Linked Data Paradigm Research Articles

A synthetic data generation system based on the variational-autoencoder technique and the linked data paradigm

A synthetic data generation system based on the variational-autoencoder technique and the linked data paradigm

Publishing the Dictionary of Medieval Latin in the Czech Lands as Linked Data in the LiLa Knowledge Base

The article presents the process of linking the Dictionary of Medieval Latin in the Czech Lands to the LiLa Knowledge Base, which adopts the Linked Data paradigm to make linguistic resources for Latin interoperable. First, we provide an overview of the architecture of the LiLa Knowledge Base and of the Dictionary; then, we detail the stages of the process of linking the Dictionary to the collection of Latin lemmas that represents the core of LiLa. Once completed the linking process, we demonstrate to what extent the publication of the Dictionary as Linked Data proves beneficial by presenting the linking to LiLa of a new text from the same area and period covered by the Dictionary. In conclusion, a few queries illustrate how interoperability allows for full exploitation of a set of interlinked Latin resources.1

A hybrid recommender system based on description/dialetheic logic and linked data

AbstractIn this research, we developed a hybrid recommender system based on description/dialetheic logic, which provides an innovative architecture for the recommendation based on ambiguous reasoning. In addition, our approach allows enriching the knowledge during the reasoning using the linked data paradigm. Thus, the architecture allows the integration of linked data, in order to be exploited by the recommender. On the other hand, the reasoning mechanisms of dialetheic logic allow handling situations of contradiction or inconsistency, to determine the information to offer to users. According to the reviewed literature, our proposal is the first that implements a dialetheic engine in a Recommender System. In general, there are a lot of works about the utilization of linked open data (LOD) to improve the Recommender Systems, for example, to enrich the information to recommend or to solve the cold start problem. However, there are no proposals to deal with the problems of ambiguity, incoherence or incompleteness of the information in these environments extended with LOD. In this way, this paper proposes a hybrid reasoning engine that uses the linked data paradigm for the knowledge extraction, and dialetheic logic for the management of inconsistency/ambiguity information, in order to obtain recommendations. Particularly, the information extracted with Linked Data is processed with the Dialetheic Logic reasoner to solve ambiguous cases. Thus, each recommendation is enriched with related content extracted from the linked data sources, which has been disambiguated. The results are very promising since our hybrid reasoning mechanism allows obtaining more precise recommendations, considering the different classical states of ambiguity in dialetheic logic (contingent statements about the future, failure of a presupposition, vagueness, counterfactual reasoning), according to various metrics of quality used to evaluate the recommendations achieved.

LIMES: A Framework for Link Discovery on the Semantic Web

The Linked Data paradigm builds upon the backbone of distributed knowledge bases connected by typed links. The mere volume of current knowledge bases as well as their sheer number pose two major challenges when aiming to support the computation of links across and within them. The first is that tools for link discovery have to be time-efficient when they compute links. Secondly, these tools have to produce links of high quality to serve the applications built upon Linked Data well. Solutions to the second problem build upon efficient computational approaches developed to solve the first and combine these with dedicated machine learning techniques. The current version of the Limes framework is the product of seven years of research on these two challenges. A series of machine learning techniques and efficient computation approaches were developed and integrated into this framework to address the link discovery problem. The framework combines these diverse algorithms within a generic and extensible architecture. In this article, we give an overview of version 1.7.4 of the open-source release of the framework. In particular, we focus on an overview of the architecture of the framework, an intuition of its inner workings and a brief overview of the approaches it contains. Some descriptions of the applications within which the framework was used complete the paper. Our framework is open-source and available under a GNU license at https://github.com/dice-group/LIMES together with a user manual and a developer manual.

Multilingual Verbalization and Summarization for Explainable Link Discovery

The number and size of datasets abiding by the Linked Data paradigm increase every day. Discovering links between these datasets is thus central to achieving the vision behind the Data Web. Declarative Link Discovery (LD) frameworks rely on complex Link Specification (LS) to express the conditions under which two resources should be linked. Understanding such LS is not a trivial task for non-expert users. Particularly when such users are interested in generating LS to match their needs. Even if the user applies a machine learning algorithm for the automatic generation of the required LS, the challenge of explaining the resultant LS persists. Hence, providing explainable LS is the key challenge to enable users who are unfamiliar with underlying LS technologies to use them effectively and efficiently. In this paper, we extend our previous work (Ahmed et al., 2019) by proposing a generic multilingual approach that allows verbalization of LS in many languages, i.e., converts LS into understandable natural language text. In this work, we ported our LS verbalization framework into German and Spanish, in addition to English language. Our adequacy and fluency evaluations show that our approach can generate complete and easily understandable natural language descriptions even by lay users. Moreover, we devised an experimental neural approach for improving the quality of our generated texts. Our neural approach achieves promising results in terms of BLEU, METEOR and chrF++.

