Marios Meimaris

Public Spending as LOD: The Case of Greece

The PSGR project is the first attempt to generate, curate, interlink and distribute daily updated public spending data in LOD formats that can be useful to both expert (i.e. scientists and professionals) and naive users. The PSGR ontology is based on the UK payments ontology and reuses, among others, the W3C Registered Organization Vocabulary and the Core Business Vocabulary. RDFized data are linked to product classifications, Geonames and DBpedia resources. Online services contain advanced search features and domain level information (e.g. local government), simple and complex visualizations based on network analysis, linked information about payment entities and SPARQL endpoints. During February 2013, the growing dataset consists of approximately 2 mil. payment decisions valued 44.5 bil. euros forming 65 mil. triples.

A query language for multi-version data web archives

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.

Insights in global public spending

Governments around the globe are opening up public spending data in order to promote transparency and citizen awareness. However, data openness by itself is not enough to guarantee that the data is consumed efficiently and in meaningful ways. In this work public spending data from seven governments, both local and national, with total value almost 1,5 trillion euro, are processed, cleansed and converted to Linked Open Data, following best practices. Namely, the cases of Greece, the UK, the US federal government, Australia, the city of Chicago and the states of Alaska and Massachusetts are considered. Furthermore, the resulting Linked Data are interlinked with external resources and made accessible on a public SPARQL endpoint. A web portal application with several functionalities is deployed in order to make the data mashups understandable and easily consumable.

Weaving the Economic Linked Open Data

Efforts in integrating the basic economic functions under a common or compatible context could be accelerated by enabling semantic processing of the underlying data. We establish the basic flows among public budgeting, contracting and spending with business information and provide the necessary ontological elements that would integrate them in economic Linked Open Data corpus.

LinkZoo: A Linked Data Platform for Collaborative Management of Heterogeneous Resources

Modern collaborations rely on sharing and reusing heterogeneous resources. The ability to combine different types of information objects in semantically meaningful ways becomes a necessity for the information-intensive requirements of collaborative environments. In this paper we present LinkZoo, a web-based, linked data enabled platform that allows users to create, manage, discover and search heterogeneous resources such as files, web documents, people and events, interlink them, annotate them, exploit their inherent structures, enrich them with semantics and make them available as linked data. LinkZoo easily and intuitively allows for dynamic communities that enable web-based collaboration through resource sharing and annotating, exposing objects on the Linked Data Web under controlled vocabularies and permissions.

Extended Characteristic Sets: Graph Indexing for SPARQL Query Optimization

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.

RDF Resource Search and Exploration with LinkZoo

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 userâ??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.

Introducing Solon: A Semantic Platform for Managing Legal Sources

In this paper we introduce Solon, a legal document management platform aiming to improve access to legal sources by offering advanced modelling, managing and mining functions. It utilizes a novel method for extracting semantic representations of legal sources from unstructured formats, interlinking and enriching them with advanced classification features. Also, it provides refined search results utilizing the structure and specific features of legal sources, allowing users to connect and explore legal resources according to individual needs.

Distance-Based Triple Reordering for SPARQL Query Optimization

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.

Enabling persistent identification of groups of duplicates in data aggregators

Data aggregators harvest, deduplicate and make available content from disparate data sources in different domains, such as cultural information, academic, and scientific content. The availability of aggregated data in the form of Linked Data is subject to the evolution of information at the data sources, thus proper handling is necessary for published data to comply with Linked Data guidelines, such as persistent identification through time. In this paper we present the problem of disambiguating groups of duplicates in settings where the Information Space is regenerated at its whole in every harvesting cycle of data aggregation and propose an approach that aims at providing persistent identifiers for groups through time.