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Searching and Browsing Linked Data with SWSE: the Semantic Web Search Engine
, 2011
"... In this paper, we discuss the architecture and implementation of the Semantic Web Search Engine (SWSE). Following traditional search engine architecture, SWSE consists of crawling, data enhancing, indexing and a user interface for search, browsing and retrieval of information; unlike traditional sea ..."
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Cited by 49 (15 self)
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In this paper, we discuss the architecture and implementation of the Semantic Web Search Engine (SWSE). Following traditional search engine architecture, SWSE consists of crawling, data enhancing, indexing and a user interface for search, browsing and retrieval of information; unlike traditional search engines, SWSE operates over RDF Web data – loosely also known as Linked Data – which implies unique challenges for the system design, architecture, algorithms, implementation and user interface. In particular, many challenges exist in adopting Semantic Web technologies for Web data: the unique challenges of the Web – in terms of scale, unreliability, inconsistency and noise – are largely overlooked by the current Semantic Web standards. Herein, we describe the current SWSE system, initially detailing the architecture and later elaborating upon the function, design, implementation and performance of each individual component. In so doing, we also give an insight into how current Semantic Web standards can be tailored, in a besteffort manner, for use on Web data. Throughout, we offer evaluation and complementary argumentation to support our design choices, and also offer discussion on future directions and open research questions. Later, we also provide candid discussion relating to the difficulties currently faced in bringing such a search engine into the mainstream, and lessons learnt from roughly six years working on the Semantic Web Search Engine project.
Representing and querying validity time in RDF and OWL: a logicbased approach. In:
- Proceedings of the 9th international semantic web conference on The semantic web - Volume Part I.
, 2010
"... Abstract. RDF(S) and OWL 2 currently support only static ontologies. In practice, however, the truth of statements often changes with time, and Semantic Web applications often need to represent such changes and reason about them. In this paper we present a logic-based approach for representing vali ..."
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Cited by 21 (0 self)
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Abstract. RDF(S) and OWL 2 currently support only static ontologies. In practice, however, the truth of statements often changes with time, and Semantic Web applications often need to represent such changes and reason about them. In this paper we present a logic-based approach for representing validity time in RDF and OWL. Unlike the existing proposals, our approach is applicable to entailment relations that are not deterministic, such as the Direct Semantics or the RDF-Based Semantics of OWL 2. We also extend SPARQL to temporal RDF graphs and present a query evaluation algorithm. Finally, we present an optimization of our algorithm that is applicable to entailment relations characterized by a set of deterministic rules, such RDF(S) and OWL 2 RL/RDF entailment.
Federated Data Management and Query Optimization for Linked Open Data
"... The automatic processing of information from the World Wide Web requires that data is available in a structured and machine readable format. The Linking Open Data initiative 1 actively promotes and supports the publication and interlinking of so called Linked Open Data from various sources and domai ..."
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Cited by 18 (1 self)
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The automatic processing of information from the World Wide Web requires that data is available in a structured and machine readable format. The Linking Open Data initiative 1 actively promotes and supports the publication and interlinking of so called Linked Open Data from various sources and domains. Its main objective
H2RDF: Adaptive Query Processing on RDF Data in the Cloud.
, 2012
"... In this work we present H2RDF, a fully distributed RDF store that combines the MapReduce processing framework with a NoSQL distributed data store. Our system features two unique characteristics that enable efficient processing of both simple and multi-join SPARQL queries on virtually unlimited numbe ..."
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Cited by 13 (3 self)
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In this work we present H2RDF, a fully distributed RDF store that combines the MapReduce processing framework with a NoSQL distributed data store. Our system features two unique characteristics that enable efficient processing of both simple and multi-join SPARQL queries on virtually unlimited number of triples: Join algorithms that execute joins according to query selectivity to reduce processing; and adaptive choice among centralized and distributed (MapReduce-based) join execution for fast query responses. Our system efficiently answers both simple joins and complex multivariate queries and easily scales to 3 billion triples using a small cluster of 9 worker nodes. H2RDF outperforms state-of-the-art distributed solutions in multi-join and nonselective queries while achieving comparable performance to centralized solutions in selective queries. In this demonstration we showcase the system’s functionality through an interactive GUI. Users will be able to execute predefined or custom-made SPARQL queries on datasets of different sizes, using different join algorithms. Moreover, they can repeat all queries utilizing a different number of cluster resources. Using real-time cluster monitoring and detailed statistics, participants will be able to understand the advantages of different execution schemes versus the input data as well as the scalability properties of H2RDF over both the data size and the available worker resources.
Triad: a distributed shared-nothing rdf engine based on asynchronous message passing
- In Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, SIGMOD ’14
, 2014
"... We investigate a new approach to the design of distributed, shared-nothing RDF engines. Our engine, coined “TriAD”, combines join-ahead pruning via a novel form of RDF graph summarization with a locality-based, horizontal partitioning of RDF triples into a grid-like, distributed index structure. The ..."
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Cited by 11 (0 self)
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We investigate a new approach to the design of distributed, shared-nothing RDF engines. Our engine, coined “TriAD”, combines join-ahead pruning via a novel form of RDF graph summarization with a locality-based, horizontal partitioning of RDF triples into a grid-like, distributed index structure. The multi-threaded and distributed execution of joins in TriAD is facilitated by an asynchronous Mes-sage Passing protocol which allows us to run multiple join oper-ators along a query plan in a fully parallel, asynchronous fashion. We believe that our architecture provides a so far unique approach to join-ahead pruning in a distributed environment, as the more classical form of sideways information passing would not permit for executing distributed joins in an asynchronous way. Our experi-ments over the LUBM, BTC andWSDTS benchmarks demonstrate that TriAD consistently outperforms centralized RDF engines by up to two orders of magnitude, while gaining a factor of more than three compared to the currently fastest, distributed engines. To our knowledge, we are thus able to report the so far fastest query re-sponse times for the above benchmarks using a mid-range server and regular Ethernet setup.
