Being based on Web technologies, Linked Data is distributed and decentralised in its nature. Hence, for the purpose of finding relevant Linked Data on the Web, search indices play an important role. Also for avoiding network communication overhead and latency, applications rely on indices or caches over Linked Data. These indices and caches are based on local copies of the original data and, thereby, introduce redundancy. Furthermore, as changes at the original Linked Data sources are not automatically propagated to the local copies, there is a risk of having inaccurate indices and caches due to outdated information. In this paper I discuss and compare methods for measuring the accuracy of indices. I will present different measures which have been used in related work and evaluate their advantages and disadvantages from a theoretic point of view as well as from a practical point of view by analysing their behaviour on real world data in an empirical experiment.
More specific to LOD are indices for optimising federated queries @cite_16 , on-demand queries on the Web @cite_2 or looking up data sources relevant to particular schema patterns @cite_3 . However, most of these approaches do not deal with index accuracy either. Their focus is more on how to implement or make use of the index in specific scenarios.
@cite_16 @cite_3 @cite_2
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We present SchemEX, an approach and tool for a stream-based indexing and schema extraction of Linked Open Data (LOD) at web-scale. The schema index provided by SchemEX can be used to locate distributed data sources in the LOD cloud. It serves typical LOD information needs such as finding sources that contain instances of one specific data type, of a given set of data types (so-called type clusters), or of instances in type clusters that are connected by one or more common properties (so-called equivalence classes). The entire process of extracting the schema from triples and constructing an index is designed to have linear runtime complexity. Thus, the schema index can be computed on-the-fly while the triples are crawled and provided as a stream by a linked data spider. To demonstrate the web-scalability of our approach, we have computed a SchemEX index over the Billion Triples Challenge (BTC) dataset 2011 consisting of 2,170 million triples. In addition, we have computed the SchemEX index on a dataset with 11 million triples. We use this smaller dataset for conducting a detailed qualitative analysis. We are capable of locating relevant data sources with recall between 71 and 98 and a precision between 74 and 100 at a window size of 100 K triples observed in the stream and depending on the complexity of the query, i.e. if one wants to find specific data types, type clusters or equivalence classes. Retrieving semi-structured entities to answer keyword queries is an increasingly important feature of many modern Web applications. The fast-growing Linked Open Data (LOD) movement makes it possible to crawl and index very large amounts of structured data describing hundreds of millions of entities. However, entity retrieval approaches have yet to find efficient and effective ways of ranking and navigating through those large data sets. In this paper, we address the problem of Ad-hoc Object Retrieval over large-scale LOD data by proposing a hybrid approach that combines IR and structured search techniques. Specifically, we propose an architecture that exploits an inverted index to answer keyword queries as well as a semi-structured database to improve the search effectiveness by automatically generating queries over the LOD graph. Experimental results show that our ranking algorithms exploiting both IR and graph indices outperform state-of-the-art entity retrieval techniques by up to 25 over the BM25 baseline. An increasing amount of data is becoming available in the form of large triple stores, with the Semantic Web's linked open data cloud (LOD) as one of the most prominent examples. Data quality and completeness are key issues in many community-generated data stores, like LOD, which motivates probabilistic and statistical approaches to data representation, reasoning and querying. In this paper we address the issue from the perspective of probabilistic databases, which account for uncertainty in the data via a probability distribution over all database instances. We obtain a highly compressed representation using the recently developed RESCAL approach and demonstrate experimentally that efficient querying can be obtained by exploiting inherent features of RESCAL via sub-query approximations of deterministic views. Based on URIs, HTTP and RDF, the Linked Data project [3] aims to expose, share and connect related data from diverse sources on the SemanticWeb. Linked Open Data (LOD) is a community effort to apply the Linked Data principles to data published under open licenses. With this effort, a large number of LOD datasets have been gathered in the LOD cloud, such as DBpedia, Freebase and FOAF profiles. These datasets are connected by links such as owl:sameAs. LOD has gained rapidly progressed and is still growing constantly. Until May 2009, there are 4.7 billion RDF triples and around 142 million RDF links [3]. After that, the total has been increased to 16 billion triples in March 2010 and another 14 billion triples have been published by the AIFB according to [17].