The Semantic Web 10

The Semantic Web promises to open innumerable opportunities for automation and information retrieval by standardizing the protocols for metadata exchange. However, just as the success of the World Wide Web can be attributed to the ease of use and ubiquity of Web browsers, we believe that the unfolding of the Semantic Web vision depends on users getting powerful but easy-to-use tools for managing their information. But unlike HTML, which can be easily edited in any text editor, RDF is more complicated to author and does not have an obvious presentation mechanism. Previous work has concentrated on the ideas of generic RDF graph visualization and RDF Schema-based form generation. In this paper, we present a comprehensive platform for constructing end user applications that create, manipulate, and visualize arbitrary RDF-encoded information, adding another layer to the abstraction cake. We discuss a programming environment specifically designed for manipulating RDF and introduce user interface concepts on top that allow the developer to quickly assemble applications that are based on RDF data models. Also, because user interface specifications and program logic are themselves describable in RDF, applications built upon our framework enjoy properties such as network updatability, extensibility, and end user customizability - all desirable characteristics in the spirit of the Semantic Web.

Haystack: A Platform for Authoring End User Semantic Web Applications.

In the recent years, several approaches for machine learning on the Semantic Web have been proposed. However, no extensive comparisons between those approaches have been undertaken, in particular due to a lack of publicly available, acknowledged benchmark datasets. In this paper, we present a collection of 22 benchmark datasets of different sizes. Such a collection of datasets can be used to conduct quantitative performance testing and systematic comparisons of approaches.

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A collection of benchmark datasets for systematic evaluations of machine learning on the Semantic Web

Knowledge areas, themes and future research on open data: A co-word analysis

How to understand individual human actions is a fundamental question to modern science, which drives and incurs many social, technological, racial, religious and economic phenomena. Human dynamics tries to reveal the temporal pattern and internal mechanism of human actions in letter or electronic communications, from the perspective of continuous interactions among friends or acquaintances. For interactions between stranger to stranger, taxi industry provide fruitful phenomina and evidence to investigate the action decisions. In fact, one striking disturbing events commonly reported in taxi industry is passenger refusing or denial, whose reasons vary, including skin color, blind passenger, being a foreigner or too close destination, religion reasons and anti specific nationality, so that complaints about taxi passenger refusing have to be concerned and processed carefully by local governments. But more universal factors for this phenomena are of great significance, which might be fulfilled by big data research to obtain novel insights in this question. In this paper, we demonstrate the big data analytics application in revealing novel insights from massive taxi trace data, which, for the first time, validates the passengers denial in taxi industry and estimates the denial ratio in Beijing city. We first quantify the income differentiation facts among taxi drivers. Then we find out that choosing the drop-off places also contributes to the high income for taxi drivers, compared to the previous explanation of mobility intelligence. Moreover, we propose the pick-up, drop-off and grid diversity concepts and related diversity analysis suggest that, high income taxi drivers will deny passengers in some situations, so as to choose the passengers’ destination they prefer. Finally we design an estimation method for denial ratio and infer that high income taxi drivers will deny passengers with 8.52% likelihood in Beijing. Our work exhibits the power of big data analysis in revealing some dark side investigation.

https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0165597&type=printable

Inferring Passenger Denial Behavior of Taxi Drivers from Large-Scale Taxi Traces.

Drug product data is available on the Web in a distributed fashion. The reasons lie within the regulatory domains, which exist on a national level. As a consequence, the drug data available on the Web are independently curated by national institutions from each country, leaving the data in varying languages, with a varying structure, granularity level and format, on different locations on the Web. Therefore, one of the main challenges in the realm of drug data is the consolidation and integration of large amounts of heterogeneous data into a comprehensive dataspace, for the purpose of developing data-driven applications. In recent years, the adoption of the Linked Data principles has enabled data publishers to provide structured data on the Web and contextually interlink them with other public datasets, effectively de-siloing them. Defining methodological guidelines and specialized tools for generating Linked Data in the drug domain, applicable on a global scale, is a crucial step to achieving the necessary levels of data consolidation and alignment needed for the development of a global dataset of drug product data. This dataset would then enable a myriad of new usage scenarios, which can, for instance, provide insight into the global availability of different drug categories in different parts of the world. We developed a methodology and a set of tools which support the process of generating Linked Data in the drug domain. Using them, we generated the LinkedDrugs dataset by seamlessly transforming, consolidating and publishing high-quality, 5-star Linked Drug Data from twenty-three countries, containing over 248,000 drug products, over 99,000,000 RDF triples and over 278,000 links to generic drugs from the LOD Cloud. Using the linked nature of the dataset, we demonstrate its ability to support advanced usage scenarios in the drug domain. The process of generating the LinkedDrugs dataset demonstrates the applicability of the methodological guidelines and the supporting tools in transforming drug product data from various, independent and distributed sources, into a comprehensive Linked Drug Data dataset. The presented user-centric and analytical usage scenarios over the dataset show the advantages of having a de-siloed, consolidated and comprehensive dataspace of drug data available via the existing infrastructure of the Web.

