Linked Data & DBpediaM. Freudenberg, K. Müller & M. Ackermann

AKSW/KILT - LeipzigDBpedia Association

Linked Data & DBpedia

AKSW/KILT- Knowledge Integration and Language Technology- Part of Agile Knowledge Engineering and Semantic Web (AKSW)

Knowledge Integration

Language TechnologyKILT

Linked Data & DBpedia

Tim Berners-Lee

- British computer scientist- director of the W3C- Inventor of the World Wide Web

(1989 @ CERN)- Over his frustration of disconnected islands of

information (about scientists, projects and results)- Published the first Website:

http://info.cern.ch/hypertext/WWW/TheProject.html

Linked Data & DBpedia

From the WWW to the Web of Data

- applying the principles of the WWW to data

data is relationships,not only properties

Linked Data & DBpedia

TimBL’s next leap: from WWW to WOD

Use Linked Data to build a Web Of Data

- applying the principles of the WWW to data- Data is relationships → not only properties- The more data you have to connect together

→ the more you can find out

- using Linked Data to:- Bridging disciplines and domains (by linking their data) - Unlock the potential of island repositories

→ don’t hoard your data, if possible: share itWatch the TED talk of TimBL about Linked Data

Linked Data & DBpedia

Linked Data Principles

1. Use HTTP URIs as identifiers for resources→ so people can look up the names

2. Provide data at the location of URIs→ to provide data for interested parties

3. Include links to other resources→ so people can discover more things

→ bridging disciplines and domains

→ the more linked resources, the more one can find out

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RDF - Resource Description Framework

… is a so called ‘Triple’.

http://dbpedia.org/resource/Siem

ens

- Statements of subject > predicate > object

"Siemens"label

Predicate ObjectSubject

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Knowledge Graphs

● Combining multiple Triples is known as a Graph

● Linking resource to resource inside or outside the current graph/dataset

● A knowledge-base of this style is considered a Knowledge Graph (KG)

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The Data (in RDF/XML)

<rdf:Description rdf:about="http://dbpedia.org/resource/Siemens"> <rdfs:label>Siemens</rdfs:label> <dbo:type rdf:resource="http://dbpedia.org/resource/Aktiengesellschaft"/> <dbo:location rdf:resource="http://dbpedia.org/resource/Munich"/></rdf:Description>

<rdf:Description rdf:about="http://dbpedia.org/resource/Munich"> <dbo:country rdf:resource="http://dbpedia.org/resource/Germany"/></rdf:Description>

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The Data (in Turtle)

<http://dbpedia.org/resource/Siemens> dbo:type <http://dbpedia.org/resource/Aktiengesellschaft> ; rdfs:label "Siemens"@de ; dbo:location <http://dbpedia.org/resource/Munich> .

<http://dbpedia.org/resource/Munich> dbo:country <http://dbpedia.org/resource/Germany/> .

Linked Data & DBpedia

Linked Data vs Open Data

4. Final principle: Open your data using open licenses

• Not all linked data is open

• Licensed data can still profit from using standards

• Can be enriched with links to Linked Data

• Can be accessed by standard tools

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5 ★ Linked Open Data

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Why publish Linked Data

• Ease of discovery through linking

• Easy to consume by humans and machines

• Reduce data redundancy

• Support collaboration & interoperability

• Add value and visibility

Linked Data & DBpedia

Benefits of Linked Data: Consumer View

- discover more related data by following links- reuse the data of other datasets- combine data safely from different sources- formulate sophisticated queries → example in appendix- query data over multiple repositories- semantic enrichment of text resources- semantic feature for machine learning models (e.g. deep

learning, word embeddings, etc.)

