Ilianna Kollia

Optimizing SPARQL query answering over OWL ontologies

The SPARQL query language is currently being extended by the World Wide Web Consortium (W3C) with so-called entailment regimes. An entailment regime defines how queries are evaluated under more expressive semantics than SPARQLs standard simple entailment, which is based on subgraph matching. The queries are very expressive since variables can occur within complex concepts and can also bind to concept or role names. In this paper, we describe a sound and complete algorithm for the OWL Direct Semantics entailment regime. We further propose several novel optimizations such as strategies for determining a good query execution order, query rewriting techniques, and show how specialized OWL reasoning tasks and the concept and role hierarchy can be used to reduce the query execution time. For determining a good execution order, we propose a cost-based model, where the costs are based on information about the instances of concepts and roles that are extracted from a model abstraction built by an OWL reasoner. We present two ordering strategies: a static and a dynamic one. For the dynamic case, we improve the performance by exploiting an individual clustering approach that allows for computing the cost functions based on one individual sample from a cluster. We provide a prototypical implementation and evaluate the efficiency of the proposed optimizations. Our experimental study shows that the static ordering usually outperforms the dynamic one when accurate statistics are available. This changes, however, when the statistics are less accurate, e.g., due to nondeterministic reasoning decisions. For queries that go beyond conjunctive instance queries we observe an improvement of up to three orders of magnitude due to the proposed optimizations.

Cost based query ordering over OWL ontologies

The paper presents an approach for cost-based query planning for SPARQL queries issued over an OWL ontology using the OWL Direct Semantics entailment regime of SPARQL 1.1. The costs are based on information about the instances of classes and properties that are extracted from a model abstraction built by an OWL reasoner. A static and a dynamic algorithm are presented which use these costs to find optimal or near optimal execution orders for the atoms of a query. For the dynamic case, we improve the performance by exploiting an individual clustering approach that allows for computing the cost functions based on one individual sample from a cluster. Our experimental study shows that the static ordering usually outperforms the dynamic one when accurate statistics are available. This changes, however, when the statistics are less accurate, e.g., due to non-deterministic reasoning decisions.

A systemic approach for effective semantic access to cultural content

A large on-going activity for digitization, dissemination and preservation of cultural heritage is taking place in Europe and the United States, which involves all types of cultural institutions, i.e., galleries, libraries, museums, archives and all types of cultural content. The development of Europeana, as a single point of access to European Cultural Heritage, has probably been the most important result of the activities in the field till now. Semantic interoperability is a key issue in these developments. This paper presents a system that provides content providers and users with the ability to map, in an effective way, their own metadata schemas to common domain standards and the Europeana (ESE, EDM) data models. Based on these mappings, semantic enrichment and query answering techniques are proposed as a means tbr providing effective access of users to digital cultural heritage. An experimental study is presented involving content from national and thematic content aggregators in Europeana, which illustrates the proposed system capabilities.

Optimizing query answering over OWL ontologies

Query answering is a key reasoning task for many ontology based applications in the Semantic Web. Unfortunately for OWL, the worst case complexity of query answering is very high. That is why, when the schema of an ontology is written in a highly expressive language like OWL 2 DL, currently used query answering systems do not find all answers to queries posed over the ontology, i.e., they are incomplete. In this paper optimizations are discussed that may make query answering over expressive languages feasible in practice. These optimizations mostly focus on the use of traditional database techniques that will be adapted to be applicable to knowledge bases. Moreover, caching techniques and a form of progressive query answering are also explored.

Web Reasoning for Cultural Heritage

Cultural Heritage is the focus of a great and continually increasing number of R&D initiatives, aiming at efficiently managing and disseminating cultural resources on the Web. As more institutions make their collections available online and proceed to aggregate them in domain repositories, knowledge-based management and retrieval becomes a necessary evolution from simple syntactic data exchange. In the process of aggregating heterogeneous resources and publishing them for retrieval and creative reuse, networks such as Europeana and DPLA invest in technologies that achieve semantic data integration. The resulting repositories join the Linked Open Data cloud, allowing to link cultural heritage domain knowledge to existing datasets. Integration of diverse information is achieved through the use of formal ontologies, enabling reasoning services to offer powerful semantic search and navigation mechanisms.

Connectionist Models for Formal Knowledge Adaptation

Both symbolic knowledge representation systems and artificial neural networks play a significant role in Artificial Intelligence. A recent trend in the field aims at interweaving these techniques, in order to improve robustness and performance of classification and clustering systems. In this paper, we present a novel architecture based on the connectionist adaptation of ontological knowledge. The proposed architecture was used effectively to improve image segment classification within a multimedia application scenario.