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Consolidating drug data on a global scale using Linked Data

Food - drug interactions are well studied, however much less is known about cuisine - drug interactions. Non-native cuisines are becoming increasingly more popular as they are available in (almost) all regions in the world. Here we address the problem of how known negative food - drug interactions are spread in different cuisines. We show that different drug categories have different distribution of the negative effects in different parts of the world. The effects certain ingredients have on different drug categories and in different cuisines are also analyzed. This analysis is aimed towards stressing out the importance of cuisine - drug interactions for patients which are being administered drugs with known negative food interactions. A patient being under a treatment with one such drug should be advised not only about the possible negative food - drug interactions, but also about the cuisines that could be avoided from the patient's diet.

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Inferring Cuisine - Drug Interactions Using the Linked Data Approach

These days, the number of data sources an ordinary computer user works with every day is very large and continues to grow. With the increasing number of cloud services with specialized functionalities, the users are faced with the necessity to routinely perform manual actions to interchange data among different cloud and web services, in order to perform more complex and composite actions. These actions always require a certain amount of dedicated time from the user, who has to change the context in which he work, in order to take the actions and transfer data from one system to another. In this paper, we present a software platform, based on the concepts and technologies of the Semantic Web, which provides the users with a unified and simple composite approach to the different services they use, and crates a simple flow of information from one infrastructure to another. The system is able to automatically discover the context in which the user is working, and offer him the actions which can be used over the data within the context. In this way, the user can completely focus on his tasks in his work environment, and get relevant information and possible actions in that context. This system automates the execution of the users' tasks, which leads to improvements in their productivity, information exchange and efficiency. The system is called "Semantic Sky" and represents a platform where many cloud services are interconnected by the use of semantic web technologies.

Semantic sky: a platform for cloud service integration based on semantic web technologies

Synthetic datasets used in benchmarking need to mimic all characteristics of real-world datasets, in order to provide realistic benchmarking results. Synthetic RDF datasets usually show a significant discrepancy in the level of structuredness compared to real-world RDF datasets. This structural difference is important as it directly affects storage, indexing and querying. In this paper, we show that the synthetic RDF dataset used in the Social Network Benchmark is characterized with high-structuredness and therefore introduce modifications to the data generator so that it produces an RDF dataset with a real-world structuredness.

/pdf/an-rdf-dataset-generator-for-the-social-network-benchmark-4rxclsykra.pdf

An RDF Dataset Generator for the Social Network Benchmark with Real-World Coherence.

One of the most active fields of research in the past decade has been data representation, storage and retrieval. With the vast amount of data available on the Web, this field has initiated the development of data management techniques for distributed datasets over the existing infrastructure of the Web. The Linked Data paradigm is one of them, and it aims to provide common practices for publishing and linking data on the Web with the use of Semantic Web technologies. This allows for a transformation of the Web from a web of documents, to a web of data. With this, the Web becomes a distributed network for data access, usable by software agents and machines. The interlinked nature of the distributed datasets provides new use-case scenarios for the end users, scenarios which are unavailable over isolated datasets. In this paper, we are describing the process of generating Linked Open Data from the public data of the Health Insurance Fund along with data from the Associated Pharmacies of Macedonia. With this we generate and publish an interlinked RDF dataset in a machine-readable format. We also provide examples of newly available use-case scenarios which exploit the Linked Data format of the data. These use-cases can be used by applications and services for providing relevant information to the end-users.

Milos Jovanovik

Papers

Consolidating drug data on a global scale using Linked Data

Inferring Cuisine - Drug Interactions Using the Linked Data Approach

Semantic sky: a platform for cloud service integration based on semantic web technologies

An RDF Dataset Generator for the Social Network Benchmark with Real-World Coherence.

Linked Open Data for Medical Institutions and Drug Availability Lists in Macedonia