Andreas Blumauer

From Taxonomies over Ontologies to Knowledge Graphs

With the rise of linked data and the semantic web, concepts and terms like ‘ontology’, ‘vocabulary’, ‘thesaurus’ or ‘taxonomy’ are being picked up frequently by information managers, search engine specialists or data engineers to describe ‘knowledge models’ in general. In many cases the terms are used without any specific meaning which brings a lot of people to the basic question:

What are the differences between a taxonomy, a thesaurus, an ontology and a knowledge graph?

This article should bring light into this discussion by guiding you through an example which starts off from a taxonomy, introduces an ontology and finally exposes a knowledge graph (linked data graph) to be used as the basis for semantic applications.

1. Taxonomies and thesauri

Taxonomies and thesauri are closely related species of controlled vocabularies to describe relations between concepts and their labels including synonyms, most often in various languages. Such structures can be used as a basis for domain-specific entity extraction or text categorization services. Here is an example of a taxonomy created with PoolParty Thesaurus Server which is about the Apollo programme:

Apollo programme taxonomyThe nodes of a taxonomy represent various types of ‘things’ (so called ‘resources’): The topmost level (orange) is the root node of the taxonomy, purple nodes are so called ‘concept schemes’ followed by ‘top concepts’ (dark green) and ordinary ‘concepts’ (light green). In 2009 W3C introduced the Simple Knowledge Organization System (SKOS) as a standard for the creation and publication of taxonomies and thesauri. The SKOS ontology comprises only a few classes and properties. The most important types of resources are: Concept, ConceptScheme and Collection. Hierarchical relations between concepts are ‘broader’ and its inverse ‘narrower’. Thesauri most often cover also non-hierarchical relations between concepts like the symmetric property ‘related’. Every concept has at least on ‘preferred label’ and can have numerous synonyms (‘alternative labels’). Whereas a taxonomy could be envisaged as a tree, thesauri most often have polyhierarchies: a concept can be the child-node of more than one node. A thesaurus should be envisaged rather as a network (graph) of nodes than a simple tree by including polyhierarchical and also non-hierarchical relations between concepts.

2. Ontologies

Ontologies are perceived as being complex in contrast to the rather simple taxonomies and thesauri. Limitations of taxonomies and SKOS-based vocabularies in general become obvious as soon as one tries to describe a specific relation between two concepts: ‘Neil Armstrong’ is not only unspecifically ‘related’ to ‘Apollo 11′, he was ‘commander of’ this certain Apollo mission. Therefore we have to extend the SKOS ontology by two classes (‘Astronaut’ and ‘Mission’) and the property ‘commander of’ which is the inverse of ‘commanded by’.

Apollo ontology relationsThe SKOS concept with the preferred label ‘Buzz Aldrin’ has to be classified as an ‘Astronaut’ in order to be described by specific relations and attributes like ‘is lunar module pilot of’ or ‘birthDate’. The introduction of additional ontologies in order to expand expressivity of SKOS-based vocabularies is following the ‘pay-as-you-go’ strategy of the linked data community. The PoolParty knowledge modelling approach suggests to start first with SKOS to further extend this simple knowledge model by other knowledge graphs, ontologies and annotated documents and legacy data. This paradigm could be memorized by a rule named ‘Start SKOS, grow big’.

3. Knowledge Graphs

Knowledge graphs are all around (e.g. DBpedia, Freebase, etc.). Based on W3C’s Semantic Web Standards such graphs can be used to further enrich your SKOS knowledge models. In combination with an ontology, specific knowledge about a certain resource can be obtained with a simple SPARQL query. As an example, the fact that Neil Armstrong was born on August 5th, 1930 can be retrieved from DBpedia. Watch this YouTube video which demonstrates how ‘linked data harvesting’ works with PoolParty.

Knowledge graphs could be envisaged as a network of all kind things which are relevant to a specific domain or to an organization. They are not limited to abstract concepts and relations but can also contain instances of things like documents and datasets.

Why should I transform my content and data into a large knowledge graph?

The answer is simple: to being able to make complex queries over the entirety of all kind of information. By breaking up the data silos there is a high probability that query results become more valid.

With PoolParty Semantic Integrator, content and documents from SharePoint, Confluence, Drupal etc. can be tranformed automatically to integrate them into enterprise knowledge graphs.

Taxonomies, thesauri, ontologies, linked data graphs including enterprise content and legacy data – all kind of information could become part of an enterprise knowledge graph which can be stored in a linked data warehouse. Based on technologies like Virtuoso, such data warehouses have the ability to serve as a complex question answering system with excellent performance and scalability.

