RDF Repositories Research Articles

The Jazz Ontology: A semantic model and large-scale RDF repositories for jazz

Jazz is a musical tradition that is just over 100 years old; unlike in other Western musical traditions, improvisation plays a central role in jazz. Modelling the domain of jazz poses some ontological challenges due to specificities in musical content and performance practice, such as band lineup fluidity and importance of short melodic patterns for improvisation. This paper presents the Jazz Ontology – a semantic model that addresses these challenges. Additionally, the model also describes workflows for annotating recordings with melody transcriptions and for pattern search. The Jazz Ontology incorporates existing standards and ontologies such as FRBR and the Music Ontology. The ontology has been assessed by examining how well it supports describing and merging existing datasets and whether it facilitates novel discoveries in a music browsing application. The utility of the ontology is also demonstrated in a novel framework for managing jazz related music information. This involves the population of the Jazz Ontology with the metadata from large scale audio and bibliographic corpora (the Jazz Encyclopedia and the Jazz Discography). The resulting RDF datasets were merged and linked to existing Linked Open Data resources. These datasets are publicly available and are driving an online application that is being used by jazz researchers and music lovers for the systematic study of jazz.

Link maintenance for integrity in linked open data evolution: Literature survey and open challenges

RDF data has been extensively deployed describing various types of resources in a structured way. Links between data elements described by RDF models stand for the core of Semantic Web. The rising amount of structured data published in public RDF repositories, also known as Linked Open Data, elucidates the success of the global and unified dataset proposed by the vision of the Semantic Web. Nowadays, semi-automatic algorithms build connections among these datasets by exploring a variety of methods. Interconnected open data demands automatic methods and tools to maintain their consistency over time. The update of linked data is considered as key process due to the evolutionary characteristic of such structured datasets. However, data changing operations might influence well-formed links, which turns difficult to maintain the consistencies of connections over time. In this article, we propose a thorough survey that provides a systematic review of the state of the art in link maintenance in linked open data evolution scenario. We conduct a detailed analysis of the literature for characterising and understanding methods and algorithms responsible for detecting, fixing and updating links between RDF data. Our investigation provides a categorisation of existing approaches as well as describes and discusses existing studies. The results reveal an absence of comprehensive solutions suited to fully detect, warn and automatically maintain the consistency of linked data over time.

WODII: a solution to process SPARQL queries over distributed data sources

The web of data can be seen as a distributed environment hosting structured and linked data based on Semantic Web standards. This is one of the promising features for Semantic Web developers who would benefit from having the possibility to remotely access different RDF repositories, available on the web, in order to collect fragments of information from several sources and combine the resulting parts in an integrated answer. In this paper, we propose an index-based solution, Web of Data Information Integrator (WoDII), to process SPARQL queries over independent data sources without having a prior knowledge of the sources contributing to the answer. By relying on an index, the system avoids non-relevant sources and maps each selected source to a cluster of sub-queries, as a result, network traffic decreases, making the process less dependent on the quality of the connection flow.

COMBINING OF METHODS OF CONSTRAINT PROPAGATION AND STRUCTURAL DECOMPOSITION FOR THE A PRIORI ANALYSIS OF QUERIES TO THE ONTOLOGIES

Unlike relational DBMS that focused on maintaining of the referential integrity of rapidly changing transactional data, while using of RDF repositories which are storages for ontologies, there is no need to operate with such atomic structures as a tuple and you can use more suitable structures for grouping and summarizing of information. The article considers ontology as a set of relations (unary and binary), which are represented by specialized matrix-like structures – C-systems. That allows us to consider tasks of inference on ontologies as constraint satisfaction problems. The problem of a priori analysis and simplification of the SPARQL queries, considered in the article, is solved for ontologies that are developed using content ontology design patterns, which ensures the predictability of the structure of potential queries. In fact, each pattern is associated with a set of patterns of SPARQL queries. A method of a priori analysis and transformation of SPARQL queries patterns into a form, which speeds up the subsequent execution of concrete user queries has been developed. The method is based on the combining of the methods of structural decomposition and the author's methods for non-numeric constraints satisfaction. The structural decomposition methods make it possible to split the SPARQL query pattern into parts and parallelize its execution, which is especially important when accessing large volume RDF repositories. Due to the low frequency of modifications of the RDF repository, it is possible, based on the analysis of templates, to prepare data structures for the subsequent execution of concrete queries. This allows you to avoid unnecessary overhead incurred by repeated execution of the operation of joining the same relations when processing user requests described by a common pattern. For the join of relations, expressed as a set of C-systems, author's methods of non-numeric constraints satisfaction are applied, which are modifications of the known methods of maintaining node and arc consistency. As a result, the proposed approach to the ontology presentation and organization of inference procedures on ontologies allows to reduce the memory consumption and to ensure an acceptable speed of execution of SPARQL queries even for RDF repositories containing large amounts of data.

