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149
Data Summaries for On-Demand Queries over Linked Data
- In: Proceedings of the 19th International World Wide Web Conference
, 2010
"... Typical approaches for querying structured Web Data collect (crawl) and pre-process (index) large amounts of data in a central data repository before allowing for query answering. However, this time-consuming pre-processing phase however leverages the benefits of Linked Data – where structured data ..."
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Cited by 84 (15 self)
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Typical approaches for querying structured Web Data collect (crawl) and pre-process (index) large amounts of data in a central data repository before allowing for query answering. However, this time-consuming pre-processing phase however leverages the benefits of Linked Data – where structured data is accessible live and up-to-date at distributed Web resources that may change constantly – only to a limited degree, as query results can never be current. An ideal query answering system for Linked Data should return current answers in a reasonable amount of time, even on corpora as large as the Web. Query processors evaluating queries directly on the live sources require knowledge of the contents of data sources. In this paper, we develop and evaluate an approximate index structure summarising graph-structured content of sources adhering to Linked Data principles, provide an algorithm for answering conjunctive queries over Linked Data on the Web exploiting the source summary, and evaluate the system using synthetically generated queries. The experimental results show that our lightweight index structure enables complete and up-to-date query results over Linked Data, while keeping the overhead for querying low and providing a satisfying source ranking at no additional cost.
SPLENDID: SPARQL Endpoint Federation Exploiting VOID Descriptions
- In Proceedings of the 2nd International Workshop on Consuming Linked Data
, 2011
"... Abstract. In order to leverage the full potential of the Semantic Web it is necessary to transparently query distributed RDF data sources in the same way as it has been possible with federated databases for ages. However, there are significant differences between the Web of (linked) Data and the tra ..."
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Cited by 49 (1 self)
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Abstract. In order to leverage the full potential of the Semantic Web it is necessary to transparently query distributed RDF data sources in the same way as it has been possible with federated databases for ages. However, there are significant differences between the Web of (linked) Data and the traditional database approaches. Hence, it is not straightforward to adapt successful database techniques for RDF federation. Reasons are the missing cooperation between SPARQL endpoints and the need for detailed data statistics for estimating the costs of query execution plans. We have implemented SPLENDID, a query optimization strategy for federating SPARQL endpoints based on statistical data obtained from voiD descriptions [1]. 1
Ad-hoc object retrieval in the web of data.
- In Proceedings of the 19th international conference on World wide web,
, 2010
"... ABSTRACT Semantic Search refers to a loose set of concepts, challenges and techniques having to do with harnessing the information of the growing Web of Data (WoD) for Web search. Here we propose a formal model of one specific semantic search task: ad-hoc object retrieval. We show that this task pr ..."
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Cited by 42 (8 self)
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ABSTRACT Semantic Search refers to a loose set of concepts, challenges and techniques having to do with harnessing the information of the growing Web of Data (WoD) for Web search. Here we propose a formal model of one specific semantic search task: ad-hoc object retrieval. We show that this task provides a solid framework to study some of the semantic search problems currently tackled by commercial Web search engines. We connect this task to the traditional ad-hoc document retrieval and discuss appropriate evaluation metrics. Finally, we carry out a realistic evaluation of this task in the context of a Web search application.
Linked data query processing strategies
- In ISWC
"... Abstract. Recently, processing of queries on linked data has gained at-tention. We identify and systematically discuss three main strategies: a bottom-up strategy that discovers new sources during query process-ing by following links between sources, a top-down strategy that relies on complete knowl ..."
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Cited by 37 (1 self)
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Abstract. Recently, processing of queries on linked data has gained at-tention. We identify and systematically discuss three main strategies: a bottom-up strategy that discovers new sources during query process-ing by following links between sources, a top-down strategy that relies on complete knowledge about the sources to select and process relevant sources, and a mixed strategy that assumes some incomplete knowledge and discovers new sources at run-time. To exploit knowledge discovered at run-time, we propose an additional step, explicitly scheduled during query processing, called correct source ranking. Additionally, we propose the adoption of stream-based query processing to deal with the unpre-dictable nature of data access in the distributed Linked Data environ-ment. In experiments, we show that our implementation of the mixed strategy leads to early reporting of results and thus, more responsive query processing, while not requiring complete knowledge. 1
A Distributed Graph Engine for Web Scale RDF Data
"... Much work has been devoted to supporting RDF data. But state-of-the-art systems and methods still cannot handle web scale RDF data effectively. Furthermore, many useful and general purpose graph-based operations (e.g., random walk, reachability, community discovery) on RDF data are not supported, as ..."
