When to Reach for the Cloud: Using Parallel Hardware for Link Discovery

Abstract

Abstract. With the ever-growing amount of RDF data available across the Web, the discovery of links between datasets and deduplication of resources within knowledge bases have become tasks of crucial importance. Over the last years, several link discovery approaches have been developed to tackle the runtime and complexity problems that are intrinsic to link discovery. Yet, so far, little attention has been paid to the management of hardware resources for the execution of link discovery tasks. This paper addresses this research gap by investigating the efficient use of hardware resources for link discovery. We implement the HR 3 approach for three different parallel processing paradigms including the use of GPUs and MapReduce platforms. We also perform a thorough performance comparison for these implementations. Our results show that certain tasks that appear to require cloud computing techniques can actually be accomplished using standard parallel hardware. Moreover, our evaluation provides break-even points that can serve as guidelines for deciding on when to use which hardware for link discovery.

A survey of the first 20 years of research on semantic Web and linked data

“…For instance, (Tangmunarunkit et al, 2003) solve resource matching in the Grid using Semantic Web technologies. On the other hand, (Ngomo et al, 2013) assesses cloud computing and different parallel processing paradigms hardware, including the use of GPUs and MapReduce platforms, for link discovery.…”

A survey of current Link Discovery frameworks

“…Another general optimization approach is parallel LD on multiple cores or multiple nodes in a cluster. This includes the utilization of modern hardware and infrastructures such as graphical processing units (GPU) or Hadoop-based clusters [33].…”

“…Blocking and filtering can jointly be applied, e.g., to reduce the number of comparisons for partition-wise linking. Furthermore, both approaches can be utilized in combination with parallel LD [24,33].…”

“…A high effectiveness requires finding (almost) all links between two given sources without deriving incorrect links. Achieving this goal requires finding a suitable link configuration or specification [20,33] specifying the similarity condition(s) two resources s ∈ S and t ∈ T have to comply with in order to count as a being in the input relation. Even when given a suitable link specification, we have to address the efficiency problem since a naïve implementation which compares all elements of S with all elements of T would have a complexity of O(|S| • |T |).…”

User interfaces for semantic authoring of textual content: A systematic literature review

“…recent triple store benchmarking efforts such as the DBpedia benchmark [46]). Similarly tools and algorithms for linking data and schemata are progressing and approaches are deployed for the use on the emerging Web of Data [48]. The quantity of semantic content being made available on the Data Web is rapidly increasing, mainly due to the use of automated knowledge extraction techniques or due to the semantic enrichment and transformation of existing structured data (cf.…”