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Benefits of Linked Data: Publisher View

- link data to any other resource on the web, thereby increasing the value of your data

- making your data discoverable (via links)- exhaustive descriptions of large and changing domains (Gene

Ontology, Human Disease Ontology)- structured representation of large, versatile datasets

(Knowledge Graphs, Thesauri, Taxonomies)- deal with unstructured data (text) as no DB-Schema could- data and schemata using the same format (RDF)- store metadata alongside the actual data (e.g. DCAT)

Linked Data & DBpedia

Linked Open Data

LOD-Cloud 2014

Linked Data -Datasets under an open access- 1014 datasets- any subject- over 50B triples- over 100M links

Linked Data & DBpedia

DBpedia

● First public Knowledge Graph● Has become the focal point of the so

called “Linked Open Data Cloud”.● Is the most universal dataset

(since it’s based on Wikipedia).● Links actively to many relevant

Linked Open Datasets.● Is a link destination for many other

Datasets.(more on DBpedia later…)

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Other Linked Data Sets: Freebase

● Managed, hosted by Google until 2015● Now (in part) subsumed by Wikidata● extracted structured data from Wikipedia and other Sources● available in RDF● Differences to DBpedia

○ Freebase used several sources (but DBpedia+ does as well)○ Freebase can be directly edited by users ○ Ontology and mappings were not coordinated by a community

→ never established a community which enriched or validated the data, mostly generated by crawlers

Linked Data & DBpedia

Wikidata

● Initialized by Wikimedia Germany e.V. in 2012● free knowledge base about the world that can be read● edited by humans and machines alike● can offer a variety of statements from different sources● DBpedia is extracting information from Wikidata to fuse it with

knowledge from Wikipedia● Goal is to provide a single point of truth for facts in Wikipedia

across different language versions

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Other Datasets

● Geonames○ geographical database covers all countries○ contains over eleven million placenames○ e.g. http://www.geonames.org/3399415/fortaleza.html

● Linked Open Vocabularies (LOV)○ Keeps track of available open ontologies and provides them as a graph○ Search for available ontologies, open for reuse○ e.g. http://lov.okfn.org/dataset/lov/vocabs/foaf

● Lexvo.org○ information about languages, words, characters, and other human

language-related entities○ e.g. http://www.lexvo.org/page/iso639-3/deu

Linked Data & DBpedia

Excursus: Ontologies

This is a concise introduction to ontologies and their role as schemata in Linked Data.

(No worries, we keep this short ;)

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Levels of Knowledge

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Different Perceptions

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Conceptualization

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Ontologies in Computer Science

● An ontology has a common language (symbols, expressions) → Syntax● The meaning of symbols and expressions is clear → Semantics● Symbols and expressions with similar semantics are grouped in

concepts (classes) → Conceptualization● Concepts are organized in a hierarchical way → Taxonomy● Concepts might be related to others → Relations● Implicit knowledge can be made explicit → Reasoning

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Ontology, a definition

“An ontology is an explicit, formal specification of a shared conceptualization.”

(Thomas R. Gruber, 1993)

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Example

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Axioms

Axioms are knowledge definitions in the ontology that were explicitly defined and have not been proven true.

Implicit knowledge can be made explicit by logical induction: → Reasoning over an ontology

Source for the ontology related slides:http://www.slideshare.net/SergeLinckels/semantic-web-ontologies

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Ontology Language

To express ontologies in a formal, machine readable way, in order to reason over the outlined knowledge, we need a specialized language.→ most common: Web ontology language (OWL)● represent rich and complex knowledge about things● based on a subset of First Order Logic (FOL)● can be used to verify the consistency of a knowledge ● can make implicit knowledge explicit● as the data it conceptualizes, it is serializable in RDF

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How to utilize Linked Data Standards

Any OWL ontology/taxonomy can be used in a non LD context.- through its ability to link resources, RDF based ontologies can

easily amalgamate, thereby making them reusable- extending ontologies to fit a narrower use cases- reducing ontologies of a certain area to fit a broader scope- separating semantic structure (classes, properties) from use case

specific restrictions (e.g. cardinalities) -> SHACL- Example: DataID

The W3C, responsible for common standards on the Web, is focusing on RDF based standards in many fields.

