Nandana Mihindukulasooriya

A comprehensive quality model for Linked Data

With the increasing amount of Linked Data published on the Web, the community has recognised the importance of the quality of such data and a number of initiatives have been undertaken to specify and evaluate Linked Data quality. However, these initiatives are characterised by a high diversity in terms of the quality aspects that they address and measure. This leads to difficulties in comparing and benchmarking evaluation results, as well as in selecting the right data source according to certain quality needs. This paper presents a quality model for Linked Data, which provides a unique terminology and reference for Linked Data quality specification and evaluation. The mentioned quality model specifies a set of quality characteristics and quality measures related to Linked Data, together with formulas for the calculation of measures. Furthermore, this paper also presents an extension of the W3C Data Quality Vocabulary that can be used to capture quality information specific to Linked Data, a Linked Data representation of the Linked Data quality model, and a use case in which the benefits of the quality model proposed in this paper are presented in a tool for Linked Data evaluation.

An Analysis of the Quality Issues of the Properties Available in the Spanish DBpedia

DBpedia exposes data from Wikipedia as machine-readable Linked Data. The DBpedia data extraction process generates RDF data in two ways; a using the mappings that map the data from Wikipedia infoboxes to the DBpedia ontology and other vocabularies, and b using infobox-properties, i.e., properties that are not defined in the DBpedia ontology but are auto-generated using the infobox attribute-value pairs. The work presented in this paper inspects the quality issues of the properties used in the Spanish DBpedia dataset according to conciseness, consistency, syntactic validity, and semantic accuracy quality dimensions. The main contribution of the paper is the identification of quality issues in the Spanish DBpedia and the possible causes of their existence. The findings presented in this paper can be used as feedback to improve the DBpedia extraction process in order to eliminate such quality issues from DBpedia.

Collaborative Ontology Evolution and Data Quality - An Empirical Analysis

Since more than a decade, theoretical research on ontology evolution has been published in literature and several frameworks for managing ontology changes have been proposed. However, there are less studies that analyze widely used ontologies that were developed in a collaborative manner to understand community-driven ontology evolution in practice. In this paper, we perform an empirical analysis on how four well-known ontologies (DBpedia, Schema.org, PROV-O, and FOAF) have evolved through their lifetime and an analysis of the data quality issues caused by some of the ontology changes. To that end, the paper discusses the composition of the communities that developed the aforementioned ontologies and the ontology development process followed. Further, the paper analyses the changes in those ontologies in the 53 versions of them examined in this study. Depending of the use case, the community involved, and other factors different approaches for the ontology development and evolution process are used (e.g., bottom-up approach with high automation or top-down approach with a lot of manual curation). This paper concludes that one model for managing changes does not fit all. Furthermore, it is also clear that none of the selected ontologies follow the theoretical frameworks found in literature. Nevertheless, in communities where industrial participants are dominant more rigorous editorial processes are followed, largely influenced by software development tools and processes. Based on the analysis, the most common quality problems caused by ontology changes include the use of abandoned classes and properties in data and introduction of duplicate classes and properties.

Seven challenges for RESTful transaction models

The REpresentational State Transfer (REST) architectural style describes the design principles that made the World Wide Web scalable and the same principles can be applied in enterprise context to do loosely coupled and scalable application integration. In recent years, RESTful services are gaining traction in the industry and are commonly used as a simpler alternative to SOAP Web Services. However, one of the main drawbacks of RESTful services is the lack of standard mechanisms to support advanced quality-of-service requirements that are common to enterprises. Transaction processing is one of the essential features of enterprise information systems and several transaction models have been proposed in the past years to fulfill the gap of transaction processing in RESTful services. The goal of this paper is to analyze the state-of-the-art RESTful transaction models and identify the current challenges.

Predicting incorrect mappings: a data-driven approach applied to DBpedia

DBpedia releases consist of more than 70 multilingual datasets that cover data extracted from different language-specific Wikipedia instances. The data extracted from those Wikipedia instances are transformed into RDF using mappings created by the DBpedia community. Nevertheless, not all the mappings are correct and consistent across all the distinct language-specific DBpedia datasets. As these incorrect mappings are spread in a large number of mappings, it is not feasible to inspect all such mappings manually to ensure their correctness. Thus, the goal of this work is to propose a data-driven method to detect incorrect mappings automatically by analyzing the information from both instance data as well as ontological axioms. We propose a machine learning based approach to building a predictive model which can detect incorrect mappings. We have evaluated different supervised classification algorithms for this task and our best model achieves 93% accuracy. These results help us to detect incorrect mappings and achieve a high-quality DBpedia.

morph-LDP: An R2RML-Based Linked Data Platform Implementation

The W3C Linked Data Platform (LDP) candidate recommendation defines a standard HTTP-based protocol for read/write Linked Data. The W3C R2RML recommendation defines a language to map relational databases (RDBs) and RDF. This paper presents morph-LDP, a novel system that combines these two W3C standardization initiatives to expose relational data as read/write Linked Data for LDP-aware applications, whilst allowing legacy applications to continue using their relational databases.