Processing SPARQL queries over distributed RDF graphs

We propose techniques for processing SPARQL queries over a large RDF graph in a distributed environment. We adopt a evaluation and assembly framework. Answering a SPARQL query Q is equivalent to finding subgraph matches of the query graph Q over RDF graph G. Based on properties of subgraph matching over a distributed graph, we introduce local partial match as partial answers in each fragment of RDF graph G. For assembly, we propose two methods: centralized and distributed assembly. We analyze our algorithms from both theoretically and experimentally. Extensive experiments over both real and benchmark RDF repositories of billions of triples confirm that our method is superior to the state-of-the-art methods in both the system's performance and scalability.

Faceted Search Over RDF-Based Knowledge Graphs

Knowledge graphs such as Yago and Freebase have become a powerful asset for enhancing search, and are being intensively used in both academia and industry. Many existing knowledge graphs are either available as Linked Open Data, or they can be exported as RDF datasets enhanced with background knowledge in the form of an OWL 2 ontology. Faceted search is the de facto approach for exploratory search in many online applications, and has been recently proposed as a suitable paradigm for querying RDF repositories. In this paper, we provide rigorous theoretical underpinnings for faceted search in the context of RDFbased knowledge graphs enhanced with OWL 2 ontologies. We identify well-defined fragments of SPARQL that can be naturally captured using faceted search as a query paradigm, and establish the computational complexity of answering such queries. We also study the problem of updating faceted interfaces, which is critical for guiding users in the formulation of meaningful queries during exploratory search. We have implemented our approach in a fully-fledged faceted search system, SemFacet, which we have evaluated over the Yago knowledge graph.

Faceted search over RDF-based knowledge graphs

Knowledge graphs such as Yago and Freebase have become a powerful asset for enhancing search, and are being intensively used in both academia and industry. Many existing knowledge graphs are either available as Linked Open Data, or they can be exported as RDF datasets enhanced with background knowledge in the form of an OWL 2 ontology. Faceted search is the de facto approach for exploratory search in many online applications, and has been recently proposed as a suitable paradigm for querying RDF repositories. In this paper, we provide rigorous theoretical underpinnings for faceted search in the context of RDF-based knowledge graphs enhanced with OWL 2 ontologies. We identify well-defined fragments of SPARQL that can be naturally captured using faceted search as a query paradigm, and establish the computational complexity of answering such queries. We also study the problem of updating faceted interfaces, which is critical for guiding users in the formulation of meaningful queries during exploratory search. We have implemented our approach in a fully-fledged faceted search system, SemFacet, which we have evaluated over the Yago knowledge graph.

Dynamic and fast processing of queries on large-scale RDF data

As RDF data continue to gain popularity, we witness the fast growing trend of RDF datasets in both the number of RDF repositories and the size of RDF datasets. Many known RDF datasets contain billions of RDF triples (subject, predicate and object). One of the grant challenges for managing these huge RDF data is how to execute RDF queries efficiently. In this paper, we address the query processing problems against the billion triple challenges. We first identify some causes for the problems of existing query optimization schemes, such as large intermediate results, initial query cost estimation errors. Then, we present our block-oriented dynamic query plan generation approach powered with pipelining execution. Our approach consists of two phases. In the first phase, a near-optimal execution plan for queries is chosen by identifying the processing blocks of queries. We group the join patterns sharing a join variable into building blocks of the query plan since executing them first provides opportunities to reduce the size of intermediate results generated. In the second phase, we further optimize the initial pipelining for a given query plan. We employ optimization techniques, such as sideways information passing and semi-join, to further reduce the size of intermediate results, improve the query processing cost estimation and speed up the performance of query execution. Experimental results on several RDF datasets of over a billion triples demonstrate that our approach outperforms existing RDF query engines that rely on dynamic programming based static query processing strategies.