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Incremental adoption of LD technologies

Linked Data standards and technologies are manifold and, at times, confusing.Fortunately, introducing Linked Data into existing IT environments can be accomplished in an incremental fashion:● Collect data without given schemata/ontologies

○ Very helpful when dealing with semi- or unstructured data● Use RDF Views on top of existing DBMS

○ With an easy to change R2RML mapping● Develop an domain ontology over time (‘Open World Assumption’)

○ Especially useful in fast changing domains● Enrich data with every iteration of you data management cycle

○ See ALIGNED methods for more● Start using LD based tooling: e.g. Rel Finder

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Linked Data in the context of Big Data

In 2006, Clive Humby coined the phrase "the new oil" for (digital) data, heralding the ever-expanding realm of what is now summarised as: Big Data.

What role does Linked Data play in the context of buzzwords like:● Big Data● Smart Data (e.g. Smart Data Forum)

Linked Data & DBpedia

The four V’s of Big Data

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The four V’s heatmap for Linked Data

Gartner Study in 2013 found:- many organizations find the “variety” dimension a much bigger challenge than volume or velocity.

Linked Data to the rescue:

- Combine multiple sources with different structures- while retaining the flexibility to add new ones- without adapting schematas- query combined data, or multiple sources at once- detecting patterns in the data

Linked Data & DBpedia

Linked Data in the context of Big Data

- Linked Data can describe any kind of data

- no matter the amount or domain

- especially useful in here:- graph structured data

- social media, knowledge graphs- (multi-) lingual data

- easy incorporation of unstructured data- perfect for annotating purposes

- data for complex domains - e.g. taxonomies in life sciences

- ontologies, metadata and provenance info- ontos are modeled with OWL a RDF extension

Big Data

Linked Data & DBpedia

Linked Data in the context of Big Data

- Linked Data can describeany kind of data

- no matter the amount or domain

- especially useful in here:- graph structured data

- social media, knowledge graphs- (multi-) lingual data

- easy incorporation of unstructured data- perfect for annotating purposes

- data for complex domains - e.g. taxonomies in life sciences

- ontologies, metadata and provenance info- ontos are modeled with OWL a RDF extension

Big Data Smart Data

Linked Data & DBpedia

Linked Data in the context of Big Data

- Linked Data can describeany kind of data

- no matter the amount or domain

- especially useful in here:- graph structured data

- social media, knowledge graphs- (multi-) lingual data

- easy incorporation of unstructured data- perfect for annotating purposes

- data for complex domains - e.g. taxonomies in life sciences

- ontologies, metadata and provenance info- ontologies are modeled with OWL a RDF extension

Big Data Smart Data

Linked Data

Linked Data & DBpedia

Linked Data in Research

● Computer science: especially graph- and NLP-related, QA, AI○ e.g. IBM: Natural Language Understanding of Unstructured Data

● Life sciences: to describe complex domains (large ontologies & taxonomies)

○ e.g. Human Disease Ontology● (Digital) Humanities: to manage (record, annotate…) large text records

○ e.g. Homer Multitext Project● Libraries: recording of metadata and interlinking it with other

institutions○ e.g. Deutsche Nationalbibliothek

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● Online Search (large Knowledge Graphs)○ e.g. Google

● Social Media (social network analysis)○ e.g. Facebook

● Publishing Industry (large text corpora annotation)○ e.g. Wolters Kluwer, NYT

● Broadcasting (ontology-centered Linked Data services)○ e.g. BBC

● (Open) Government Data○ e.g. US publishing data as RDF http://data.gov

Linked Data in Industry

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Who uses RDF (in public)

Linked Data & DBpedia

DBpedia… a fused, multi-domain, multilingual dataset

● Is a crowed sourced community effort to extract structured information from Wikipedia and Wikidata.

● Enriches the extracted information w. semantic layer

● Provides a query service and many additional tools.

Linked Data & DBpedia

A web of knowledge

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DBpedia History

● DBpedia project was started in 2006 as a collaboration of Freie U. Berlin, U. Leipzig and U. Mannheim

● Has been a key factor in the rapid growth of the LOD initiative and the overall success of Linked Data

● http://wiki.dbpedia.orgExample: http://dbpedia.org/page/Leipzig_University

http://en.wikipedia.org/wiki/Monty_Python ⇔ http://dbpedia.org/resource/Monty_Python

Linked Data & DBpedia

Some statistics

The latest release (2015-10):● was extracted from 127 language editions ● describing up to 20 million things● with 8.8 billion RDF statements (triples)● Mirroring every wikipedia page = 6.2M things

○ of which 4.6M have abstracts, ○ 955K have geo coordinates ○ and 1.54M depictions

● “In general we observed a significant growth in raw infobox and mapping-based statements of close to 10%”.