Diversified Stress Testing of RDF Data Management Systems
"... Abstract. The Resource Description Framework (RDF) is a standard for conceptually describing data on the Web, and SPARQL is the query language for RDF. As RDF data continue to be published across heterogeneous domains and integrated at Web-scale such as in the Linked Open Data (LOD) cloud, RDF data ..."
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Cited by 11 (1 self)
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Abstract. The Resource Description Framework (RDF) is a standard for conceptually describing data on the Web, and SPARQL is the query language for RDF. As RDF data continue to be published across heterogeneous domains and integrated at Web-scale such as in the Linked Open Data (LOD) cloud, RDF data management systems are being exposed to queries that are far more diverse and workloads that are far more varied. The first contribution of our work is an indepth experimental analysis that shows existing SPARQL benchmarks are not suitable for testing systems for diverse queries and varied workloads. To address these shortcomings, our second contribution is the Waterloo SPARQL Diversity Test Suite (WatDiv) that provides stress testing tools for RDF data management systems. Using WatDiv, we have been able to reveal issues with existing systems that went unnoticed in evaluations using earlier benchmarks. Specifically, our experiments with five popular RDF data management systems show that they cannot deliver good performance uniformly across workloads. For some queries, there can be as much as five orders of magnitude difference between the query execution time of the fastest and the slowest system while the fastest system on one query may unexpectedly time out on another query. By performing a detailed analysis, we pinpoint these problems to specific types of queries and workloads.
H2RDF+: High-performance Distributed Joins over Large-scale RDF Graphs
"... Abstract—The proliferation of data in RDF format calls for efficient and scalable solutions for their management. While scalability in the era of big data is a hard requirement, modern systems fail to adapt based on the complexity of the query. Current approaches do not scale well when faced with su ..."
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Cited by 9 (5 self)
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Abstract—The proliferation of data in RDF format calls for efficient and scalable solutions for their management. While scalability in the era of big data is a hard requirement, modern systems fail to adapt based on the complexity of the query. Current approaches do not scale well when faced with substantially complex, non-selective joins, resulting in exponential growth of execution times. In this work we present H2RDF+, an RDF store that efficiently performs distributed Merge and Sort-Merge joins over a multiple index scheme. H2RDF+ is highly scalable, utilizing distributed MapReduce processing and HBase indexes. Utilizing aggressive byte-level compression and result grouping over fast scans, it can process both complex and selective join queries in a highly efficient manner. Furthermore, it adaptively chooses for either single- or multi-machine execution based on join complexity estimated through index statistics. Our extensive evaluation demonstrates that H2RDF+ efficiently answers nonselective joins an order of magnitude faster than both current state-of-the-art distributed and centralized stores, while being only tenths of a second slower in simple queries, scaling linearly to the amount of available resources.
YASGUI: Not Just Another SPARQL Client. In
- The Semantic Web: ESWC 2013 Satellite Events,
, 2013
"... Abstract. This paper introduces YASGUI, a user-friendly SPARQL client. We compare YASGUI with other SPARQL clients, and show the added value and ease of integrating Web APIs, services, and new technologies such as HTML5. Finally, we discuss some of the challenges we encountered in using these techn ..."
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Cited by 8 (4 self)
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Abstract. This paper introduces YASGUI, a user-friendly SPARQL client. We compare YASGUI with other SPARQL clients, and show the added value and ease of integrating Web APIs, services, and new technologies such as HTML5. Finally, we discuss some of the challenges we encountered in using these technologies for a building robust and feature rich web application.
Can we ever catch up with the Web?
, 2010
"... The Semantic Web is about to grow up. By efforts such as the Linking Open Data initiative, we finally find ourselves at the edge of a Web of Data becoming reality. Standards such as OWL 2, RIF and SPARQL 1.1 shall allow us to reason with and ask complex structured queries on this data, but still t ..."
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Cited by 8 (2 self)
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The Semantic Web is about to grow up. By efforts such as the Linking Open Data initiative, we finally find ourselves at the edge of a Web of Data becoming reality. Standards such as OWL 2, RIF and SPARQL 1.1 shall allow us to reason with and ask complex structured queries on this data, but still they do not play together smoothly and robustly enough to cope with huge amounts of noisy Web data. In this paper, we discuss open challenges relating to querying and reasoning with Web data and raise the question: can the burgeoning Web of Data ever catch up with the now ubiquitous HTML Web?
Put in your postcode, out comes the data: A case study
- In 7th Extended Semantic Web Conference (ESWC
, 2010
"... Abstract. A single datum or a set of a categorical data has little value on its own. Combinations of disparate sets of data increase the value of those data sets and helps to discover interesting patterns or relationships, facilitating the construction of new applications and services. In this paper ..."
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Cited by 8 (2 self)
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Abstract. A single datum or a set of a categorical data has little value on its own. Combinations of disparate sets of data increase the value of those data sets and helps to discover interesting patterns or relationships, facilitating the construction of new applications and services. In this paper, we describe an implementation of using open geographical data as a core set of “join point”(s) to mesh different public datasets. We describe the challenges faced during the implementation, which include, sourcing the datasets, publishing them as linked data, and normalising these linked data in terms of finding the appropriate “join points ” from the individual datasets, as well as developing the client application used for data consumption. We describe the design decisions and our solutions to these challenges. We conclude by drawing some general principles from this work.