Wikidata

Library catalogues may be connected to the linked data cloud through various types of thesauri. For name authority thesauri in particular I would like to suggest a fundamental break with the current distributed linked data paradigm: to make a transition from a multitude of different identifiers to using a single, universal identifier for all relevant named entities, in the form of the Wikidata identifier. Wikidata (https://wikidata.org) seems to be evolving into a major authority hub that is lowering barriers to access the web of data for everyone. Using the Wikidata identifier of notable entities as a common identifier for connecting resources has significant benefits compared to traversing the ever-growing linked data cloud. When the use of Wikidata reaches a critical mass, for some institutions, Wikidata could even serve as an authority control mechanism.

Logical Foundations of Linked Data Anonymisation

The widespread adoption of the Linked Data paradigm has been driven by the increasing demand for information exchange between organisations, as well as by regulations in domains such as health care and governance that require certain data to be published. In this setting, sensitive information is at high risk of disclosure since published data can be often seamlessly linkedwith arbitrary external data sources.In this paper we lay the logical foundations of anonymisation in the context of Linked Data. We consider anonymisations of RDF graphs (and, more generally, relational datasets with labelled nulls) and define notions of policy-compliant and linkage-safe anonymisations. Policy compliance ensures that an anonymised dataset does not reveal any sensitive information as specified by a policy query. Linkage safety ensures that an anonymised dataset remains compliant even if it is linked to (possibly unknown) external datasets available on the Web, thus providing provable protection guarantees against data linkage attacks. We establish the computational complexity of the underpinning decision problems both under the open-world semantics inherent to RDF and under the assumption that an attacker has complete, closed-world knowledge over some parts of the original data.

QueryMed: Semantic Web functions for linking pharmacological and medical knowledge to data.

In public health research and more precisely in the reuse of electronic health data, selecting patients, identifying specific events and interpreting results typically requires biomedical knowledge. The queryMed R package aims to facilitate the integration of medical and pharmacological knowledge stored in formats compliant with the Linked Data paradigm (e.g. OWL ontologies and RDF datasets) into the R statistical programming environment. We show how it allowed us to identify all the drugs prescribed for critical limb ischemia (CLI) and also to detect one contraindicated prescription for one patient by linking a medical database of 1003 CLI patients to ontologies. queryMed is readily usable for medical data mappings and enrichment. Sources, R vignettes and test data are available on GitHub (https://github.com/yannrivault/queryMed) and are archived on Zenodo (https://doi.org/10.5281/zenodo.1323481).

Classifying the LOD cloud

Massive amounts of data from different contexts and producers are collected and connected relying often solely on statistical techniques. Problems to the acclaimed value of data lie in the precise definition of data and associated contexts as well as the problem that data are not always published in meaningful and open ways. The Linked Data paradigm offers a solution to the limitations of simple keywords by having unique, resolvable and shared identifiers instead of strings This paper reports on a three-year research project “Digging Into the Knowledge Graph,” funded as part of the 2016 Round Four Digging Into Data Challenge (https://diggingintodata.org/awards/2016/project/digging-knowledge-graph). Our project involves comparing terminology employed within the LOD cloud with terminology employed within two general but different KOSs – Universal Decimal Classification and Basic Concepts Classification. We are exploring whether these classifications can encourage greater consistency in LOD terminology and linking the largely distinct scholarly literatures that address LOD and KOSs. Our project is an attempt to connect the Linked Open Data community, which has tended to be centered in computer science, and the KO community, with members from linguistics, metaphysics, library and information science. We focus on the shared challenges related to Big Data between both communities.

On modeling linked open statistical data

A major part of Open Data concerns statistics such as economic and social indicators. Statistical data are structured in a multidimensional manner creating data cubes. Recently, National Statistical Institutes and public authorities adopted the Linked Data paradigm to publish their statistical data on the Web. Many vocabularies have been created to enable modeling data cubes as RDF graphs, and thus creating Linked Open Statistical Data (LOSD). However, the creation of LOSD remains a demanding task mainly because of modeling challenges related either to the conceptual definition of the cube, or to the way of modeling cubes as linked data. The aim of this paper is to identify and clarify (a) modeling challenges related to the creation of LOSD and (b) approaches to address them. Towards this end, nine LOSD experts were involved in an interactive feedback collection and consensus-building process that was based on Delphi method. We anticipate that the results of this paper will contribute towards the formulation of best practices for creating LOSD, and thus facilitate combining and analyzing statistical data from diverse sources on the Web.

