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  • Linked Data & DBpediaM. Freudenberg, K. Mller & M. Ackermann

    AKSW/KILT - LeipzigDBpedia Association

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    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

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

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    TimBLs 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

    dont hoard your data, if possible: share itWatch the TED talk of TimBL about Linked Data

    https://www.ted.com/talks/tim_berners_lee_on_the_next_web?language=enhttps://www.ted.com/talks/tim_berners_lee_on_the_next_web?language=en

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    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)

    Siemens

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    The Data (in Turtle)

    dbo:type ; rdfs:label "Siemens"@de ; dbo:location .

    dbo:country .

    http://dbpedia.org/resource/Aktiengesellschafthttp://dbpedia.org/resource/Munichhttp://dbpedia.org/resource/Aktiengesellschaft

  • 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

  • Linked Data & DBpedia

    5 Linked Open Data

  • Linked Data & DBpedia

    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

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    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 its based on Wikipedia). Links actively to many relevant

    Linked Open Datasets. Is a link destination for many other

    Datasets.(more on DBpedia later)

  • Linked Data & DBpedia

    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

    http://www.geonames.orghttp://www.geonames.orghttp://www.geonames.org/3399415/fortaleza.htmlhttp://lov.okfn.org/dataset/lovhttp://lov.okfn.org/dataset/lovhttp://lov.okfn.org/dataset/lov/vocabs/foafhttp://www.lexvo.orghttp://www.lexvo.orghttp://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.

    http://w3c.github.io/data-shapes/shacl/http://wiki.dbpedia.org/projects/dbpedia-dataid

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    Incremental adoption of LD technologies

    Linked Data standards and technologies are manifold and, at times, confusing.Fortunately, i