Petr Škoda

To achieve interoperability for effective data exchange on the web, we need a contract. Depending on the field, we may refer to technical data schemas, web vocabularies, or, more generally, to data specifications (DSes). The development and management of these DSes can become difficult in complex domains with multiple stakeholders and related DSes involved. In this article, we present Dataspecer, an open-source, modular web application for the development of semantic data specifications (SDSes), DSes that target the semantic and technical layers of data exchange. Dataspecer allows users to design web vocabularies and their application profiles, maintaining relations between reused concepts and their original SDSes. Furthermore, Dataspecer assists users in the creation of technical artifacts such as schemas for JSON or XML, while maintaining consistency of the artifacts with the application profiles. We motivate the need for SDSes and derive requirements for such a tool. In case studies based on the ecosystem of Data Catalog Vocabulary-based specifications, we demonstrate that SDSes created in Dataspecer meet these requirements and are of higher quality. We show SDSes that were created directly in Dataspecer, and in the evaluation section, we argue that using our tool is more efficient than creating them manually, even for smaller domains.

Purpose: Semantic retrieval and discovery of datasets published as open data remains a challenging task. The datasets inherently originate in the globally distributed web jungle, lacking the luxury of centralized database administration, database schemes, shared attributes, vocabulary, structure and semantics. The existing dataset catalogs provide basic search functionality relying on keyword search in brief, incomplete or misleading textual metadata attached to the datasets. The search results are thus often insufficient. However, there exist many ways of improving the dataset discovery by employing content-based retrieval, machine learning tools, third-party (external) knowledge bases, countless feature extraction methods and description models and so forth.Design/methodology/approach: In this paper, the authors propose a modular framework for rapid experimentation with methods for similarity-based dataset discovery. The framework consists of an extensible catalog of components prepared to form custom pipelines for dataset representation and discovery.Findings: The study proposes several proof-of-concept pipelines including experimental evaluation, which showcase the usage of the framework.Originality/value: To the best of authors’ knowledge, there is no similar formal framework for experimentation with various similarity methods in the context of dataset discovery. The framework has the ambition to establish a platform for reproducible and comparable research in the area of dataset discovery. The prototype implementation of the framework is available on GitHub.

There is a large number of datasets published as Linked (Open) Data (LOD/LD). At the same time, there is also a multitude of tools for publication of LD. However, potential LD consumers still have difficulty discovering, accessing and exploiting LD.

PrankWeb is an online resource providing an interface to P2Rank, a state-of-the-art ligand binding site prediction method. P2Rank is a template-free machine learning method which is based on the prediction of ligandability of local chemical neighborhoods centered on points placed on a solvent accessible surface of a protein. Points with high ligandability score are then clustered to form the resulting ligand binding sites. On top of that, PrankWeb then provides a web interface enabling users to easily carry out the prediction and visually inspect the predicted binding sites via an integrated sequence-structure view. Moreover, PrankWeb can determine sequence conservation for the input molecule and use it in both the prediction and results visualization steps. Alongside its online visualization options, PrankWeb also offers the possibility to export the results as a PyMOL script for offline visualization. The web frontend communicates with the serer side via a REST API. Therefore, in high-throughput scenarios users can utilize the server API directly, bypassing the need for a webbased front end or installation of the P2Rank application. PrankWeb is available at http://prankweb.cz/ . The source code of the web application and the P2Rank method can be accessed at https://github.com/jendelel/PrankWebApp and https://github.com/rdk/p2rank , respectively.