Characteristic sets (CS) organize RDF triples based on the set of properties associated with their subject nodes. This concept was recently used in indexing techniques, as it can capture the implicit schema of RDF data. While most CS-based approaches yield significant improvements in space and query performance, they fail to perform well when answering complex query workloads in the presence of schema heterogeneity, i.e., when the number of CSs becomes very large, resulting in a highly partitioned data organization. In this paper, we address this problem by introducing a novel technique, for merging CSs based on their hierarchical structure. Our method employs a lattice to capture the hierarchical relationships between CSs, identifies dense CSs and merges dense CSs with their ancestors. We have implemented our algorithm on top of a relational backbone, where each merged CS is stored in a relational table, and therefore, CS merging results in a smaller number of required tables to host the source triples of a dataset. Moreover, we perform an extensive experimental study to evaluate the performance and impact of merging to the storage and querying of RDF datasets, indicating significant improvements. We also conduct a sensitivity analysis to identify the stability and any possible weaknesses of our algorithm, and report on our results.

This article presents LinkZoo, a web-based, linked data enabled tool that supports collaborative management of information resources. LinkZoo addresses the modern needs of information-intensive collaboration environments to publish, manage, and share heterogeneous resources within user-driven contexts. Users create and manage diverse types of resources into common spaces such as files, web documents, people, datasets, and calendar events. They can interlink them, annotate them, and share them with other users, thus enabling collaborative editing, as well as enrich them with links to externally linked data resources. Resources are inherently modeled and published as resource description framework (RDF) and can be explicitly interlinked and dereferenced by external applications. LinkZoo supports creation of dynamic communities that enable web-based collaboration through resource sharing and annotating, exposing objects on the linked data Cloud under controlled vocabularies and permissions. The authors demonstrate the applicability of the tool on a popular collaboration use case scenario for sharing and organizing research resources.

State of the art works for Point-Of-Interest (POI) classification use either traditional feature extraction methods, or, more recently, deep learning (DL) methods, in order to train classification models on historical data, i.e. POIs already annotated with categories, and then deploy these models to classify new, unannotated POIs. These methods are either inherently limited to learning simple, rather than complex, relationships, or learn embeddings trivially and without taking into account domain knowledge or semantic information, thus yielding disappointing classification results. In this vision paper, we discuss the problem of POI classification, identify limitations in the state of the art and propose novel research directions. We propose a framework for learning meaningful context embeddings for POIs, that incorporate domain knowledge and semantic information extracted from external data sources, and we discuss how the proposed approach can overcome inherent limitations of the state of the art. Specifically, we prescribe the construction of fine-grained embeddings, by refining the consideration of spatial neighborhoods of POIs, assessing and selecting the proper attributes to be used as context and target information in the embedding learning process, as well as by properly enriching these embeddings with external, semantic knowledge. Additionally, we advocate the joint utilization of traditional training features and embedding-derived features for POI classification, and argue towards the usefulness of the proposed approach.

SPARQL query optimization relies on the design and execution of query plans that involve reordering triple patterns, in the hopes of minimizing cardinality of intermediate results. In practice, this is not always effective, as many existing systems succeed in certain types of query patterns and fail in others. This kind of trade-off is often a derivative of the algorithms behind query planning. In this paper, we introduce a novel join reordering approach that translates a query into a multidimensional vector space and performs distance-based optimization by taking into account the relative differences between the triple patterns. Preliminary experiments on synthetic data show that our algorithm consistently outperforms established methodologies, providing better plans for many different types of query patterns.

SPARQL query execution in state of the art RDF engines depends on, and is often limited by the underlying storage and indexing schemes. Typically, these systems exhaustively store permutations of the standard three-column triples table. However, even though RDF can give birth to datasets with loosely defined schemas, it is common for an emerging structure to appear in the data. In this paper, we introduce a novel indexing scheme for RDF data, that takes advantage of the inherent structure of triples. To this end, we define the Extended Characteristic Set (ECS), a schema abstraction that classifies triples based on the properties of their subjects and objects, and we discuss methods and algorithms for the identification and extraction of ECSs. We show how these can be used to assist query processing, and we implement axonDB, an RDF storage and querying engine based on ECS indexing. We perform an experimental evaluation on real world and synthetic datasets and observe that axonDB outperforms the competition by a few orders of magnitude.

State of the art RDF stores often rely on exhaustive indexing and sequential (self-)joins for SPARQL query processing. However, query execution is dependent on, and often limited by the underlying storage and indexing schemes. Even though RDF can give birth to datasets with loosely defined schemas, it is common for an emerging structure to be present in the data. In this paper we introduce a novel indexing scheme, called Double Chain Star (DCS), that takes advantage of the inherent structure that is often found in RDF datasets by extending the notion of Characteristic Sets to cater for chain-star joins. DCS essentially reduces pairs of chain-star patterns that typically involve multiple self-joins, to mere index scans. We perform preliminary experiments and show promising results in comparison with Jena TDB and RDF-3X. © 2016, Copyright is with the authors.

Multidimensional data are published in the web of data under common directives, such as the Resource Description Framework (RDF). The increasing volume and diversity of these data pose the challenge of finding relations between them in a most efficient and accurate way, by taking into advantage their overlapping schemes. In this paper we define two types of relationships between multidimensional RDF data, and we propose algorithms for efficient and scalable computation of these relationships. Specifically, we define the notions of containment and complementarity between points in multidimensional dataspaces, as different aspects of relatedness, and we propose a baseline method for computing them, as well as two alternative methods that target speed and scalability. We provide an experimental evaluation over real-world and synthetic datasets and we compare our approach to a SPARQL-based and a rule-based alternative, which prove to be inefficient for increasing input sizes. © 2016, Copyright is with the authors.

Abstract The Data Web refers to the vast and rapidly increasing quantity of scientific, corporate, government and crowd‐sourced data published in the form of Linked Open Data, which encourages the uniform representation of heterogeneous data items on the web and the creation of links between them. The growing availability of open linked datasets has brought forth significant new challenges regarding their proper preservation and the management of evolving information within them. In this paper, we focus on the evolution and preservation challenges related to publishing and preserving evolving linked data across time. We discuss the main problems regarding their proper modelling and querying and provide a conceptual model and a query language for modelling and retrieving evolving data along with changes affecting them. We present in details the syntax of the query language and demonstrate its functionality over a real‐world use case of evolving linked dataset from the biological domain.

The Linked Data paradigm is the most common practice for publishing, sharing and managing information in the Data Web. Linkzoo is an IT infrastructure for collaborative publishing, annotating and sharing of Data Web resources, and their publication as Linked Data. In this paper, we overview LinkZoo and its main components, and we focus on the search facilities provided to retrieve and explore RDF resources. Two search services are presented: (1) an interactive, two-step keyword search service, where live natural language query suggestions are given to the user based on the input keywords and the resource types they match within LinkZoo, and (2) a keyword search service for exploring remote SPARQL endpoints that automatically generates a set of candidate SPARQL queries, i.e., SPARQL queries that try to capture user's information needs as expressed by the keywords used. Finally, we demonstrate the search functionalities through a use case drawn from the life sciences domain.