Linked Data & DBpedia

Structure of DBpedia

● Structured in language datasets with multiple subsets● Each sub-dataset specializes on a certain type of data (see below)● mapping-based types and facts governed by the DBpedia Ontology

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DBpedia Ontology

● A cross-domain ontology● maintained and extended by the community in the

DBpedia Mappings Wiki● manually created based on the most commonly used

infoboxes● currently covers 685 classes which form a subsumption

hierarchy and are described by 2,795 different properties● subsumption hierarchy with a maximal depth of 5● is maintained and extended by the community in the

DBpedia Mappings Wiki

DBpedia Ontology Extract

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DBpedia Mappings Wiki

● a community effort to:● develop an ontology schema● provide mappings from Wikipedia Infoboxes properties

to this ontology → creating an alignment between Wikipedia and Dbpedia→ eliminating name variations in properties and classes→ big boost for Precision

http://mappings.dbpedia.org/

Linked Data & DBpedia

Extracting a DBpedia

● Wikipedia articles consist mostly of free text● also comprise various types of structured

information ● depending on the template used for a specific article

(e.g. Actor, Village etc.)● including: infobox templates, categorisation

information, images, geo-coordinates, links to external web pages, disambiguation pages,

● redirects between pages, other language links

Linked Data & DBpedia

Wikipedia Article Structure

● Title● Abstract● Infoboxes● Geo-coordinates● Categories● Images● Links

○ other language versions○ other Wikipedia pages○ To the Web○ Redirects○ Disambiguations

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Infobox Encoding

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DIEF - DBpedia Information Extraction Framework

● extracts structured information from Wikipedia and turns it into a rich knowledge base○ Mapping-Based Infobox Extraction, ○ Raw Infobox Extraction, ○ Feature Extraction, ○ Statistical Extraction

● Updated to adapt to changes in Wikipedia● Expanded for new knowledge extraction methods

○ E.g. by multiple GSOC projects (extraction tables, NIF,...)● Open Source code in Scala & Java

Linked Data & DBpedia

DBpedia Live

● Wikipedia articles are continuously revised at a very high rate

● English Wikipedia, in June 2013, had approximately 3.3 million edits per month (^= 77 edits per minute)

● Dbpedia Live was developed to keep Dbpedia in synchronization with Wikipedia

● works on a continuous stream of updates from Wikipedia and processes that stream on the fly

Linked Data & DBpedia

Accessing and Querying DBpedia

● per resource view○ Linked Data interfaces

http://dbpedia.org/page/Immanuel_Kant

● navigation view○ LodLive Browser

http://en.lodlive.it/?http:

//dbpedia.org/resource/Immanuel_Kant

● querying for resources○ SPARQL (introduced later)○ DBpedia Lookup Service

Linked Data & DBpedia

DBpedia Lookup Service

● REST service to query for DBpedia resources

● index of DBpedia resource, including alternative names/labels(page redirects, disambiguisation links, …)

● search by complete keywords and prefix search● results ranked by relevance (Page Rank)● filtering by DBpedia ontology classes

http://lookup.dbpedia.org/api/search/KeywordSearch?QueryClass=place&QueryString=berlin

http://lookup.dbpedia.org/api/search/KeywordSearch?QueryClass=person&QueryString=berlin

Linked Data & DBpedia

DBpedia internationalised

● non-English versions of DBpedia offers○ coverage of more entities○ more detailed or up-to-date information for entities associated with the

particular countries

● international mapping community ○ helps in provision of localized dbpedia datasets for 125 languages

● 15 DBpedia chapters (by languages)○ autonomous management of mapping, ○ organisation of local community, ○ hosting of datasets and services

