Yannis Theoharis

Benchmarking database representations of RDF/S stores

In this paper we benchmark three popular database representations of RDF/S schemata and data: (a) a schema-aware (i.e., one table per RDF/S class or property) with explicit (ISA) or implicit (NOISA) storage of subsumption relationships, (b) a schema-oblivious (i.e., a single table with triples of the form 〈subject-predicate-object〉), using (ID) or not (URI) identifiers to represent resources and (c) a hybrid of the schema-aware and schema-oblivious representations (i.e., one table per RDF/S meta-class by distinguishing also the range type of properties). Furthermore, we benchmark two common approaches for evaluating taxonomic queries either on-the-fly (ISA, NOISA, Hybrid), or by precomputing the transitive closure of subsumption relationships (MatView, URI, ID). The main conclusion drawn from our experiments is that the evaluation of taxonomic queries is most efficient over RDF/S stores utilizing the Hybrid and MatView representations. Of the rest, schema-aware representations (ISA, NOISA) exhibit overall better performance than URI, which is superior to that of ID, which exhibits the overall worst performance.

Coloring RDF Triples to Capture Provenance

Recently, the W3C Linking Open Data effort has boosted the publication and inter-linkage of large amounts of RDF datasets on the Semantic Web. Various ontologies and knowledge bases with millions of RDF triples from Wikipedia and other sources, mostly in e-science, have been created and are publicly available. Recording provenance information of RDF triples aggregated from different heterogeneous sources is crucial in order to effectively support trust mechanisms, digital rights and privacy policies. Managing provenance becomes even more important when we consider not only explicitly stated but also implicit triples (through RDFS inference rules) in conjunction with declarative languages for querying and updating RDF graphs. In this paper we rely on colored RDF triples represented as quadruples to capture and manipulate explicit provenance information.

On Graph Features of Semantic Web Schemas

In this paper, we measure and analyze the graph features of semantic Web (SW) schemas with focus on power-law degree distributions. Our main finding is that the majority of SW schemas with a significant number of properties (respectively, classes) approximate a power law for total-degree (respectively, the number of subsumed classes) distribution. Moreover, our analysis revealed some emerging conceptual modeling practices of SW schema developers: (1) each schema has a few focal classes that have been analyzed in detail (that is, they have numerous properties and subclasses), which are further connected with focal classes defined in other schemas, (2) class subsumption hierarchies are mostly unbalanced (that is, some branches are deep and heavy, while others are shallow and light), (3) most properties have as domain/range classes that are located high at the class subsumption hierarchies, and (4) the number of recursive/multiple properties is significant. The knowledge of these features is essential for guiding synthetic SW schema generation, which is an important step toward benchmarking SW repositories and query language implementations.

On Provenance of Queries on Semantic Web Data

Capturing trustworthiness, reputation, and reliability of Semantic Web data manipulated by SPARQL requires researchers to represent adequate provenance information, usually modeled as source data annotations and propagated to query results along with a query evaluation. Alternatively, abstract provenance models can capture the relationship between query results and source data by taking into account the employed query operators. The authors argue the benefits of the latter for settings in which query results are materialized in several repositories and analyzed by multiple users. They also investigate how relational provenance models can be leveraged for SPARQL queries, and advocate for new provenance models.

On storage policies for semantic web repositories that support versioning

This paper concerns versioning services over Semantic Web (SW) repositories. We propose a novel storage index (based on partial orders), called POI, that exploits the fact that RDF Knowledge Bases (KB) have not a unique serialization (as it happens with texts). POI can be used for storing several (version-related or not) SW KBs. We discuss the benefits and drawbacks of this approach in terms of storage space and efficiency both analytically and experimentally in comparison with the existing approaches (including the change-based approach). For the latter case we report experimental results over synthetic data sets. POI offers notable space saving as well as efficiency in various cross version operations. It is equipped with an efficient version insertion algorithm and could be also exploited in cases where the set of KBs does not fit in main memory.

Mitos: Design and Evaluation of a DBMS-Based Web Search Engine

Engineering a Web search engine offering effective and efficient information retrieval is a challenging task. Mitos is a recently developed search engine that offers a wide spectrum of functionalities. A rather unusual design choice is that its index is based on an object-relational database system, instead of the classical inverted file. This paper discusses the benefits and the drawbacks of this choice (compared to inverted files), proposes three different database representations, and reports comparative experimental results. Two of these representations are one order of magnitude more space economical and two orders of magnitude faster in query evaluation, than the plain relational representation.

On the Synthetic Generation of Semantic Web Schemas

In order to cope with the expected size of the Semantic Web ( SW ) in the coming years, we need to benchmark existing SW tools (e.g., query language interpreters) in a credible manner. In this paper we present the first RDFS schema generator, termed PoweRGen, which takes into account the morphological features that schemas frequently exhibit in reality. In particular, we are interested in generating synthetically the two core components of an RDFS schema, namely the property(relationships between classes or attributes) and the subsumption(subsumption relationships among classes) graph. The total-degree distribution of the former, as well as the out-degree distribution of the Transitive Closure ( TC ) of the latter, usually follow a power-law . PoweRGen produces synthetic property and subsumption graphs whose distributions respect the power-law exponents given as input with a confidence ranging between 90 ? 98%.

PoweRGen: A power-law based generator of RDFS schemas

As the amount of RDF datasets available on the Web has grown significantly over the last years, scalability and performance of Semantic Web (SW) systems are gaining importance. Current RDF benchmarking efforts either consider schema-less RDF datasets or rely on fixed RDFS schemas. In this paper, we present the first RDFS schema generator, termed PoweRGen, which takes into account the features exhibited by real SW schemas. It considers the power-law functions involved in (a) the combined in- and out-degree distribution of the property graph (which captures the domains and ranges of the properties defined in a schema) and (b) the out-degree distribution of the transitive closure (TC) of the subsumption graph (which essentially captures the class hierarchy). The synthetic schemas generated by PoweRGen respect the power-law functions given as input with an accuracy ranging between 89 and 96%, as well as, various morphological characteristics regarding the subsumption hierarchy depth, structure, etc.