New Forms of Reasoning for the Semantic Web: Scalable & Dynamic (original) (raw)

StreamRule: A Nonmonotonic Stream Reasoning System for the Semantic Web

Lecture Notes in Computer Science, 2013

Stream reasoning is an emerging research field focused on dynamic processing and continuous reasoning over huge volumes of streaming data. Finding the right trade-off between scalability and expressivity is a key challenge in this area. In this paper, we want to provide a baseline for exploring the applicability of complex reasoning to the Web of Data based on a solution that combines results and approaches from database research, stream processing, and nonmonotonic logic programming.

SAOR: Scalable Reasoning for the Web

W3C recommendation, publishing open and machine-readable content on the Web has recently received a lot more attention, including from corporate and governmental bodies; there now exists a rich vein of heterogeneous RDF data published on the Web (the so-called “Web of Data”) accessible to all.

Strider-lsa: Massive RDF Stream Reasoning in the Cloud

ArXiv, 2017

Reasoning over semantically annotated data is an emerging trend in stream processing aiming to produce sound and complete answers to a set of continuous queries. It usually comes at the cost of finding a trade-off between data throughput and the cost of expressive inferences. Strider-lsa proposes such a trade-off and combines a scalable RDF stream processing engine with an efficient reasoning system. The main reasoning tasks are based on a query rewriting approach for SPARQL that benefits from an intelligent encoding of RDFS+ (RDFS + owl:sameAs) ontology elements. Strider-lsa runs in production at a major international water management company to detect anomalies from sensor streams. The system is evaluated along different dimensions and over multiple datasets to emphasize its performance.

Streaming the Web: Reasoning over dynamic data

Web Semantics: Science, Services and Agents on the World Wide Web, 2014

In the last few years a new research area, called stream reasoning, emerged to bridge the gap between reasoning and stream processing. While current reasoning approaches are designed to work on mainly static data, the Web is, on the other hand, extremely dynamic: information is frequently changed and updated, and new data is continuously generated from a huge number of sources, often at high rate. In other words, fresh information is constantly made available in the form of streams of new data and updates.

BigSR: an empirical study of real-time expressive RDF stream reasoning on modern Big Data platforms

ArXiv, 2018

The trade-off between language expressiveness and system scalability (E&S) is a well-known problem in RDF stream reasoning. Higher expressiveness supports more complex reasoning logic, however, it may also hinder system scalability. Current research mainly focuses on logical frameworks suitable for stream reasoning as well as the implementation and the evaluation of prototype systems. These systems are normally developed in a centralized setting which suffer from inherent limited scalability, while an in-depth study of applying distributed solutions to cover E&S is still missing. In this paper, we aim to explore the feasibility of applying modern distributed computing frameworks to meet E&S all together. To do so, we first propose BigSR, a technical demonstrator that supports a positive fragment of the LARS framework. For the sake of generality and to cover a wide variety of use cases, BigSR relies on the two main execution models adopted by major distributed execution frameworks: B...

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

LDSR is a collection of datasets from th e Linked Open Data (LOD) W3C commun it y project, which have been selected and refin ed for th e purpose of presenting a us eful perspective to some of th e 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 semanti cs, (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 expli cit statements and includes DBP edia, Geonames, Wordnet, CIA Factbook, li ngvoj, and UMBEL. LDSR includes the ontologi es of th e datasets and th e following schemata, used by them: SKOS, FOAF, RSS, and Dublin Core.

Incremental Reasoning on Streams and Rich Background Knowledge

2010

This article presents a technique for Stream Reasoning, consisting in incremental maintenance of materializations of ontological entailments in the presence of streaming information. Previous work, delivered in the context of deductive databases, describes the use of logic programming for the incremental maintenance of such entailments. Our contribution is a new technique that exploits the nature of streaming data in order to efficiently maintain materialized views of RDF triples, which can be used by a reasoner. By adding expiration time information to each RDF triple, we show that it is possible to compute a new complete and correct materialization whenever a new window of streaming data arrives, by dropping explicit statements and entailments that are no longer valid, and then computing when the RDF triples inserted within the window will expire. We provide experimental evidence that our approach significantly reduces the time required to compute a new materialization at each window change, and opens up for several further optimizations.

Scalable OWL 2 reasoning for linked data

2011

The goal of the Scalable OWL 2 Reasoning for Linked Data lecture is twofold: first, to introduce scalable reasoning and querying techniques to Semantic Web researchers as powerful tools to make use of Linked Data and large-scale ontologies, and second, to present interesting research problems for the Semantic Web that arise in dealing with TBox and ABox reasoning in OWL 2. The lecture consists of three parts.

Scalable Reasoning and Querying for the Semantic Web

ABSTRACT Ontologies, in the OWL language, form the basis of the Semantic Web: they define concepts and relationships, and are thus the fundamental model for encoding information. As the Semantic Web has been more widely adopted, extremely large ontologies have begun to emerge, particularly in the life sciences. Reasoning about such ontologies requires scalability beyond that of current Description Logic-based reasoning tools.

Reasoning on web data: Algorithms and performance

2015 IEEE 31st International Conference on Data Engineering, 2015

Techniques for efficiently managing Semantic Web data have attracted significant interest from the data management and knowledge representation communities. A great deal of effort has been invested, especially in the database community, into algorithms and tools for efficient RDF query evaluation. However, the main interest of RDF lies in its blending of heterogeneous data and semantics. Simple RDF graphs can be seen as collections of facts, which may be further enriched with ontological schemas, or semantic constraints, based on which reasoning can be applied to infer new information. Taking into account this implicit information is crucial for answering queries.