● canonicalized datasets○ facts derived from localized Wikipedias, but only statements for resources

also present in Englisch DBpedia

Linked Data & DBpedia

DBpedia Association

- founded in 2014, based in Leipzig- goal: supporting the DBpedia community and provide free

data and services to the general public- Data Releases- Software Maintenance- Dissemination- Data Accessibility- Communication (internal and external)

- persons and organisations can become member:- gaining support for all DBpedia specific problems (queries, tools etc.)- deciding on the future of DBpedia- acquiring help for creating and linking their own datasets

Linked Data & DBpedia

A need for information

„Which films starred John Cleese without any other members of Monty Python?“

Linked Data & DBpedia

SPARQL Protocol and RDF Query Language

● RDF data query language○ also query and data transfer protocol specifications (HTTP-based)

● graph-data oriented, designed independently from ontology & related reasoning○ but some SPARQL implementations can provide reasoning (e.g.

RDFS+)

● declarative approach carrying several similarities to SQL

tutorial: https://jena.apache.org/tutorials/sparql.html

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Basic Graph Patterns

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Graph Group Pattern

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Filtering Unwanted Results

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SPARQL: Combination of Complex Graph Patterns

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SPARQL - additional constructs

● alternative result types:○ ASK ⇒ true, if a valid binding can be found○ CONSTRUCT ⇒ create new graph from result bindings

● combinators and modifiers for queries/graph patterns:○ UNION, MINUS, Subqueries○ LIMIT, OFFSET, DISTINCT, ORDER BY

● property paths as regular language (*,+,^,{n,m})○ e.g. rel:hasParent / rel:hasChild{2} / rel:hasFriend+

● sizable library of functions and operators for resources and literal values

find it all at: http://www.w3.org/TR/sparql11-query/

Linked Data & DBpedia

End of session 1

Grab a coffee…

Next session:helpful LD technologies for:

- NLP- Link Discovery- Data Fusion

A common Use Case for integrating LD technologies

Linked Data & DBpedia

NIF - Natural Language Processing Interchange Format

● RDF/OWL-based

● utilizes various existing standards: RDF, OWL2, PROV Ontology, ITSRDF, …

● promotes stable URIs to identify primary text, its structure, annotations and their meta-data

Linked Data & DBpedia

Interoperability for Language Data and Tools

Structural Interoperability:unanimous data format and structure of annotations ⇒ RDF & NIF vocab

Conceptual Interoperability:identical vocabularies/taxonomies for annotations (or linkage to common reference vocabulary) ⇒ Ontology of Linguistic Annotations (OLiA), GOLD, ...

Access Interoperability:unanimous, widespread, easily adoptable method for access ⇒ REST

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NIF: String Relations and Text Structure

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Integration of NIF service results

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Integration of NIF service results

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Integration of NIF service results

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Linking OWL Ontologies for Conceptual Interoperability

● Ontology of Linguistic Annotations○ linking specific annotation tag sets into

ling. refrence models/ontologies○ machine-actionable, granular

representations of semantics of tags (beyond string values)

Linked Data & DBpedia

NIF: Further Widespread Requirements Covered

● Provenance and Confidence for Annotations

● Multiple Alternative Annotations

exdoc:2_offset_23_29 nif:anchorOf "Berlin" ; itsrdf:taIdentRef <http://dbpedia.org/resource/Berlin> ; nif-ann:taIdentConf "0.9"^^xsd:decimal ; nif-ann:taIdentProv exdoc:eEntityProdServiceInvocation ; nif:annotationUnit [ itsrdf:taIdentRef <http://dbpedia.org/resource/Berlin,%20Nevada> ; nif-ann:taIdentConf "0.32"^^xsd:decimal ; nif-ann:taIdentProv exdoc:eEntityExpServiceInvocation ] .

Linked Data & DBpedia

Available NIF Resources

● Corpora

● Services○ Tokenisation○ Annotation○ Validation○ Combining Tools Outputs

● Documentation, Specs

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NIF Corpora

● Brown Corpus

● AQUAINT News Corpus

● NER corpora○ RSS-500, Reuters-128, KORE 50○ Microposts NEEL○ ACE Mutlilingual

● DBpedia abstract corpora○ English, French, German, Dutch,

...

