In this paper, we introduce a family of expressive extensions of Datalog, called Datalog+/-, as a new paradigm for query answering over ontologies. The Datalog+/- family admits existentially quantified variables in rule heads, and has suitable restrictions to ensure highly efficient ontology querying. We show in particular that Datalog+/- generalizes the DL-Lite family of tractable description logics, which are the most common tractable ontology languages in the context of the Semantic Web and databases. We also show how stratified negation can be added to Datalog+/- while keeping ontology querying tractable. Furthermore, the Datalog+/- family is of interest in its own right and can, moreover, be used in various contexts such as data integration and data exchange.

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A general datalog-based framework for tractable query answering over ontologies

This introduction presents the main motivations for the development of Description Logics (DLs) as a formalism for representing knowledge, as well as some important basic notions underlying all systems that have been created in the DL tradition. In addition, we provide the reader with an overview of the entire book and some guidelines for reading it.We first address the relationship between Description Logics and earlier semantic network and frame systems, which represent the original heritage of the field. We delve into some of the key problems encountered with the older efforts. Subsequently, we introduce the basic features of DL languages and related reasoning techniques.DL languages are then viewed as the core of knowledge representation systems. considering both the structure of a DL knowledge base and its associated reasoning services. The development of some implemented knowledge representation systems based on Description Logics and the first applications built with such systems are then reviewed.Finally, we address the relationship of Description Logics to other fields of Computer Science. We also discuss some extensions of the basic representation language machinery; these include features proposed for incorporation in the formalism that originally arose in implemented systems, and features proposed to cope with the needs of certain application domains.

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An Introduction to Description Logic

We present Ontop, an open-source Ontology-Based Data Access (OBDA) system that allows for querying relational data sources through a conceptual representation of the domain of interest, provided in terms of an ontology, to which the data sources are mapped. Key features of Ontop are its solid theoretical foundations, a virtual approach to OBDA, which avoids materializing triples and is implemented through the query rewriting technique, extensive optimizations exploiting all elements of the OBDA architecture, its compliance to all relevant W3C recommendations (including SPARQL queries, R2RML mappings, and OWL2QL and RDFS ontologies), and its support for all major relational databases.

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Ontop: Answering SPARQL queries over relational databases

Growing main memory capacity has fueled the development of in-memory big data management and processing. By eliminating disk I/O bottleneck, it is now possible to support interactive data analytics. However, in-memory systems are much more sensitive to other sources of overhead that do not matter in traditional I/O-bounded disk-based systems. Some issues such as fault-tolerance and consistency are also more challenging to handle in in-memory environment. We are witnessing a revolution in the design of database systems that exploits main memory as its data storage layer. Many of these researches have focused along several dimensions: modern CPU and memory hierarchy utilization, time/space efficiency, parallelism, and concurrency control. In this survey, we aim to provide a thorough review of a wide range of in-memory data management and processing proposals and systems, including both data storage systems and data processing frameworks. We also give a comprehensive presentation of important technology in memory management, and some key factors that need to be considered in order to achieve efficient in-memory data management and processing.

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In-Memory Big Data Management and Processing: A Survey

A general Datalog-based framework for tractable query answering over ontologies

An explosion in the use of RDF for representing information about resources has driven the requirements for Web-scale server systems that can store and process huge quantities of data, and furthermore provide powerful data access and mining functionalities. This paper describes OWLIM, a family of semantic repositories that provide storage, inference and novel data-access features delivered in a scalable, resilient, industrial-strength platform.

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OWLIM: A family of scalable semantic repositories

The advent of Linked Open Data has seen a large number of structured datasets from various domains made available to the public. These datasets are seen as a key enabler for the Semantic Web, where applications can consume and combine this data in powerful and meaningful ways. However, the uptake of Linked Data during this 'introductory phase' is hampered in ways similar to the uptake of any new technology - until the technology has found widespread use, the range of opportunities for exploiting it is limited and until the opportunities are fully explored, the uptake of the technology is restricted. FactForge is a free, publicly available service that provides an easy point of entry for would-be consumers of Linked Data. This Web application is based on OWLIM, a high performance semantic repository that offers outstanding RDF data management and reasoning capabilities based on OWL. The data-exploration functionality provided by FactForge exploits the advanced features of OWLIM to allow users to combine SPARQL with various full-text search and ranking functions for powerful, user-guided data-mining over a number of the most popular LOD datasets. This paper gives an overview of FactForge, its many unique capabilities and its role within the emerging trend for the exploitation of Linked Open Data using OWL-based inference.

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FactForge: a fast track to the web of data

LDSR is a collection of datasets from the Linked Open Data (LOD) W3C community project, which have been selected and refined for the purpose of presenting a useful perspective to some of the central LOD datasets and to present a good use-case for large-scale reasoning and data integration. The design objectives are as fol lows: (i) consistency with respect to the formal semantics, (ii) generality – no specific domain knowledge should be required to comprehend most of the semantics, and (iii) heterogeneity – data from mult iple data sources should be included. The current version of LDSR consists of about 440 million explicit statements and includes DBPedia, Geonames, Wordnet, CIA Factbook, lingvoj, and UMBEL. LDSR includes the ontologies of the datasets and the following schemata, used by them: SKOS, FOAF, RSS, and Dublin Core. Here we report on the materialization of the deductive closure of LDSR performed with the OWLIM semantic repository, which uses a propriety native RDF rule engine. Entailment was performed with respect to a rule-set defining a tractable OWL dialect similar to OWL 2 RL, inferring 1.15 billion statements that have been materialized and indexed along the explicit ones. Although OWLIM performs complete forward-chaining, it does not materialize all the results for performance reasons. Groups of URIs, defined to be equivalent th rough owl:sameAs, are represented in the indices by a single super-node. Upon retrieval request, the repository “expands” the results in accordance with the owl:sameAs semantics. Thus, while the total number of all indexed statements is 1.58 billion, the number of retrievable statements is 2.32 billion . The initial analysis of the results shows that the vast majority of the inferred statements match the expectations dictated by common sense. Although no formal validation has been performed, analysis of the ontologies and schemata used makes us believe that the OWL dialect used is sufficient ly expressive, i.e. that reasoning with respect to a more expressive dialect will not entail additional implicit statements. There is still plenty of room left for analysis of the results and reasoning experiments with respect to various tasks (e.g. inconsistency checking) and OWL dialects. LDSR is available for exploration and query evaluation at http://www.ontotext.com/ldsr/.

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LDSR: Materialized Reason-able View to the Web of Linked Data.

repositories - RDF databases with inferencer and query answering engine - are set to become a cornerstone of the Semantic Web (and Linked Open Data) due to their ability to store and reason with the massive quantities of data involved . In this paper, we describe the features of BigOWLIM that have allowed it to penetrate into the commerci al sector, focusing on one particular use-case, that b eing its use in the BBC's World Cup website.

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Ivan Peikov

Papers

OWLIM: A family of scalable semantic repositories

FactForge: a fast track to the web of data

LDSR: Materialized Reason-able View to the Web of Linked Data.

The Features of BigOWLIM that Enabled the BBC's World Cup Website