Reply to: Soils need to be considered when assessing the impacts of land-use change on carbon sequestration

Industrial Ecolog

Sextant: Visualizing time-evolving linked geospatial data

The linked open data cloud is constantly evolving as datasets get continuously updated with newer versions. As a result, representing, querying, and visualizing the temporal dimension of linked data is crucial. This is especially important for geospatial datasets that form the backbone of large scale open data publication efforts in many sectors of the economy (e.g., the public sector, the Earth Observation sector). Although there has been some work on the representation and querying of linked geospatial data that change over time, to the best of our knowledge, there is currently no tool that offers spatio-temporal visualization of such data. This is in contrast with the existence of many tools for the visualization of the temporal evolution of geospatial data in the GIS area. In this article, we present Sextant, a Web-based system for the visualization and exploration of time-evolving linked geospatial data and the creation, sharing, and collaborative editing of “temporally-enriched” thematic maps which are produced by combining different sources of such data. We present the architecture of Sextant, give examples of its use and present applications in which we have deployed it

A More Decentralized Vision for Linked Data

In this deliberately provocative position paper, we claim that ten years into Linked Data there are still (too?) many unresolved challenges towards arriving at a truly machine-readable and decentralized Web of data. We take a deeper look at the biomedical domain - currently, one of the most promising "adopters" of Linked Data - if we believe the ever-present "LOD cloud" diagram. Herein, we try to highlight and exemplify key technical and non-technical challenges to the success of LOD, and we outline potential solution strategies. We hope that this paper will serve as a discussion basis for a fresh start towards more actionable, truly decentralized Linked Data, and as a call to the community to join forces.Series: Working Papers on Information Systems, Information Business and Operation

Automated Knowledge Base Quality Assessment and Validation based on Evolution Analysis

In recent years, numerous efforts have been put towards sharing Knowledge Bases (KB) in the Linked Open Data (LOD) cloud. These KBs are being used for various tasks, including performing data analytics or building question answering systems. Such KBs evolve continuously: their data (instances) and schemas can be updated, extended, revised and refactored. However, unlike in more controlled types of knowledge bases, the evolution of KBs exposed in the LOD cloud is usually unrestrained, what may cause data to suffer from a variety of quality issues, both at a semantic level and at a pragmatic level. This situation affects negatively data stakeholders – consumers, curators, etc. –. Data quality is commonly related to the perception of the fitness for use, for a certain application or use case. Therefore, ensuring the quality of the data of a knowledge base that evolves is vital. Since data is derived from autonomous, evolving, and increasingly large data providers, it is impractical to do manual data curation, and at the same time, it is very challenging to do a continuous automatic assessment of data quality. Ensuring the quality of a KB is a non-trivial task since they are based on a combination of structured information supported by models, ontologies, and vocabularies, as well as queryable endpoints, links, and mappings. Thus, in this thesis, we explored two main areas in assessing KB quality: (i) quality assessment using KB evolution analysis, and (ii) validation using machine learning models. The evolution of a KB can be analyzed using fine-grained “change” detection at low-level or using “dynamics” of a dataset at high-level. In this thesis, we present a novel knowledge base quality assessment approach using evolution analysis. The proposed approach uses data profiling on consecutive knowledge base releases to compute quality measures that allow detecting quality issues. However, the first step in building the quality assessment approach was to identify the quality characteristics. Using high-level change detection as measurement functions, in this thesis we present four quality characteristics: Persistency, Historical Persistency, Consistency and Completeness. Persistency and historical persistency measures concern the degree of changes and lifespan of any entity type. Consistency and completeness measures identify properties with incomplete information and contradictory facts. The approach has been assessed both quantitatively and qualitatively on a series of releases from two knowledge bases, eleven releases of DBpedia and eight releases of 3cixty Nice. However, high-level changes, being coarse-grained, cannot capture all possible quality issues. In this context, we present a validation strategy whose rationale is twofold. First, using manual validation from qualitative analysis to identify causes of quality issues. Then, use RDF data profiling information to generate integrity constraints. The validation approach relies on the idea of inducing RDF shape by exploiting SHALL constraint components. In particular, this approach will learn, what are the integrity constraints that can be applied to a large KB by instructing a process of statistical analysis, which is followed by a learning model. We illustrate the performance of our validation approach by using five learning models over three sub-tasks, namely minimum cardinality, maximum cardinality, and range constraint. The techniques of quality assessment and validation developed during this work are automatic and can be applied to different knowledge bases independently of the domain. Furthermore, the measures are based on simple statistical operations that make the solution both flexible and scalable

