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REVIEWING… WEB AND LINKED DATA
World Wide Web is full of data!
“These data are formated for human consumption!”And the machines?
Linked Data mug
Linked Data Lifecycle
INTERLINKING
Interlinking - Definition
Interlinking refers to the degree to which entities that
represent the same concept are linked to each other.
Introduction to linked data and its lifecycle on the web (Auer, Sören Lehmann, Jens Ngomo, CAN Zaveri, Amrapali)
“connecting things that are somehow related”
Interlinking - DefinitionMetrics. Interlinking can be measured by
- Using network measures that calculate the interlinking degree
- Cluster coefficient
- SameAs chains
- Centrality and description richness through sameAs links.Airline dataset Spatial DatasetURI: americaairlines.com/country/America URI: dbpedia.org/page/United_StatesSameAs
Why Interlinking?“Include links to other URIs, so that they can discover more
things” 4th principle of LD (most important)
The goal of linking is to transform the Web into a platform for
data and information integration as well as for search and
querying.
Triples in Linked Data sources > 31 billions
-> Links consititute less than 5% of these triples
LINK DISCOVERY
Two categories of frameworks:
Linking on the Web of Data is a more generic and thus more complex
task, as it is not limited to finding equivalent entities in two knowledge
bases
Frameworks have been developed to address the lack of links
between the different knowledge bases on the web.
1) Ontology matching:establish links between ontologies underlying two data sources.
2) Instance matching (link discovery):discover links between instances contained in two data sources.
LINK DISCOVERYFormally…
Given Two sets S (source); T (target) of instances,a (complex) semantic similarity measure σ : S × T → [0, 1] and a threshold θ [0, 1]∈
The goal of link discovery task is to compute the set M = {(s, t), σ(s, t) ≥ θ}.
In general, the similarity function used to carry out a link discovery task
is described by using a link specification (sometimes called linkage
decision rule).
CHALLENGESTwo key challenges arise when trying to discover links between two sets of instances:
1) computational complexity of the matching task
2) selection of an appropriate link specification.
CHALLENGES1) Computational complexity of the matching task• The time complexity of a matching task can be measured
by the number of comparisons necessary to complete this task
• Reduction of the time complexity of link discovery is a key requirement to instance linking frameworks for Linked Data.
Ex.: discovering duplicate cities in Dbpedia would necessitate approximately 0.15 × 109 similarity computations.
CHALLENGES2) Selection of an appropriate link specification.
• The configuration of link discovery frameworks is usually carried out manually, in most cases simply by guessing
• Methods such as supervised and active learning can be used to guide the user in need of mapping to a suitable linking configuration for his matching task
APPROACHES TO LINK DISCOVERYCurrent frameworks for link discovery can be subdivided into two main categories:
Domain-specific
Universal
• RKBExplorer (academic purposes)• GNAT (music)
• RDF-AI (not time optimized)• LIMES (time optimized)• SILK
ACTIVE LEARNING OF LINK SPECIFICATIONSThe second challenge of Link Discovery is the time-efficient discovery of link specifications for a particular linking tasks.
Several approaches have been proposed to achieve this goal, of which most rely on genetic programming
COALA (Correlation-Aware Active Learning) approach was implemented on top of the genetic programming
CONCLUSIONS• First works on running link discovery in parallel have
shown that using massively parallel hardware such as GPUs can lead to better results that using cloud implementations even on considerably large datasets.
• Detecting the right resources for linking automatically given a hardware landscape is yet still a dream to achieve.
CURRENT CHALLENGES
• Authoring
• Extraction from structured fonts (RDBS, XML)
• Natural Language Queries
• Automatic Management of Resources for Linking
• Linked Data Visualization
• Linked Data Quality/Reliability
Main References[Book] Linked Data – Structured data on the web. David Wood, Marsha Zaidman, Luke Ruth. 2014
[Paper] Linked Data – The story so far. Berners Lee.