4. Conclusion

In the early days of the semantic web, we’ve constantly discussed whether taxonomies, ontologies or linked data graphs will be part of the solution. Again and again discussions like ‘Did the current data-driven world kill ontologies?‘ are being lead. My proposal is: try to combine all of those. Embrace every method which makes meaningful information out of data. Stop to denounce communities which don’t follow the one or the other aspect of the semantic web (e.g. reasoning or SKOS). Let’s put the pieces together – together!

 

Andreas Blumauer

Why SKOS should be a focal point of your linked data strategy

skos_hand-small

The Simple Knowledge Organization System (SKOS) has become one of the ‘sweet spots’ in the linked data ecosystem in recent years. Especially when semantic web technologies are being adapted for the requirements of enterprises or public administration, SKOS has played a most central role to create knowledge graphs.

In this webinar, key people from the Semantic Web Company will describe why controlled vocabularies based on SKOS play a central role in a linked data strategy, and how SKOS can be enriched by ontologies and linked data to further improve semantic information management.

SKOS unfolds its potential at the intersection of three disciplines and their methods:

  • library sciences: taxonomy and thesaurus management
  • information sciences: knowledge engineering and ontology management
  • computational linguistics: text mining and entity extraction

Linked Data based IT-architectures cover all three aspects and provide means for agile data, information, and knowledge management.

In this webinar, you will learn about the following questions and topics:

  • How SKOS builds the foundation of enterprise knowledge graphs to be enriched by additional vocabularies and ontologies?
  • How can knowledge graphs be used build the backbone of metadata services in organisations?
  • How text mining can be used to create high-quality taxonomies and thesauri?
  • How can knowledge graphs be used for enterprise information integration?

Based on PoolParty Semantic Suite, you will see several live demos of end-user applications based on linked data and of PoolParty’s latest release which provides outstanding facilities for professional linked data management, including taxonomy, thesaurus and ontology management.

Register here: https://www4.gotomeeting.com/register/404918583

 

Andreas Blumauer

SEMANTiCS 2014: Call for Industry Presentations

SEMANTiCS 2014 will take place in Leipzig (Germany) this year from September 4-5. The International Conference on Semantic Systems will be co-located with several workshops and other meetings, e.g. the 2nd DBpedia community meeting.

SEMANTICS-2014-logo-leipzig

SEMANTiCS conference (formerly ‘I-Semantics’) focuses on transfer and industry-related applications of semantic systems and linked data.
Here are some of the options for end-users, vendors and experts to get involved (besides participating as a regular attendee and the option to submit a paper):

  1. Submit an Industry Presentation: http://www.semantics.cc/open-calls/industry-presentations/
  2. Sponsoring / Marketplace / Exhibition: http://www.semantics.cc/sponsoring
  3. Become a reviewer: http://www.semantics.cc/open-calls/call-for-participation/call-for-reviewers/

The organizing committee would be happy to have you on board of the SEMANTiCS 2014 in Leipzig.

Andreas Blumauer

Linked data based search: Make use of linked data to provide means for complex queries

Two live demos of PoolParty Semantic Integrator demonstrate new ways to retrieve information based on linked data technologies

data visualisation

Linked data graphs can be used to annotate and categorize documents. By transforming text into RDF graphs and linking them with LOD like DBpedia, Geonames, MeSH etc. completely new ways to make queries over large document repositories become possible.

An online-demo illustrates those principles: Imagine you were an information officer at the Global Health Observatory of the World Health Organisation. You inform policy makers about the global situation in specific disease areas to direct support to the required health support programs. For your research you need data about disease prevalence in relation with socioeconomic factors.

Datasets and technology

About 160.000 scientific abstracts from PubMed, linked to three different disease categories were collected. Abstracts were automatically annotated with PoolParty Extractor, based on terms from the Medical Subject Headings (MeSH) and Geonames that are organized in a SKOS thesaurus, managed with PoolParty Thesaurus Server. Abstracts were transformed to RDF and stored in Virtuoso RDF store. In the next step, it is easy to combine these data sets within the triple store with large linked data sources like DBPedia, Geonames or Yago. The use of linked data makes it easy to e.g. group annotated countries by the Human Development Index (HDI). The hierarchical structure of the thesaurus was used to collect all concepts that are connected to a specific disease.

This demo was developed based on the libraries sgvizler to visualize SPARQL results. AngularJS was used to dynamically replace variables in SPARQL query templates.

Another example of linked data based search in the field of renewable energy can be tried out here.

Links: