is inflexible and complex. This observation has led to a paradigm shift toward software-defined networks (SDNs), in which the network control level is separated from the data link layer. This change became possible because of the control plane transfer from switching equipment to software modules that run on a dedicated server called a controller (or a network operating system) or to network applications that work with this controller. Methods of presentation, storage, and communication interfaces with network topology elements available to SDN controller users are the most important aspects of network operating systems because the operation of some key controller modules depends heavily on the internal representation of the network topology. Firewall and routing modules can be cited as examples of these modules. This paper considers the methods used to represent and store the network topology, as well as communication interfaces with corresponding modules of the Floodlight controller. An alternative algorithm for exchanging messages on the changes in the network topology between the controller and network applications has been proposed and developed. The algorithm makes it possible to issue notifications based on a subscription to relevant events. An API for the module of interacting with applied programs of the SDN controller has been developed. The Topology Tracker module has been designed based on this algorithm and API. In active mode, this module can inform network applications about the changes in the network topology and store its compact representation for the interaction acceleration. [ABSTRACT FROM AUTHOR]

Creating links among online encyclopedia articles in different languages is crucial in the construction and integration of large multilingual knowledge bases. Most research to date has focused on linking among different language versions of Wikipedia, yet other large online encyclopedias in a variety of languages exist. In this work, we present a cross-language article-linking method using a bilingual topic model and translation features based on an SVM model to link articles in English Wikipedia and Chinese Baidu Baike, the most widely used Wiki-like encyclopedia in China. To evaluate our approach, we compile data sets from Baidu Baike articles and their corresponding English Wikipedia articles. The evaluation results show that our approach achieves at most 0.8158 in MRR, outperforming the baseline system by 0.1328 (+19.44%) in MRR. Our method does not heavily depend on linguistic characteristics, and it can be easily extended to generate cross-language article links among different online encyclopedias in other languages. [ABSTRACT FROM AUTHOR]

Today, full-texts of scientific articles are often stored in different locations than the used datasets. Dataset registries aim at a closer integration by making datasets citable but authors typically refer to datasets using inconsistent abbreviations and heterogeneous metadata (e.g. title, publication year). It is thus hard to reproduce research results, to access datasets for further analysis, and to determine the impact of a dataset. Manually detecting references to datasets in scientific articles is time-consuming and requires expert knowledge in the underlying research domain. We propose and evaluate a semi-automatic three-step approach for finding explicit references to datasets in social sciences articles. We first extract pre-defined special features from dataset titles in the da∣ra registry, then detect references to datasets using the extracted features, and finally match the references found with corresponding dataset titles. The approach does not require a corpus of articles (avoiding the cold start problem) and performs well on a test corpus. We achieved an F-measure of 0.84 for detecting references in full-texts and an F-measure of 0.83 for finding correct matches of detected references in the da∣ra dataset registry. [ABSTRACT FROM AUTHOR]

The GESIS Data Catalogue contains the study descriptions for all archived studies at GESIS, currently more than 5000 datasets mainly from survey research in the social sciences. These descriptions include information about primary researchers, research topics and objects, used methods, and the resulting dataset, which is mainly used for archiving and retrieval in order to serve secondary researchers. For this purpose the existing metadata can be enriched with further information about the study investigators, involved affiliations, collection dates, content, and more from other sources like DBpedia or the Name Authority File of the German National Library. In recent years the paradigm of Linked Open Data (LOD) encouraged various research organizations to expose their data to the web according to Semantic Web standards. This has increased the number of available data sources and the feasibility of their reuse. [ABSTRACT FROM AUTHOR]