MappingPedia: A Collaborative Environment for R2RML Mappings

Most of Semantic Web data is being generated from legacy datasets with the help of mappings, some of which may have been specified declaratively in languages such as R2RML or its extensions: RML and xR2RML. Most of these mappings are kept locally in each organization, and to the best to our knowledge, a shared repository that would facilitate the discovery, registration, execution, request and analysis of mappings doesn’t exist. Additionally, many R2RML users do not have sufficient knowledge of the mapping language, and would probably benefit from collaborating with others. We present a demo of MappingPedia, a collaborative environment for storing and sharing R2RML mappings. It is comprised of five main functionalities: (1) Discover, (2) Share, (3) Execute, (4) Request, and (5) Analyze.

Inducing Implicit Relations from Text Using Distantly Supervised Deep Nets.

Knowledge Base Population (KBP) is an important problem in Semantic Web research and a key requirement for successful adoption of semantic technologies in many applications. In this paper we present Socrates, a deep learning based solution for Automated Knowledge Base Population from Text. Socrates does not require manual annotations which would make the solution hard to adapt to a new domain. Instead, it exploits a partially populated knowledge base and a large corpus of text documents to train a set of deep neural network models. As a result of the training process, the system learns how to identify implicit relations between entities across a highly heterogeneous set of documents from various sources, making it suitable for large-scale knowledge extraction from Web documents. Main contributions of this paper include (a) a novel approach based on composite contexts to acquire implicit relations from Title Oriented Documents, and (b) an architecture for unifying relation extraction using binary, unary, and composite contexts. We provide an extensive evaluation of the system across three different benchmarks with different characteristics, showing that our unified framework can consistently outperform state of the art solutions. Remarkably, Socrates ranked first in both the knowledge base population and attribute validation track at the Semantic Web Challenge at ISWC 2017.

RDF shape induction using knowledge base profiling

Knowledge Graphs (KGs) are becoming the core of most artificial intelligent and cognitive applications. Popular KGs such as DBpedia and Wikidata have chosen the RDF data model to represent their data. Despite the advantages, there are challenges in using RDF data, for example, data validation. Ontologies for specifying domain conceptualizations in RDF data are designed for entailments rather than validation. Most ontologies lack the granular information needed for validating constraints. Recent work on RDF Shapes and standardization of languages such as SHACL and ShEX provide better mechanisms for representing integrity constraints for RDF data. However, manually creating constraints for large KGs is still a tedious task. In this paper, we present a data driven approach for inducing integrity constraints for RDF data using data profiling. Those constraints can be combined into RDF Shapes and can be used to validate RDF graphs. Our method is based on machine learning techniques to automatically generate RDF shapes using profiled RDF data as features. In the experiments, the proposed approach achieved 97% precision in deriving RDF Shapes with cardinality constraints for a subset of DBpedia data.

Repairing Hidden Links in Linked Data: Enhancing the quality of RDF knowledge graphs

Knowledge Graphs (KG) are becoming core components of most artificial intelligence applications. Linked Data, as a method of publishing KGs, allows applications to traverse within, and even out of, the graph thanks to global dereferenceable identifiers denoting entities, in the form of IRIs. However, as we show in this work, after analyzing several popular datasets (namely DBpedia, LOD Cache, and Web Data Commons JSON-LD data) many entities are being represented using literal strings where IRIs should be used, diminishing the advantages of using Linked Data. To remedy this, we propose an approach for identifying such strings and replacing them with their corresponding entity IRIs. The proposed approach is based on identifying relations between entities based on both ontological axioms as well as data profiling information and converting strings to entity IRIs based on the types of entities linked by each relation. Our approach showed 98% recall and 76% precision in identifying such strings and 97% precision in converting them to their corresponding IRI in the considered KG. Further, we analyzed how the connectivity of the KG is increased when new relevant links are added to the entities as a result of our method. Our experiments on a subset of the Spanish DBpedia data show that it could add 25% more links to the KG and improve the overall connectivity by 17%.