GeoTriples: Transforming geospatial data into RDF graphs using R2RML and RML mappings

A lot of geospatial data has become available at no charge in many countries recently. Geospatial data that is currently made available by government agencies usually do not follow the linked data paradigm. In the few cases where government agencies do follow the linked data paradigm (e.g., Ordnance Survey in the United Kingdom), specialized scripts have been used for transforming geospatial data into RDF. In this paper we present the open source tool GeoTriples which generates and processes extended R2RML and RML mappings that transform geospatial data from many input formats into RDF. GeoTriples allows the transformation of geospatial data stored in raw files (shapefiles, CSV, KML, XML, GML and GeoJSON) and spatially-enabled RDBMS (PostGIS and MonetDB) into RDF graphs using well-known vocabularies like GeoSPARQL and stSPARQL, but without being tightly coupled to a specific vocabulary. GeoTriples has been developed in European projects LEO and Melodies and has been used to transform many geospatial data sources into linked data. We study the performance of GeoTriples experimentally using large publicly available geospatial datasets, and show that GeoTriples is very efficient and scalable especially when its mapping processor is implemented using Apache Hadoop.

ExConQuer: Lowering barriers to RDF and Linked Data re-use

A major obstacle to the wider use of semantic technology is the perceived complexity of RDF data by stakeholders who are not familiar with the Linked Data paradigm, or are otherwise unaware of a dataset’s underlying schema. In order to help overcome this barrier, we propose the ExConQuer Framework (Explore, Convert, and Query Framework) as a tool that preserves the semantic richness of the data model while catering for simplified and workable views of the data. With the aim of encouraging and enabling further re-use of Linked Data by people who would otherwise shy away from this task, this framework facilitates the publication and consumption of RDF in a variety of generic formats. In this manner, any stakeholder can export and work with RDF data in the formats they are most accustomed with, radically lowering the entry barrier to the use of semantic technologies, and possibly enabling the exploitation of Linked Data to its full potential. Through the ExConQuer Framework we provide a comprehensive set of tools that enable users to easily query linked datasets, download the results in a number of formats, and re-use previously-executed queries and transformations. With this framework we hence attempt to target the evident niche in existing tools that are intended to be used by non-experts to consume Linked Data.

Linking basic lexicon to shared ontology for endangered languages: A Linked Data Approach toward Formosan Languages

This paper proposes an innovative approach to link basic lexicon (e.g. Swadesh list) to upper ontology as the foundation of OntoLex interface to address the challenge of building language resources for endangered languages in the linked data paradigm. A linked data approach to language resources requires existing, and preferably sizable, language resources. For endangered and other less-resourced languages, however, the scarcity of existing resources limits the possibilities and potential benefits of linking. The challenges are then, how can construction of language resources for endangered language continue to thrive in the linked data paradigm, and how can the linked data approach benefit language resources for endangered languages. Our proposal requires the bare minimum of available data and we show with examples from Formosan languages (Austronesian or aboriginal languages of Taiwan (Blust 2013, 20)) that 1) this approach is applicable to endangered languages, and that 2) in spite of the restrictions imposed by scarcity of resources, the linked linguistic data consisting of basic lexicon + upper ontology generate important new information. Comparing Swadesh lists from different languages allowed us to build a small shared ontology that reflects direct human experience, and can serve as the cross-lingual conceptual core. In addition, these micro-ontologized lexicons can be used as seeds for developing a fully-grown and more comprehensive documentation of linguistically motivated ontology for each language.

GeoTriples: Transforming Geospatial Data into RDF Graphs Using R2RML and RML Mappings

A lot of geospatial data has become available at no charge in many countries recently. Geospatial data that is currently made available by government agencies usually do not follow the linked data paradigm. In the few cases where government agencies do follow the linked data paradigm (e.g., Ordnance Survey in the United Kingdom), specialized scripts have been used for transforming geospatial data into RDF. In this paper we present the open source tool GeoTriples which generates and processes extended R2RML and RML mappings that transform geospatial data from many input formats into RDF. GeoTriples allows the transformation of geospatial data stored in raw files (shapefiles, CSV, KML, XML, GML and GeoJSON) and spatially-enabled RDBMS (PostGIS and MonetDB) into RDF graphs using well-known vocabularies like GeoSPARQL and stSPARQL, but without being tightly coupled to a specific vocabulary. GeoTriples has been developed in European projects LEO and Melodies and has been used to transform many geospatial data sources into linked data. We study the performance of GeoTriples experimentally using large publicly available geospatial datasets, and show that GeoTriples is very efficient and scalable especially when its mapping processor is implemented using Apache Hadoop.

