Maintaining a Linked Data Cloud and Data Service for Second World War History

One of the great promises of Linked Data is to provide a shared data infrastructure into which new data can be imported and aligned with, forming a sustainable, ever growing Linked Data Cloud (LDC). This paper studies and evaluates this idea in the context of the WarSampo LDC that provides a data infrastructure for Second World War related ontologies and data in Finland, including several mutually linked graphs, totaling ca 12 million triples. Two data integration case studies are presented, where the original WarSampo LDC and the related semantic portal were first extended by a dataset of hundreds of war cemeteries and thousands of photographs of them, and then by another dataset of over 4450 Finnish prisoners of war. As a conclusion, lessons learned are explicated, based on hands-on experience in maintaining the WarSampo LDC in a production environment.Peer reviewe

An ecosystem for linked humanities data

The main promise of the digital humanities is the ability to perform scholar studies at a much broader scale, and in a much more reusable fashion. The key enabler for such studies is the availability of suciently well described data. For the eld of socio-economic history, data usually comes in a tabular form. Existing eorts to curate and publish datasets take a top-down approach and are focused on large collections. This paper presents QBer and the underlying structured data hub, which address the long tail of research data by catering for the needs of individual scholars. QBer allows researchers to publish their (small) datasets, link them to existing vocabularies and other datasets, and thereby contribute to a growing collection of interlinked datasets.We present QBer, and evaluate our rst results by showing how our system facilitates two use cases in socio-economic history

On the Mental Workload Assessment of Uplift Mapping Representations in Linked Data

Self-reporting procedures have been largely employed in literature to measure the mental workload experienced by users when executing a specific task. This research proposes the adoption of these mental workload assessment techniques to the task of creating uplift mappings in Linked Data. A user study has been performed to compare the mental workload of “manually” creating such mappings, using a formal mapping language and a text editor, to the use of a visual representation, based on the block metaphor, that generate these mappings. Two subjective mental workload instruments, namely the NASA Task Load Index and the Workload Profile, were applied in this study. Preliminary results show the reliability of these instruments in measuring the perceived mental workload for the task of creating uplift mappings. Results also indicate that participants using the visual representation achieved smaller and more consistent scores of mental workload

An architecture for establishing legal semantic workflows in the context of integrated law enforcement

A previous version of this paper was presented at the Third Workshop on Legal Knowledge and the Semantic Web (LK&SW-2016), EKAW-2016, November 19th, Bologna, ItalyTraditionally the integration of data from multiple sources is done on an ad-hoc basis for each to "silos" that prevent sharing data across different agencies or tasks, and is unable to cope with the modern environment, where workflows, tasks, and priorities frequently change. Operating within the Data to Decision Cooperative Research Centre (D2D CRC), the authors are currently involved in the Integrated Law Enforcement Project, which has the goal of developing a federated data platform that will enable the execution of integrated analytics on data accessed from different external and internal sources, thereby providing effective support to an investigator or analyst working to evaluate evidence and manage lines of inquiries in the investigation. Technical solutions should also operate ethically, in compliance with the law, and subject to good governance principles

COMET: A Contextualized Molecule-Based Matching Technique

Context-specific description of entities expressed in RDF poses challenges during data-driven tasks, e.g., data integration, and context-aware entity matching represents a building-block for these tasks. However, existing approaches only consider inter-schema mapping of data sources, and are not able to manage several contexts during entity matching. We devise COMET, an entity matching technique that relies on both the knowledge stated in RDF vocabularies and context-based similarity metrics to match contextually equivalent entities. COMET executes a novel 1-1 perfect matching algorithm for matching contextually equivalent entities based on the combined scores of semantic similarity and context similarity. COMET employs the Formal Concept Analysis algorithm in order to compute the context similarity of RDF entities. We empirically evaluate the performance of COMET on a testbed from DBpedia. The experimental results suggest that COMET is able to accurately match equivalent RDF graphs in a context-dependent manner

Integration of scholarly communication metadata using knowledge graphs

Important questions about the scientific community, e.g., what authors are the experts in a certain field, or are actively engaged in international collaborations, can be answered using publicly available datasets. However, data required to answer such questions is often scattered over multiple isolated datasets. Recently, the Knowledge Graph (KG) concept has been identified as a means for interweaving heterogeneous datasets and enhancing answer completeness and soundness. We present a pipeline for creating high quality knowledge graphs that comprise data collected from multiple isolated structured datasets. As proof of concept, we illustrate the different steps in the construction of a knowledge graph in the domain of scholarly communication metadata (SCM-KG). Particularly, we demonstrate the benefits of exploiting semantic web technology to reconcile data about authors, papers, and conferences. We conducted an experimental study on an SCM-KG that merges scientific research metadata from the DBLP bibliographic source and the Microsoft Academic Graph. The observed results provide evidence that queries are processed more effectively on top of the SCM-KG than over the isolated datasets, while execution time is not negatively affected

On the semantics of SPARQL queries with optional matching under entailment regimes

We study the semantics of SPARQL queries with optional matching features under entailment regimes. We argue that the normative semantics may lead to answers that are in conflict with the intuitive meaning of optional matching, where unbound variables naturally represent unknown information. We propose an extension of the SPARQL algebra that addresses these issues and is compatible with any entailment regime satisfying the minimal requirements given in the normative specification. We then study the complexity of query evaluation and show that our extension comes at no cost for regimes with an entailment relation of reasonable complexity. Finally, we show that our semantics preserves the known properties of optional matching that are commonly exploited for static analysis and optimisation