Linked Data & DBpedia

NIF Services

● OpenNLP○ POS tags

● Stanford NLP○ POS tags, lemmatization

● Snowball○ Stemming

● DBpedia Spotlight

● Validation (via RDFUnit)

Linked Data & DBpedia

Mapping Languages

● Helps to create class mappings between source dataset and target RDF ontology○ E.g. Table heading to RDF

predicates (e.g. rdfs:label)

Linked Data & DBpedia

Mapping Languages cont.

● R2RML: Only supports mappings between relational databases and RDF

● RML: Extension of R2RML and supports other input data formats such as CSV, JSON, XML

● SML: Extension of R2RML and supports other input formats such as CSV

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ETL Frameworks

● Extract Transform Load (ETL)● Common in Data Warehousing● Extract phase:

○ Extract data from data sources (e.g. CSV, JSON, database, etc.)● Transform phase:

○ Transform data for storing in target format○ Boilerplating/Normalization○ Content Enrichment (e.g. loading of geo-information)

● Load phase:○ Loads data into target data store (e.g. Virtuoso)

Linked Data & DBpedia

ETL Frameworks - LDIF

● Linked Data Integration Framework (LDIF)

● Hadoop based ETL pipeline● Supports Provenance Metadata● Components:

○ Scheduler○ Data Import

(Crawl, Sparql, Dump)○ ETL

● Custom Mapping Language

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ETL Frameworks - Unified Views

● Joined project between Semantic Web Company and Semantica.cz○ Supported by LOD2 FP7 project

● Components:○ Frontend UI○ Backend○ Database○ Scheduler

● Possible to add custom plugins

● Link

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Link Discovery Frameworks

● Finding links between related data items in different datasets● Use cases:

○ owl:sameAs links○ Class mappings (e.g. like R2RML)○ data transformation

● Survey● Matching algorithms:

○ string similarity, geo-location matching, regular expressions, etc.● Link Discovery Strategies

○ Rule based: Using predefined rules to find matching data items○ Statistical based: Using machine learning techniques to find matching

data items

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Link Discovery Frameworks - LIMES

● Link discovery framework for MEtric Spaces

● Fast, large-scale link discovery using specification language

● Link

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Link Discovery Frameworks - Silk

● UI driven linking framework

● Uses its own specification language

● Supports for data transformation

● Link

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Data Fusion

● Fusing of multiple records representing the same real-world object into a single, consistent, and clean representation (Bleiholder & Naumann 2008)

● Possible use cases:○ Same value for the same property in all datasets (e.g. name)○ Different value for the same property in all datasets (e.g. age)○ New information

● Problems:○ No unique IDs○ Real world data is dirty, big and complex○ No training data for many linkage applications○ Trustworthiness of external data

Linked Data & DBpedia

Data Fusion - Strategies

● Rule based○ Using observed value from most updated source○ Taking average/maximum/minimum for numerical values○ Idea is to improve efficiency

● Statistically based○ Unsupervised/supervised strategies:

■ Vote: Take the value which is supported by largest number of sources■ Quality based: evaluate trustworthiness

● Web-link-based, IR-based, Bayesian, graphical model■ Relation Based

● Extends Quality Based methods and considers relationship between sources (e.g. copy data around, etc.)

Linked Data & DBpedia

Data Fusion - LD-FusionTool

● Developed in conjunction with Unified Views project● Features:

○ Resolution of schema and identity conflicts○ Resolution of data conflicts○ Quality Assessment○ Provenance Tracking

● No machine learning based fusion● Link

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Data Fusion - Sieve

● Developed in conjunction with LDIF project● Features:

○ Resolution of data conflicts○ Quality Assessment○ Provenance Tracking○ Support for Plugins

● Link

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Data Fusion - Sieve

Examples

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ALIGNED - Software an Software & Data Engineering

● quality-centric, software and data engineering ● research project funded by Horizon 2020 (EC)● will develop new ways to build and maintain IT systems

that use big data on the web

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ALIGNED - One Page

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ALIGNED - Goals

● New methodology for parallel software and data engineering of web-scale information systems. ○ Linked Data the unifying foundation for system specification, process

and tool integration. ○ Support evolution of software dependent on heterogeneous, complex

data of varying quality with an independent lifecycle.