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Cited by 35 (1 self)
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Much work has been devoted to supporting RDF data. But state-of-the-art systems and methods still cannot handle web scale RDF data effectively. Furthermore, many useful and general purpose graph-based operations (e.g., random walk, reachability, community discovery) on RDF data are not supported, as most existing systems store and index data in particular ways (e.g., as relational tables or as a bitmap matrix) to maximize one particular operation on RDF data: SPARQL query processing. In this paper, we introduce Trinity.RDF, a distributed, memory-based graph engine for web scale RDF data. Instead of managing the RDF data in triple stores or as bitmap matrices, we store RDF data in its native graph form. It achieves much better (sometimes orders of magnitude better) performance for SPARQL queries than the state-of-the-art approaches. Furthermore, since the data is stored in its native graph form, the system can support other operations (e.g., random walks, reachability) on RDF graphs as well. We conduct comprehensive experimental studies on real life, web scale RDF data to demonstrate the effectiveness of our approach. 1
Language-model-based ranking for queries on RDF-graphs
, 2009
"... The success of knowledge-sharing communities like Wikipedia and the advances in automatic information extraction from textual and Web sources have made it possible to build large “knowledge repositories” such as DBpedia, Freebase, and YAGO. These collections can be viewed as graphs of entities and r ..."
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Cited by 31 (11 self)
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The success of knowledge-sharing communities like Wikipedia and the advances in automatic information extraction from textual and Web sources have made it possible to build large “knowledge repositories” such as DBpedia, Freebase, and YAGO. These collections can be viewed as graphs of entities and relationships (ER graphs) and can be represented as a set of subject-property-object (SPO) triples in the Semantic-Web data model RDF. Queries can be expressed in the W3C-endorsed SPARQL language or by similarly designed graph-pattern search. However, exact-match query semantics often fall short of satisfying the users ’ needs by returning too many or too few results. Therefore, IR-style ranking models are crucially needed. In this paper, we propose a language-model-based approach to ranking the results of exact, relaxed and keyword-augmented graphpattern queries over RDF graphs such as ER graphs. Our method estimates a query model and a set of result-graph models and ranks results based on their Kullback-Leibler divergence with respect to the query model. We demonstrate the effectiveness of our ranking model by a comprehensive user study.
FedBench: A Benchmark Suite for Federated Semantic Data Query Processing
- In ISWC
, 2011
"... Abstract. In this paper we present FedBench, a comprehensive bench-mark suite for testing and analyzing the performance of federated query processing strategies on semantic data. The major challenge lies in the heterogeneity of semantic data use cases, where applications may face different settings ..."
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Cited by 27 (4 self)
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Abstract. In this paper we present FedBench, a comprehensive bench-mark suite for testing and analyzing the performance of federated query processing strategies on semantic data. The major challenge lies in the heterogeneity of semantic data use cases, where applications may face different settings at both the data and query level, such as varying data access interfaces, incomplete knowledge about data sources, availability of different statistics, and varying degrees of query expressiveness. Ac-counting for this heterogeneity, we present a highly flexible benchmark suite, which can be customized to accommodate a variety of use cases and compare competing approaches. We discuss design decisions, high-light the flexibility in customization, and elaborate on the choice of data and query sets. The practicability of our benchmark is demonstrated by a rigorous evaluation of various application scenarios, where we indicate both the benefits as well as limitations of the state-of-the-art federated query processing strategies for semantic data. 1
x-RDF-3X: Fast Querying, High Update Rates, and Consistency for RDF Databases ABSTRACT
"... The RDF data model is gaining importance for applications in computational biology, knowledge sharing, and social communities. Recent work on RDF engines has focused on scalable performance for querying, and has largely disregarded updates. In addition to incremental bulk loading, applications also ..."
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Cited by 27 (2 self)
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The RDF data model is gaining importance for applications in computational biology, knowledge sharing, and social communities. Recent work on RDF engines has focused on scalable performance for querying, and has largely disregarded updates. In addition to incremental bulk loading, applications also require online updates with flexible control over multi-user isolation levels and data consistency. The challenge lies in meeting these requirements while retaining the capability for fast querying. This paper presents a comprehensive solution that is based on an extended deferred-indexing method with integrated versioning. The version store enables time-travel queries that are efficiently processed without adversely affecting queries on the current data. For flexible consistency, transactional concurrency control is provided with options for either snapshot isolation or full serializability. All methods are integrated in an extension of the RDF-3X system, and their very good performance for both queries and updates is demonstrated by measurements of multi-user workloads with real-life data as well as stress-test synthetic loads.
From Information to Knowledge: Harvesting Entities and Relationships from Web Sources
"... There are major trends to advance the functionality of search engines to a more expressive semantic level. This is enabled by the advent of knowledge-sharing communities such as Wikipedia and the progress in automatically extracting entities and relationships from semistructured as well as natural-l ..."
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Cited by 26 (7 self)
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There are major trends to advance the functionality of search engines to a more expressive semantic level. This is enabled by the advent of knowledge-sharing communities such as Wikipedia and the progress in automatically extracting entities and relationships from semistructured as well as natural-language Web sources. Recent endeavors of this kind include DBpedia, EntityCube, KnowItAll, ReadTheWeb, and our own YAGO-NAGA project (and others). The goal is to automatically construct and maintain a comprehensive knowledge base of facts about named entities, their semantic classes, and their mutual relations as well as temporal contexts, with high precision and high recall. This tutorial discusses state-ofthe-art methods, research opportunities, and open challenges along this avenue of knowledge harvesting.
gStore: Answering SPARQL Queries via Subgraph Matching
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, 2011
"... Due to the increasing use of RDF data, efficient processing of SPA-RQL 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 update ..."
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Cited by 25 (3 self)
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Due to the increasing use of RDF data, efficient processing of SPA-RQL 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.
