4,263 research outputs found
Effective searching of RDF knowledge bases
RDF data has become a vital source of information for many applications. In this thesis, we present a set of models and algorithms to effectively search large RDF knowledge bases. These knowledge bases contain a large set of subjectpredicate-object (SPO) triples where subjects and objects are entities and predicates express relationships between them. Searching such knowledge bases can be done using the W3C-endorsed SPARQL language or by similarly designed triple-pattern search. However, the exact-match semantics of triple-pattern search might fall short of satisfying the users needs by returning too many or too few results. Thus, IR-style searching and ranking techniques are crucial. This thesis develops models and algorithms to enhance triple-pattern search. We propose a keyword extension to triple-pattern search that allows users to augment triple-pattern queries with keyword conditions. To improve the recall of triple-pattern search, we present a framework to automatically reformulate triple-pattern queries in such a way that the intention of the original user query is preserved while returning a sufficient number of ranked results. For efficient query processing, we present a set of top-k query processing algorithms and for ease of use, we develop methods for plain keyword search over RDF knowledge bases. Finally, we propose a set of techniques to diversify query results and we present several methods to allow users to interactively explore RDF knowledge bases to find additional contextual information about their query results.Eine Vielzahl aktueller Anwendungen basiert auf RDF-Daten als essentieller Informationsquelle. Daher sind Modelle und Algorithmen zur effizienten Suche in RDF-Wissensdatenbanken ein entscheidender Aspekt, der über Erfolg und Nichterfolg entscheidet. Derartige Datenbanken bestehen aus einer großen Menge von Subjekt-Prädikat-Objekt-Tripeln (SPO-Tripeln), wobei Subjekt und Objekt Entitäten darstellen und Prädikate Beziehungen zwischen diesen Entitäten beschreiben. Suchanfragen werden in der Regel durch Verwendung des W3C Anfragestandards SPARQL oder ähnlich strukturierte Anfragesprachen formuliert und basieren auf Tripel-Patterns. Werden nur exakte Treffer in die Ergebnismenge übernommen, wird das Informationsbedürfnis des Nutzers häufig nicht befriedigt, wenn zu wenige oder zu viele Ergebnisse ausgegeben werden. Techniken, die ihren Ursprung im Information-Retrieval haben, sowie ein geeignetes Ranking können diesem Problem entgegenwirken. Diese Dissertation stellt daher Modelle und Algorithmen zur Verbesserung der Suche basierend auf Tripel-Patterns vor. Die im Rahmen der Dissertation erarbeitete Strategie zur Lösung der oben geschilderten Problematik basiert auf der Idee, die Tripel-Patterns einer Anfrage durch Schlüsselwörter zu erweitern. Um den Recall dieser Suchvariante zu verbessern, wird ein Framework vorgestellt, welches die vom Nutzer übergebenen Anfragen automatisch in einer Weise umformuliert, dass die Intention der ursprünglichen Nutzeranfrage erhalten bleibt und eine ausreichende Anzahl an sortierten Ergebnissen ausgegeben wird. Um derartige Anfragen effizient bearbeiten zu können, werden Top-k Algorithmen und Methoden zur Schlüsselwortsuche auf RDF-Datenbanken vorgestellt. Schließlich werden einige Methoden zur Diversifikation der Anfrageergebnisse präsentiert sowie einige Ansätze vorgestellt, die es Benutzern erlauben, RDFDatenbanken interaktiv zu explorieren und so zusätzliche Kontextinformationen zu den Anfrageergebnissen zu erhalten
Processing SPARQL queries with regular expressions in RDF databases
Background: As the Resource Description Framework (RDF) data model is widely used for modeling and sharing a lot of online bioinformatics resources such as Uniprot (dev.isb-sib.ch/projects/uniprot-rdf) or Bio2RDF (bio2rdf.org), SPARQL - a W3C recommendation query for RDF databases - has become an important query language for querying the bioinformatics knowledge bases. Moreover, due to the diversity of users' requests for extracting information from the RDF data as well as the lack of users' knowledge about the exact value of each fact in the RDF databases, it is desirable to use the SPARQL query with regular expression patterns for querying the RDF data. To the best of our knowledge, there is currently no work that efficiently supports regular expression processing in SPARQL over RDF databases. Most of the existing techniques for processing regular expressions are designed for querying a text corpus, or only for supporting the matching over the paths in an RDF graph.