GStore: a graph-based SPARQL query engine

We address efficient processing of SPARQL queries over RDF datasets. The proposed techniques, incorporated into the gStore system, handle, in a uniform and scalable manner, SPARQL queries with wildcards and aggregate operators over dynamic RDF datasets. Our approach is graph based. We store RDF data as a large graph and also represent a SPARQL query as a query graph. Thus, the query answering problem is converted into a subgraph matching problem. To achieve efficient and scalable query processing, we develop an index, together with effective pruning rules and efficient search algorithms. We propose techniques that use this infrastructure to answer aggregation queries. We also propose an effective maintenance algorithm to handle online updates over RDF repositories. Extensive experiments confirm the efficiency and effectiveness of our solutions.

Special issue on structured and crowd-sourced data on the Web

The abundance of structured and social data on theWeb coupled with ability to solicit feedback from crowds has the potential of changing the way we search for information and enabling new classes of applications on the Web. This special issue of theVLDBJournal features original contributions that advance the state of the art in this topic area. Broadly, the special issue is concernedwithmethods for analyzing and serving structured data on the Web and methods for enhancing data by soliciting feedback from crowds. Structured data appear on the Web in several forms, including hiddenWeb sources exposed through HTML form interfaces, tables, lists, and pages with repeating semistructured cards. Current research efforts for leveraging this data include approaches for extracting and combining results from multiple sources, for surfacing the deep web, and for exposing data through RDF repositories with rich linking, possibly exploiting existing knowledge bases for data annotation and integration. Crowd-sourcing and social data are increasingly popular methods for improving search results and enhancing the quality of data on the Web. Social data have huge potential to re-rank and enrich pages and content based on what the user’s friends have visited or recommended previously. Crowd-sourcing can be used to answer questions that are inherently hard for machines but can be handled relatively

Improving habitability of natural language interfaces for querying ontologies with feedback and clarification dialogues

Natural Language Interfaces (NLIs) are a viable, human-readable alternative to complex, formal query languages like SPARQL, which are typically used for accessing semantically structured data (e.g. RDF and OWL repositories). However, in order to cope with natural language ambiguities, NLIs typically support a more restricted language. A major challenge when designing such restricted languages is habitability–how easily, naturally and effectively users can use the language to express themselves within the constraints imposed by the system. In this paper, we investigate two methods for improving the habitability of a Natural Language Interface: feedback and clarification dialogues. We model feedback by showing the user how the system interprets the query, thus suggesting repair through query reformulation. Next, we investigate how clarification dialogues can be used to control the query interpretations generated by the system. To reduce the cognitive overhead, clarification dialogues are coupled with a learning mechanism. Both methods are shown to have a positive effect on the overall performance and habitability.

GStore

Due to the increasing use of RDF data, efficient processing of SPARQL queries over RDF datasets has become an important issue. However, existing solutions suffer from two limitations: 1) they cannot answer SPARQL queries with wildcards in a scalable manner; and 2) they cannot handle frequent updates in RDF repositories efficiently. Thus, most of them have to reprocess the dataset from scratch. In this paper, we propose a graph-based approach to store and query RDF data. Rather than mapping RDF triples into a relational database as most existing methods do, we store RDF data as a large graph. A SPARQL query is then converted into a corresponding subgraph matching query. In order to speed up query processing, we develop a novel index, together with some effective pruning rules and efficient search algorithms. Our method can answer exact SPARQL queries and queries with wildcards in a uniform manner. We also propose an effective maintenance algorithm to handle online updates over RDF repositories. Extensive experiments confirm the efficiency and effectiveness of our solution.

Java Framework for Managing Semantic Repositories Based on RDF Standard

This paper presents a new approach for managing RDF repositories without necessary knowledge or experiences with RDF standard. The main differences between proposed solution and existing ones are adaptability on various semantic repositories, internal memory model for manipulation with RDF graphs and utilization of the Java annotations for mapping between ontology and java classes. Persistence API (P-API) represents Java library offered features for separation of business logic from RDF and allows simple integration of the semantic applications with selected RDF repository. The developers don't need to work directly with RDF query languages; they work only with Java classes. The P-API can also help developers with manipulation within RDF graphs constructed from data stored in repository, as the proposed RDF Memory model fits perfectly with such purposes. This Memory model can be used for more complex and straightforward manipulation with ontological knowledge models. The P-API was designed and implemented as a supporting solution on the middleware layer of the collaborative system in order to make bilateral communication effective and faster. The exploitation within KP-Lab system is described in a section no.3 as an illustrative example for possible integration approach.

