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

The World Wide Web has radically altered the way knowledge is shared by lowering the barrier to publishing and accessing documents. Recent initiatives have extended the principles and architecture of the Web to sharing and accessing data, resulting in an interconnected, global data space – the Web of Data. This chapter reviews the historical and scientific context to the emergence of this Web of Data, before detailing the technologies and principles on which it is based. Applications that have been developed and deployed on the Web of Data are reviewed, as are key resources in the field. The chapter concludes with a discussion of current research challenges in areas such as user interfaces, data fusion, link maintenance, trust, and privacy. 6.1 Scientific Overview

Semantic Annotation and Retrieval: Web of Data.

This paper gives an overview of the Semantic Web Challenge on Mining the Web of HTML-embedded Product Data (MWPD2020) which has been conducted as part of the International Semantic Web Conference (ISWC2020). The challenge consists of two tasks: product matching and product classification. In the first task, participants need to identify offers for the same product originating from different websites. The goal of the second task is to categorize offers from different websites into the GS1 GPC product hierarchy. Six teams from the USA, China, Japan, and Germany participated in the challenge. The winning system in Task 1, PMap, achieved an F1 score of 86.05 using an ensemble of
transformer-based language models. Task 2 was won by team Rhinobird achieving a weighted average F1 score of 88.62 using a BERT-based ensemble which considers the dependencies among different category levels.

/pdf/mwpd2020-semantic-web-challenge-on-mining-the-web-of-html-fypewtab8m.pdf

MWPD2020: Semantic Web Challenge on Mining the Web of HTML-embedded Product Data.

The Web has developed into a global information space consisting not just of linked documents, but also of Linked Data. In 2010, we have seen significant growth in the size of the Web of Data, as well as in the number of communities contributing to its creation. In addition to publishing and interlinking datasets, there is intensive work on developing Linked Data browsers, Linked Data crawlers, Web of Data search engines and other applications that consume Linked Data from the Web. The goal of the 4th Linked Data on the Web workshop (LDOW2011) is to provide a forum for exposing high quality research on Linked Data as well as to showcase innovative Linked Data applications. In addition, by bringing together researchers in this field, we expect the event to further shape the Linked Data research agenda.

/pdf/4th-linked-data-on-the-web-workshop-ldow2011-17l9nun3aa.pdf

4th linked data on the web workshop (LDOW2011)

A subset of the HTML tables on the Web contains relational data. The data in these tables covers a multitude of topics and is thus very useful for complementing or validating cross-domain knowledge bases, such as DBpedia, YAGO, or the Google Knowledge Graph. A large fraction of the data in these knowledge bases is time-dependent, meaning that the correctness of an attribute value depends on a point in time. Fusing data from web tables in order to determine correct values for time-dependent attributes is challenging as most web tables do not contain timestamp information. A possibility to deal with this sparsity is to exploit timestamps which appear in different locations on the web page around the table. But as these timestamps might not apply to the web table value in question, this approach introduces noise. This paper investigates the extent to which the performance of data fusion strategies that rely on voting, PageRank, and Knowledge-Based-Trust can be improved by incorporating noisy and sparse timestamp information. For this, we present a machine-learning-based approach which considers different types of noisy timestamps in the data fusion process, and experiment with propagating timestamp information between web tables in order to overcome sparsity. We evaluate the data fusion strategies using a large public corpus of web tables and a public gold standard of time-dependent attribute values. We find that our methods effectively choose and weigh timestamp information per attribute and reduce sparsity using propagation. By incorporating timestamp information into data fusion strategies that previously did not exploit temporal meta information, we are able to increase F1-measure on average by 5%.

Christian Bizer

Papers

Semantic Annotation and Retrieval: Web of Data.

MWPD2020: Semantic Web Challenge on Mining the Web of HTML-embedded Product Data.

4th linked data on the web workshop (LDOW2011)

Fusing time-dependent web table data

Web of Linked Data - A global public data space on the Web.