Linked Data & DBpedia

ALIGNing Problem: the example of DBpedia

Lot’s of code & a lot more data● Wikipedia evolves over time

○ Infobox Templates change, merge, deleted○ New formatting templates○ Structural differences per language edition

● DBpedia Ontology and Mappings change as well● Code should adapt to all the changes

○ hard at this (data) scale→ Data Quality will suffer

Linked Data & DBpedia

Unit-testing to the rescue?

● Software & Data testing● Straightforward for software (since 70’s)● Preliminary for (RDF) data

○ RDFUnit, SPIN…○ W3C Data Shapes WG

Data testing● Generation: manual, (Semi)automatic, ...● Linking: data & software tests

Linked Data & DBpedia

RDF Unit

http://rdfunit.aksw.org

Linked Data & DBpedia

RDF Unit

● Input: ontologies, updated Data Quality Patterns (DQP),and the Datasets to test against

● Produces Data Unit Test Cases automatically by applying DQPs to the Axioms of an ontology○ User defined test cases are added as well

● Runs all Data Unit Test Cases against a given Dataset● Generates Test Case Result data for every violating

triple for one of these DQPs● ( evaluate Test Case Results to change triples, software

or DQP/Test Cases, then run RDF Unit again… )

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DBpedia+ Workflow

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FREME: Multilingual Content Enrichment & Curation

● two year H2020 innovation action: bridge language and data

● driven by four business use-casesbusiness partners:vistatec - translation, localisation, content creation/curation

tilde - language & terminology services

agroknow - agriculture & food information & research

wripl - content optimisation & personalisation, SEO

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Current State and Challenges for Content Enrichment

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Contribution of FREME

various target user groups:● developers● content authors● content architects● ...

several access modes:● graphical interfaces● programmatically● official endpoints and

local service instance

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FREMEs e-Services

● e-Entity: for enriching content with information on named entities

● e-Link: for enrichment with linked data sources● e-Terminology: for detecting terms and enriching them

with term related information;● e-Translation: for providing custom machine translation

systems● e-Publishing: for exporting enriched contend in the ePub

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FREME Demo

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Linked Data in FREME

● service interoperability and integration using NIF

● low entrance barrier: start for test / limited volumes immediately, just using REST queries

● utilization of several popular Linked Data knowledge bases:Europeana (cultural heritage data), ORCID (research idetifiers), ONLD (organisation names), Library of Congress Authorities

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Named Entity Spotting, Recognition and Linking

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Choice of the Most Appropriate NER/NEL Tool

● Topic/Domain of○ used training data○ knowledge bases linked against

● Overall Performance (Precision,Recall,...)

● Support for/ Performance for specific Entity Categories (Companies, Artists, …)

●

?

Linked Data & DBpedia

GERBIL - Sustainable Entity Annotation Benchmarks

● unified experiment setups

● extensible for additional services and datasets

● experiment results as Linked Data Resources○ easy documentation○ improving

reproducibility

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Smart Data Web (SDW)

- BMWi funded project- Main goal: Data collection for

the German industry using state of the art extraction and enrichment technologies

- Use Cases:- Supply Chain Management- Market Research

Linked Data & DBpedia

SDW - Knowledge Graph

- AKSW/KILT group responsible for Knowledge Graph

- Curated sources:- DBpedia, PermID, GRID, etc.

- Uncurated sources:- Twitter, news feeds, etc.

- Data quality and persistence- RdfUnit: test driven data-

debugging framework- LIMES: link discovery for the web

of data

Linked Data & DBpedia

SDW - Use Case: Supply Chain Management

● Public KG is fed continuously with information from news channels, websites, etc.

● Corporate/Internal KG is connected to public KG● Detection of potential problems in supply chain

○ Need to check suppliers regularly■ Check for compliance■ Quality of products■ Who else is being supplied by this supplier?■ strike, natural disaster, insolvency, etc.■ ...