SPARQL queries to RDFS views of Topic Maps

Both Topic Maps and RDF are popular semantic web standards designed for machine processing of web documents. Since these representations were originally created for different purposes, they have differences both in their concepts and in their data models. The Topic Map data model can be seen as an ontology for building indices over web documents, whereas RDF is a language to define arbitrary properties of web resources. Ontologies can be defined using the RDF vocabulary description language RDF-Schema (RDFS) language. RDF(S) repositories can be searched using the query language SPARQL. To make Topic Maps exposed to RDF-based tools and searchable with SPARQL, our approach is to map Topic Maps to a view expressed in RDFS. The view can be queried using SPARQL. The problem concerning efficient query processing of SPARQL queries to the rather complex RDF view of Topic Maps has been studied in detail. Our approach has been applied on searching indices to e-government services.

System Π: A Native RDF Repository Based on the Hypergraph Representation for RDF Data Model

RDF is the data interchange layer for the Semantic Web. In order to manage the increasing amount of RDF data, an RDF repository should provide not only the necessary scalability and efficiency, but also sufficient inference capabilities. Though existing RDF repositories have made progress towards these goals, there is still ample space for improving the overall performance. In this paper, we propose a native RDF repository, System Π, to pursue a better tradeoff among system scalability, query efficiency, and inference capabilities. System Π takes a hypergraph representation for RDF as the data model for its persistent storage, which effectively avoids the costs of data model transformation when accessing RDF data. Based on this native storage scheme, a set of efficient semantic query processing techniques are designed. First, several indices are built to accelerate RDF data access including a value index, a labeling scheme for transitive closure computation, and three triple indices. Second, we propose a hybrid inference strategy under the pD * semantics to support inference for OWL-Lite with a relatively low computational complexity. Finally, we extend the SPARQL algebra to explicitly express inference semantics in logical query plan by defining some new algebra operators. In addition, MD5 hash value of URI and schema level cache are introduced as practical implementation techniques. The results of performance evaluation on the LUBM benchmark and a real data set show that System Π has a better combined metric value than other comparable systems.

Towards distributed processing of RDF path queries

A technical infrastructure for storing, querying and managing RDF data is a key element in the current semantic web development. Systems like Jena, Sesame or the ICS-FORTH RDF Suite are widely used for building semantic web applications. Currently, none of these systems support the integrated querying of distributed RDF repositories. We consider this a major shortcoming since the semantic web is distributed by nature. In this paper we present an architecture for querying distributed RDF repositories by extending the existing Sesame system. We discuss the implications of our architecture and propose an index structure as well as algorithms for query processing and optimisation in such a distributed context.

KIM – a semantic platform for information extraction and retrieval

The KIM platform provides a novel Knowledge and Information Management framework and services for automatic semantic annotation, indexing, and retrieval of documents. It provides a mature and semantically enabled infrastructure for scalable and customizable information extraction (IE) as well as annotation and document management, based on GATE.General Architecture for Text Engineering (GATE) (http://gate.ac.uk), leading NLP and IE platform developed at the University of Sheffield. Our understanding is that a system for semantic annotation should be based upon a simple model of real-world entity concepts, complemented with quasi-exhaustive instance knowledge. To ensure efficiency, easy sharing, and reusability of the metadata we introduce an upper-level ontology. Based on the ontology, a large-scale instance base of entity descriptions is maintained. The knowledge resources involved are handled by use of state-of-the-art Semantic Web technology and standards, including RDF(S) repositories, ontology middleware and reasoning. From a technical point of view, the platform allows KIM-based applications to use it for automatic semantic annotation, for content retrieval based on semantic queries, and for semantic repository access. As a framework, KIM also allows various IE modules, semantic repositories and information retrieval engines to be plugged into it. This paper presents the KIM platform, with an emphasis on its architecture, interfaces, front-ends, and other technical issues.

SECO: mediation services for semantic Web data

SECO (semantic collaboration), an infrastructure that lets agents uniformly access data that is potentially scattered across the Web is described. The SECO system provides mediation services that aggregate, integrate, and display RDF data obtained from the semantic Web. Using a crawler, SECO collects the RDF data available in files and uses RDF repositories as data sources. SECO applies semantic Web technologies to RDF data by combining techniques from information retrieval and semistructured databases.