Results: In this paper, we propose a novel framework for supporting regular expression processing in SPARQL query. Our contributions can be summarized as follows. 1) We propose an efficient framework for processing SPARQL queries with regular expression patterns in RDF databases. 2) We propose a cost model in order to adapt the proposed framework in the existing query optimizers. 3) We build a prototype for the proposed framework in C++ and conduct extensive experiments demonstrating the efficiency and effectiveness of our technique.
Conclusions: Experiments with a full-blown RDF engine show that our framework outperforms the existing ones by up to two orders of magnitude in processing SPARQL queries with regular expression patterns.X113sciescopu
Semantic Query Optimisation with Ontology Simulation
Semantic Web is, without a doubt, gaining momentum in both industry and
academia. The word "Semantic" refers to "meaning" - a semantic web is a web of
meaning. In this fast changing and result oriented practical world, gone are
the days where an individual had to struggle for finding information on the
Internet where knowledge management was the major issue. The semantic web has a
vision of linking, integrating and analysing data from various data sources and
forming a new information stream, hence a web of databases connected with each
other and machines interacting with other machines to yield results which are
user oriented and accurate. With the emergence of Semantic Web framework the
na\"ive approach of searching information on the syntactic web is clich\'e.
This paper proposes an optimised semantic searching of keywords exemplified by
simulation an ontology of Indian universities with a proposed algorithm which
ramifies the effective semantic retrieval of information which is easy to
access and time saving
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
A Framework for Reference Management in the Semantic Web
Much of the semantic web relies upon open and unhindered interoperability between diverse systems. The successful convergence of multiple ontologies and referencing schemes is key. This is hampered by a lack of any means for managing and communicating co-references. We have therefore developed an ontology and framework for the exploration and resolution of potential co-references, in the semantic web at large, that allow the user to a) discover and record uniquely identifying attributes b) interface candidates with and create pipelines of other systems for reference management c) record identified duplicates in a usable and retrievable manner, and d) provide a consistent reference service for accessing them. This paper describes this ontology and a framework of web services designed to support and utilise it
On Reasoning with RDF Statements about Statements using Singleton Property Triples
The Singleton Property (SP) approach has been proposed for representing and
querying metadata about RDF triples such as provenance, time, location, and
evidence. In this approach, one singleton property is created to uniquely
represent a relationship in a particular context, and in general, generates a
large property hierarchy in the schema. It has become the subject of important
questions from Semantic Web practitioners. Can an existing reasoner recognize
the singleton property triples? And how? If the singleton property triples
describe a data triple, then how can a reasoner infer this data triple from the
singleton property triples? Or would the large property hierarchy affect the
reasoners in some way? We address these questions in this paper and present our
study about the reasoning aspects of the singleton properties. We propose a
simple mechanism to enable existing reasoners to recognize the singleton
property triples, as well as to infer the data triples described by the
singleton property triples. We evaluate the effect of the singleton property
triples in the reasoning processes by comparing the performance on RDF datasets
with and without singleton properties. Our evaluation uses as benchmark the
LUBM datasets and the LUBM-SP datasets derived from LUBM with temporal
information added through singleton properties
An Integrated Content and Metadata based Retrieval System for Art
In this paper we describe aspects of the Artiste project to develop a distributed content and metadata based analysis, retrieval and navigation system for a number of major European Museums. In particular, after a brief overview of the complete system, we describe the design and evaluation of some of the image analysis algorithms developed to meet the specific requirements of the users from the museums. These include a method for retrievals based on sub images, retrievals based on very low quality images and retrieval using craquelure type
Ranking Archived Documents for Structured Queries on Semantic Layers
Archived collections of documents (like newspaper and web archives) serve as
important information sources in a variety of disciplines, including Digital
Humanities, Historical Science, and Journalism. However, the absence of
efficient and meaningful exploration methods still remains a major hurdle in
the way of turning them into usable sources of information. A semantic layer is
an RDF graph that describes metadata and semantic information about a
collection of archived documents, which in turn can be queried through a
semantic query language (SPARQL). This allows running advanced queries by
combining metadata of the documents (like publication date) and content-based
semantic information (like entities mentioned in the documents). However, the
results returned by such structured queries can be numerous and moreover they
all equally match the query. In this paper, we deal with this problem and
formalize the task of "ranking archived documents for structured queries on
semantic layers". Then, we propose two ranking models for the problem at hand
which jointly consider: i) the relativeness of documents to entities, ii) the
timeliness of documents, and iii) the temporal relations among the entities.
The experimental results on a new evaluation dataset show the effectiveness of
the proposed models and allow us to understand their limitation
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