A Quality Assessment Approach for Evolving Knowledge Bases

Knowledge bases are nowadays essential components for any task that requires automation with some degrees of intelligence.Assessing the quality of a Knowledge Base (KB) is a complex task as it often means measuring the quality of structured information, ontologies and vocabularies, and queryable endpoints. Popular knowledge bases such as DBpedia, YAGO2, and Wikidata have chosen the RDF data model to represent their data due to its capabilities for semantically rich knowledge representation. Despite its advantages, there are challenges in using RDF data model, for example, data quality assessment and validation. In thispaper, we present a novel knowledge base quality assessment approach that relies on evolution analysis. The proposed approachuses data profiling on consecutive knowledge base releases to compute quality measures that allow detecting quality issues. Our quality characteristics are based on the KB evolution analysis and we used high-level change detection for measurement functions. In particular, we propose four quality characteristics: Persistency, Historical Persistency, Consistency, and Completeness.Persistency and historical persistency measures concern the degree of changes and lifespan of any entity type. Consistency andcompleteness measures identify properties with incomplete information and contradictory facts. The approach has been assessed both quantitatively and qualitatively on a series of releases from two knowledge bases, eleven releases of DBpedia and eight releases of 3cixty. The capability of Persistency and Consistency characteristics to detect quality issues varies significantly between the two case studies. Persistency measure gives observational results for evolving KBs. It is highly effective in case of KBwith periodic updates such as 3cixty KB. The Completeness characteristic is extremely effective and was able to achieve 95%precision in error detection for both use cases. The measures are based on simple statistical operations that make the solution both flexible and scalabl

Scalable Quality Assessment of Linked Data

In a world where the information economy is booming, poor data quality can lead to adverse consequences, including social and economical problems such as decrease in revenue. Furthermore, data-driven indus- tries are not just relying on their own (proprietary) data silos, but are also continuously aggregating data from different sources. This aggregation could then be re-distributed back to “data lakes”. However, this data (including Linked Data) is not necessarily checked for its quality prior to its use. Large volumes of data are being exchanged in a standard and interoperable format between organisations and published as Linked Data to facilitate their re-use. Some organisations, such as government institutions, take a step further and open their data. The Linked Open Data Cloud is a witness to this. However, similar to data in data lakes, it is challenging to determine the quality of this heterogeneous data, and subsequently to make this information explicit to data consumers. Despite the availability of a number of tools and frameworks to assess Linked Data quality, the current solutions do not aggregate a holistic approach that enables both the assessment of datasets and also provides consumers with quality results that can then be used to find, compare and rank datasets’ fitness for use. In this thesis we investigate methods to assess the quality of (possibly large) linked datasets with the intent that data consumers can then use the assessment results to find datasets that are fit for use, that is; finding the right dataset for the task at hand. Moreover, the benefits of quality assessment are two-fold: (1) data consumers do not need to blindly rely on subjective measures to choose a dataset, but base their choice on multiple factors such as the intrinsic structure of the dataset, therefore fostering trust and reputation between the publishers and consumers on more objective foundations; and (2) data publishers can be encouraged to improve their datasets so that they can be re-used more. Furthermore, our approach scales for large datasets. In this regard, we also look into improving the efficiency of quality metrics using various approximation techniques. However the trade-off is that consumers will not get the exact quality value, but a very close estimate which anyway provides the required guidance towards fitness for use. The central point of this thesis is not on data quality improvement, nonetheless, we still need to understand what data quality means to the consumers who are searching for potential datasets. This thesis looks into the challenges faced to detect quality problems in linked datasets presenting quality results in a standardised machine-readable and interoperable format for which agents can make sense out of to help human consumers identifying the fitness for use dataset. Our proposed approach is more consumer-centric where it looks into (1) making the assessment of quality as easy as possible, that is, allowing stakeholders, possibly non-experts, to identify and easily define quality metrics and to initiate the assessment; and (2) making results (quality metadata and quality reports) easy for stakeholders to understand, or at least interoperable with other systems to facilitate a possible data quality pipeline. Finally, our framework is used to assess the quality of a number of heterogeneous (large) linked datasets, where each assessment returns a quality metadata graph that can be consumed by agents as Linked Data. In turn, these agents can intelligently interpret a dataset’s quality with regard to multiple dimensions and observations, and thus provide further insight to consumers regarding its fitness for use

Strategy for energy measurement and interoperability

garciacastro2014ano abstrac

Strategy for energy measurement and interoperability

garciacastro2014ano abstrac

Linked Data based Health Information Representation, Visualization and Retrieval System on the Semantic Web

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.To better facilitate health information dissemination, using flexible ways to represent, query and visualize health data becomes increasingly important. Semantic Web technologies, which provide a common framework by allowing data to be shared and reused between applications, can be applied to the management of health data. Linked open data - a new semantic web standard to publish and link heterogonous data- allows not only human, but also machine to brows data in unlimited way. Through a use case of world health organization HIV data of sub Saharan Africa - which is severely affected by HIV epidemic, this thesis built a linked data based health information representation, querying and visualization system. All the data was represented with RDF, by interlinking it with other related datasets, which are already on the cloud. Over all, the system have more than 21,000 triples with a SPARQL endpoint; where users can download and use the data and – a SPARQL query interface where users can put different type of query and retrieve the result. Additionally, It has also a visualization interface where users can visualize the SPARQL result with a tool of their preference. For users who are not familiar with SPARQL queries, they can use the linked data search engine interface to search and browse the data. From this system we can depict that current linked open data technologies have a big potential to represent heterogonous health data in a flexible and reusable manner and they can serve in intelligent queries, which can support decision-making. However, in order to get the best from these technologies, improvements are needed both at the level of triple stores performance and domain-specific ontological vocabularies