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Tag Archives for metadata

Three shades of metadata

Search terms are like keys to documents or to any other type of content. In our days, not only the volume of available documents is increasing rapidly, but also the size of the keychain is growing.

Before we start to discuss how to organize/index the documents themselves, we should first talk about methods to organize the keys.

Take a look at these three approaches:

Semantic Keys

On the left-hand side you can see a nice example of implicit semantics. The old receptionist knows exactly which key fits into which lock. This knowledge is implicit and can be remembered by the shapes of the keys. Before the old receptionist has retired and the knowledge would have gone, labels have been attached to every key. The semantics of the keys has become more explicit, but this is still quite ambiguous (as visualised in the center column). There is no index of all the labels and colours being used, neither an explicit methodology how new keys should be labeled. As the hotel grows, the labeling system becomes quickly a mess. On the right-hand side the solution for this problem is offered: Not only the semantics of the keys becomes more explicit, but also the semantics of the semantics. For instance, the position of a key represents the position of the room, which can be unlocked by this. The number of the row of the key cabinet represents the storey of the room being unlocked, etc.

This methodology in order to organise keys helps to orientate and to remember; it can be explained with low effort to any new receptionist and it can be scaled-up in case your hotel should grow in the next few years.

March 17, 2015 by ablvienna Categories: intelligent search | Tags: information management, knowledge organization, metadata, visualisation

Why semantic knowledge graphs matter

Most information professionals already know: separation of content and presentation helps to manage and deliver complex information. This can only be done by using enriched structured content. Some call this intelligent content.

But why exactly is metadata per document (some call it “tagging”) not enough?

Here is a very brief slide-deck, which explains the difference between the traditional approach and the graph-based approach to develop not only a metadata layer seperated from the content layer, but also a knowledge layer on top of it.

Why semantic knowledge graphs matter

February 16, 2015 by ablvienna Categories: intelligent search, knowledge modeling, linked data, tagging, Uncategorized | Tags: knowledge graph, linked data, metadata, semantic web, tagging | Leave a comment

Using SKOS as an interface to the Linked Data Cloud

PoolParty 2.7 offers new and comfortable ways to enrich any SKOS thesaurus with additional facts from the semantic web (see: LOD cloud). This functionality (which was extended significantly with version 2.7 in June 2010) supports any thesaurus manager to generate much richer knowledge models (ontologies) around specific domains than ever before (without facing high extra costs due to additional research). There are at least three arguments why one should consider building such “extended thesauri”:

  1. Use even more metadata to describe your resources and improve navigation and semantic search functionalities significantly
  2. Publish (at least) parts of your metadata / knowledge models as linked (open) data to stimulate innovative services around your contents on top of network effects
  3. Use linked data for data integration and semantic mashups; combine your own contents with contents from the web to improve your business intelligence

A short example: Just imagine you are working on a knowledge model in the area of “Skiing in Austria”. You have stated that one of Tyrols´s (most famous) skiing areas is “Kitzbühel“. By looking up at geonames.org you get extra metadata, e.g. alternate labels like “Kitzbichl” or longitude and latitude to display the concept on a map. In a next step you add famous Austrian skiers like “Hermann Maier” and “Franz Klammer“. From DBpedia you retrieve additional category information like Maier is a “Person born in 1972“, thumbnail pictures and also some links to other linked data sources, e.g. to the New York Times. Here we can learn that the NYT has mentioned Hermann Maier in 14 articles already. Finally we can add “Toni Sailer” as a third skier and we will find out by harvesting linked data that he was born in Tyrol and therefore we can add a new relation in our thesaurus between him and Tyrol.

We have learned: Linked Data can help us to build expressive knowledge models by using SKOS as an “interface” to the Linked Data Cloud.

SKOS thesauri can not only serve as a backbone for rich metadata structures to improve search applications but also as a new linked data source to be published and to be linked with other semantic data. PoolParty 2.7 follows many suggestions from “Linked Data Patterns” (edited by Leigh Dodds and Ian Davis) how linked data should be published. For instance, there are various ways with PoolParty 2.7 to identify resources, e.g. via “Patterned URIs” or via “Literal Keys” (see, for example, http://vocabulary.semantic-web.at/SemanticWebThesaurus/controlledvocabulary).

PoolParty uses TuQS as very fast linked data lookup service and can harvest data from virtually any linked (open) data source which provides a SPARQL-endpoint, e.g. DBpedia, Geonames, Wordnet, UMBEL or PoolParty sources themselves.

Don´t forget: SKOS stands for Simple Knowledge Organization System, thus PoolParty was designed as an easy-to-use Linked Data and Thesaurus Editor and Publishing System.

June 7, 2010 by ablvienna Categories: linked data, skos | Tags: metadata | Leave a comment

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