On Modeling Linked Open Statistical Data

A major part of Open Data concerns statistics such as economic and social indicators. Statistical data are structured in a multidimensional manner creating data cubes. Recently, National Statistical Institutes and public authorities adopted the Linked Data paradigm to publish their statistical data on the Web. Many vocabularies have been created to enable modeling data cubes as RDF graphs, and thus creating Linked Open Statistical Data (LOSD). However, the creation of LOSD remains a demanding task mainly because of modeling challenges related either to the conceptual definition of the cube, or to the way of modeling cubes as linked data. The aim of this paper is to identify and clarify (a) modeling challenges related to the creation of LOSD and (b) approaches to address them. Towards this end, nine LOSD experts were involved in an interactive feedback collection and consensus-building process that was based on Delphi method. We anticipate that the results of this paper will contribute towards the formulation of best practices for creating LOSD, and thus facilitate combining and analysing statistical data from diverse sources on the Web.

Large-scale adverse effects related to treatment evidence standardization (LAERTES): an open scalable system for linking pharmacovigilance evidence sources with clinical data

BackgroundIntegrating multiple sources of pharmacovigilance evidence has the potential to advance the science of safety signal detection and evaluation. In this regard, there is a need for more research on how to integrate multiple disparate evidence sources while making the evidence computable from a knowledge representation perspective (i.e., semantic enrichment). Existing frameworks suggest well-promising outcomes for such integration but employ a rather limited number of sources. In particular, none have been specifically designed to support both regulatory and clinical use cases, nor have any been designed to add new resources and use cases through an open architecture. This paper discusses the architecture and functionality of a system called Large-scale Adverse Effects Related to Treatment Evidence Standardization (LAERTES) that aims to address these shortcomings.ResultsLAERTES provides a standardized, open, and scalable architecture for linking evidence sources relevant to the association of drugs with health outcomes of interest (HOIs). Standard terminologies are used to represent different entities. For example, drugs and HOIs are represented in RxNorm and Systematized Nomenclature of Medicine -- Clinical Terms respectively. At the time of this writing, six evidence sources have been loaded into the LAERTES evidence base and are accessible through prototype evidence exploration user interface and a set of Web application programming interface services. This system operates within a larger software stack provided by the Observational Health Data Sciences and Informatics clinical research framework, including the relational Common Data Model for observational patient data created by the Observational Medical Outcomes Partnership. Elements of the Linked Data paradigm facilitate the systematic and scalable integration of relevant evidence sources.ConclusionsThe prototype LAERTES system provides useful functionality while creating opportunities for further research. Future work will involve improving the method for normalizing drug and HOI concepts across the integrated sources, aggregated evidence at different levels of a hierarchy of HOI concepts, and developing more advanced user interface for drug-HOI investigations.

Big, Linked Geospatial Data and Its Applications in Earth Observation

If the terabytes of Earth observation data currently stored in archives are published on the web using a linked data paradigm, data discovery, integration with other data sources, and the development of applications will become much easier.

Managing Big, Linked, and Open Earth-Observation Data: Using the TELEIOS\/LEO software stack

Big Earth-observation (EO) data that are made freely available by space agencies come from various archives. Therefore, users trying to develop an application need to search within these archives, discover the needed data, and integrate them into their application. In this article, we argue that if EO data are published using the linked data paradigm, then the data discovery, data integration, and development of applications becomes easier. We present the life cycle of big, linked, and open EO data and show how to support their various stages using the software stack developed by the European Union (EU) research projects TELEIOS and the Linked Open EO Data for Precision Farming (LEO). We also show how this stack of tools can be used to implement an operational wildfire-monitoring service.

Enriching Linked Data with Semantics from Domain-Specific Diagrammatic Models

One key driver of the Linked Data paradigm is the ability to lift data graphs from legacy systems by employing various adapters and RDFizers (e.g., D2RQ for relational databases, XLWrap for spreadsheets). Such approaches aim towards removing boundaries of enterprise data silos by opening them to cross-organizational linking within a “Web of Data”. An insufficiently tapped source of machine-readable semantics is the underlying graph nature of diagrammatic conceptual models – a kind of information that is richer compared to what is typically lifted from table schemata, especially when a domain-specific modeling language is employed. The paper advocates an approach to Linked Data enrichment based on a diagrammatic model RDFizer originally developed in the context of the ComVantage FP7 research project. A minimal but illustrative example is provided from which arguments will be generalized, leading to a proposed vision of “conceptual model”-aware information systems.

Instance matching benchmarks in the era of Linked Data

The goal of this survey is to present the state of the art instance matching benchmarks for Linked Data. We introduce the principles of benchmark design for instance matching systems, discuss the dimensions and characteristics of an instance matching benchmark, provide a comprehensive overview of existing benchmarks, as well as benchmark generators, discuss their advantages and disadvantages, as well as the research directions that should be exploited for the creation of novel benchmarks, to answer the needs of the Linked Data paradigm.