○ Get information about potential problems as quickly as possible

Linked Data & DBpedia

SDW - Use Case: Market Research

● Public KG is fed continuously with information from news channels, websites, etc.

● Finding more information about value chain:○ Potential leads/potential customers○ Competition○ Potential suppliers○ Price development on the market○ Customer satisfaction

● Connecting information from different data silo○ Finding out about new relations

Linked Data & DBpedia

SDW - NLP

- Extraction of company and traffic events using state of the art NLP and machine learning technologies

- Demo

Linked Data & DBpedia

SDW - Linked Data

● Common public knowledge graph (KG)○ Modular corporate ontology○ ETL pipeline for different datasets○ Fusion of different datasets and web data

● Unique URIs for all entities (through knowledge fusion)● Store meta-data (e.g. provenance) for each RDF

statement● Use KG for NLP tasks (e.g. Named Entity Recognition,

Disambiguation, etc.)● Use KG for Enterprise Search

Linked Data & DBpedia

Use Case: Introducing Linked Data into an established IT environment

● Task 1: Support transformation from relational database data to RDF from different sources○ Establish a transformation process from SQL data to RDF.○ Combine multiple DBs into a single RDF

● Task 2: Generate Software components capable of manipulating the underlying data by any user○ Based on the domain description (ontology)

● Task 3: Enable data quality checks using data constraints○ Applying an iterative process of developing an ontology, providing test

results and correcting both data and ontology in a changing overall software environment.

Linked Data & DBpedia

Task 1: Transforming DB-data into RDF

● Using a Mapping Language like R2RML can generate a RDF (or Triple-) View of the relational data○ Databases like Virtuoso already have all tools needed for this mapping

included and provide an automated mapping function if needed.○ Multiple approaches for automated creation of R2RML mappings exist

creating Wrapper layer on to of the Relational Database (RDB)○ A different approach is the query rewriting SPARQL → SQL,

needs an ontology adaptation of the RDB schema● Multiple mappings for additional databases

Linked Data & DBpedia

Task 2: ALIGNED Tool - Semantic Booster

● Given a domain description as input (ontology, DB schema, etc.)

● Optional mapping to an existing DB schema

● Automatic creation of a Booster Specification

● Automatic generation of a SQL based DB-specification.

● The data is propagated through the Booster web interface

Linked Data & DBpedia

Semantic Booster - Features

● Generating high-quality software components from precise models with metadata annotations.

● Using Model-Driven Engineering techniques to generate a complete information system.

● key feature: Allows for the smooth transition of data when the underlying database is updated.

● Enables domain experts to develop systems conformant with existing standards, datasets or systems.

Linked Data & DBpedia

Booster Web Interface

Linked Data & DBpedia

Task 3: Data Quality Validation

Option A: ● use the Form Validation methods of the Booster Web

Interface to validate user input directly○ Useful for simple domains without many restrictions

Option B:● Use RDF Unit to automatically generate data unit tests

○ for more complex domains

Linked Data & DBpedia

Creating data unit tests with RDF Unit

● Common restrictions (e.g. cardinalities, domain/range) are automatically transformed into unit tests by RDF U.

● More complex restriction can be inserted ○ with new unit patterns → enabling RDF Unit to generate the test itself○ Using custom SPARQL queries → defining the pattern to look for by

the user● A failed unit test will produce error object in RDF

serialization○ containing the necessary metadata to pinpoint the offending triple

Linked Data & DBpedia

Evaluating RDF Unit results

● Test case results can be used to implement a (semi-) automatic process to improve the tested data or its generating software

● Or to provide statistics about the quality of a dataset● ...

Linked Data & DBpedia

Completing the tasks

In addition to validation processes, any number of tasks can be executed on the given data● e.g. extracting NIF annotations on linguistic data● Spotlight Entity recognition● etc.

Linked Data & DBpedia

Summary

● WOD: applying the principles of the WWW to data● Bridging disciplines and domains (by linking their data) ● Linked Data makes Smart Data out of Big Data● Many Linked Data Standards can be reused for Big Data● DBpedia can be used as for many domains and

processes● Linked Data can be applied in many different parts of

commercial environments

Linked Data & DBpedia

Q&A