Data Mining - Concepts and Techniques.

The term “Linked Data” refers to a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the last three years, leading to the creation of a global data space containing billions of assertions— the Web of Data. In this article, the authors present the concept and technical principles of Linked Data, and situate these within the broader context of related technological developments. They describe progress to date in publishing Linked Data on the Web, review applications that have been developed to exploit the Web of Data, and map out a research agenda for the Linked Data community as it moves forward.

/pdf/linked-data-the-story-so-far-2a9zvmaysa.pdf

Linked Data - the story so far

DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against datasets derived from Wikipedia and to link other datasets on the Web to Wikipedia data. We describe the extraction of the DBpedia datasets, and how the resulting information is published on the Web for human-andmachine-consumption. We describe some emerging applications from the DBpedia community and show how website authors can facilitate DBpedia content within their sites. Finally, we present the current status of interlinking DBpedia with other open datasets on the Web and outline how DBpedia could serve as a nucleus for an emerging Web of open data.

/pdf/dbpedia-a-nucleus-for-a-web-of-open-data-64wla3p0zr.pdf

DBpedia: a nucleus for a web of open data

This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.

/pdf/character-level-convolutional-networks-for-text-whh3tafudk.pdf

Character-level convolutional networks for text classification

The DBpedia community project extracts structured, multilingual knowledge from Wikipedia and makes it freely available on the Web using Semantic Web and Linked Data technologies. The project extracts knowledge from 111 different language editions of Wikipedia. The largest DBpedia knowledge base which is extracted from the English edition of Wikipedia consists of over 400 million facts that describe 3.7 million things. The DBpedia knowledge bases that are extracted from the other 110 Wikipedia editions together consist of 1.46 billion facts and describe 10 million additional things. The DBpedia project maps Wikipedia infoboxes from 27 different language editions to a single shared ontology consisting of 320 classes and 1,650 properties. The mappings are created via a world-wide crowd-sourcing effort and enable knowledge from the different Wikipedia editions to be combined. The project publishes releases of all DBpedia knowledge bases for download and provides SPARQL query access to 14 out of the 111 language editions via a global network of local DBpedia chapters. In addition to the regular releases, the project maintains a live knowledge base which is updated whenever a page in Wikipedia changes. DBpedia sets 27 million RDF links pointing into over 30 external data sources and thus enables data from these sources to be used together with DBpedia data. Several hundred data sets on the Web publish RDF links pointing to DBpedia themselves and make DBpedia one of the central interlinking hubs in the Linked Open Data (LOD) cloud. In this system report, we give an overview of the DBpedia community project, including its architecture, technical implementation, maintenance, internationalisation, usage statistics and applications.

/pdf/dbpedia-a-large-scale-multilingual-knowledge-base-extracted-2yfl0whyxn.pdf

DBpedia - A Large-scale, Multilingual Knowledge Base Extracted from Wikipedia

This paper presents a brief summary of the eight workshop on Linked Data on the Web. The LDOW 2013 workshop is held in conjunction with the World Wide Web conference 2013. The focus is on data publishing, integration and consumption using RDF and other semantic representation formalisms and technologies.

LDOW 2013: The 8th Workshop on Linked Data on the Web

The Geospatial Semantic Web makes locations first-class citizens of the Web by representing them as original Web resources. This allows locations to be described in an open and distributed manner using the Resource Description Framework and provides for interlinking data about locations between data sources. In addition to using geo-coordinates to express geographical proximity, the Geospatial Semantic Web provides for relating locations as well as regions to each other using explicit semantic relationship types such as containment or shared borders. This article gives an overview of the Geospatial Semantic Web and describes DBpedia Mobile, a location-aware Semantic Web client that can be used on an iPhone and other mobile devices. Based on the current GPS position, DBpedia Mobile renders a map indicating nearby locations from the DBpedia data set. Starting from this map, the user can explore background information about his surroundings by navigating along data links into other data sources. DBpedia Mobile has been designed for the use case of a tourist exploring a city. Besides accessing Web data, DBpedia Mobile also enables users to publish their current location, pictures and reviews to the Semantic Web so that they can be used by other Semantic Web applications. Instead of simply being tagged with geographical coordinates, published content is interlinked with a nearby DBpedia resource and thus contributes to the overall richness of the Geospatial Semantic Web.

Exploring the Geospatial Semantic Web with DBpedia Mobile

The goal of entity resolution is to identify records in multiple datasets that represent the same real-world entity. However, comparing all records across datasets can be computationally intensive, leading to long runtimes. To reduce these runtimes, entity resolution pipelines are constructed of two parts: a blocker that applies a computationally cheap method to select candidate record pairs, and a matcher that afterwards identifies matching pairs from this set using more expensive methods. This paper presents SC-Block, a blocking method that utilizes supervised contrastive learning for positioning records in the embedding space, and nearest neighbour search for candidate set building. We benchmark SC-Block against eight state-of-the-art blocking methods. In order to relate the training time of SC-Block to the reduction of the overall runtime of the entity resolution pipeline, we combine SC-Block with four matching methods into complete pipelines. For measuring the overall runtime, we determine candidate sets with 99.5% pair completeness and pass them to the matcher. The results show that SC-Block is able to create smaller candidate sets and pipelines with SC-Block execute 1.5 to 2 times faster compared to pipelines with other blockers, without sacrificing F1 score. Blockers are often evaluated using relatively small datasets which might lead to runtime effects resulting from a large vocabulary size being overlooked. In order to measure runtimes in a more challenging setting, we introduce a new benchmark dataset that requires large numbers of product offers to be blocked. On this large-scale benchmark dataset, pipelines utilizing SC-Block and the best-performing matcher execute 8 times faster than pipelines utilizing another blocker with the same matcher reducing the runtime from 2.5 hours to 18 minutes, clearly compensating for the 5 minutes required for training SC-Block.

SC-Block: Supervised Contrastive Blocking within Entity Resolution Pipelines

Microformat, Microdata and RDFa data from the November 2015 Common Crawl web corpus. We found structured data within 541 million HTML pages out of the 1.77 billion pages contained in the crawl (30%).These pages originate from 2.72 million different pay-level-domains out of the 14.41 million pay-level-domains covered by the crawl (19%). Altogether, the extracted data sets consist of 24.38 billion RDF quads.

Web Data Commons - RDFa, Microdata, and Microformat November 2015 Data Set

Disclosed is an inflatable raft having an opening formed on one of its four side portions. A downward and then radially inward extended frame is formed around the opening for a top flange of a container to be radio heated thereto. The container has an accordion body. When the raft is inflated, the accordion container can be downward stretched to accommodate a can therein. And, when the raft is deflated, the accordion container can be collapsed along with the raft for convenient storage. The structure of the inflatable raft will not be adversely affected by the provision of the accordion container on the raft.

Christian Bizer

Papers

LDOW 2013: The 8th Workshop on Linked Data on the Web

Exploring the Geospatial Semantic Web with DBpedia Mobile

SC-Block: Supervised Contrastive Blocking within Entity Resolution Pipelines

Web Data Commons - RDFa, Microdata, and Microformat November 2015 Data Set

Resource Description Framework.