Traditional KOSs include a broad range of system types from term lists to classification systems and thesauri. These organization systems vary in functional purpose and semantic expressivity. Most of these traditional KOSs were developed in a print and library environment. They have been used to control the vocabulary used when indexing and searching a specific product, such as a bibliographic database, or when organizing a physical collection such as a library (Hodge et al. 2000). Continue reading
Enabling and managing interoperability at the data and the service level is one of the strategic key issues in networked knowledge organization systems (KOSs) and a growing issue in effective data management. But why do we need “semantic” interoperability and how can we achieve it?
Interoperability vs. Integration
The concept of (data) interoperability can best be understood in contrast to (data) integration. While integration refers to a process, where formerly distinct data sources and Continue reading
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:
The 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.
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’.
The 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.
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!
The proposed scheme includes several discrete sets of categories called facets whose values can be combined to express concepts such as existing Physics and Astronomy Classification Scheme (PACS) codes, as well as new concepts that have not yet emerged, or have been difficult to express with the existing PACS.
PACS codes formed a single-hierarchy classification scheme, designed to assign the “one best” category that an item will be classified under. Classification schemes come from the need to physically locate objects in one dimension, for example in a library where a book will be shelved in one and only one location, among an ordered set of other books. Traditional journal tables of contents similarly place each article in a given issue in a specific location among an ordered set of other articles, certainly a necessary constraint with paper journals and still useful online as a comfortable and familiar context for readers.
However, the real world of concepts is multi-dimensional. In collapsing to one dimension, a classification scheme makes essentially arbitrary choices that have the effect of placing some related items close together while leaving other related items in very distant bins. It also has the effect of repeating the terms associated with the last dimension in many different contexts, leading to an appearance of significant redundancy and complexity in locating terms.
A faceted taxonomy attempts to identify each stand-alone concept through the term or terms commonly associated with it, and have it mean the same thing whenever used. Hierarchy in a taxonomy is useful to group related terms together; however the intention is not to attempt to identify an item such as an article or book by a single concept, but rather to assign multiple concepts to represent the meaning. In that way, related items can be closely associated along multiple dimensions corresponding to each assigned concept. Where previously a single PACS code was used to indicate the research area, now two, three, or more of the new concepts may be needed (although often a single new concept will be sufficient). This requires a different mindset and approach in applying the new taxonomy to the way APS has been accustomed to working with PACS; however it also enables significant new capabilities for publishing and working with all types of content including articles, papers and websites.
To build and maintain the faceted taxonomy, APS has acquired the PoolParty taxonomy management tool. PoolParty will enable APS editorial staff to create, retrieve, update and delete taxonomy term records. The tool will support the various thesaurus, knowledge organization system and ontology standards for concepts, relationships, alternate terms etc. It will also provide methods for:
- Associating taxonomy terms with content items, and storing that association in a content index record.
- Automated indexing to suggest taxonomy terms that should be associated with content items, and text mining to suggest terms to potentially be added to the taxonomy.
- Integrating taxonomy term look-up, browse and navigation in a selection user interface that, for example, authors and the general public could use.
- Implementing a feedback user interface allowing authors and the general public to suggest terms, record the source of the suggestion, and inform the user on the disposition of their suggestion.
Arthur Smith, project manager for the new APS taxonomy notes “PoolParty allows our subject matter experts to immediately visualize the layout of the taxonomy, to add new concepts, suggest alternatives, and to map out the relationships and mappings to other concept schemes that we need. While our project is still in an early stage, the software tool is already proving very useful.”
Taxonomy Strategies (www.taxonomystrategies.com) is an information management consultancy that specializes in applying taxonomies, metadata, automatic classification, and other information retrieval technologies to the needs of business and other organizations.
The American Physical Society (www.aps.org) is a non-profit membership organization working to advance and diffuse the knowledge of physics through its outstanding research journals, scientific meetings, and education, outreach, advocacy and international activities. APS represents over 50,000 members, including physicists in academia, national laboratories and industry in the United States and throughout the world. Society offices are located in College Park, MD (Headquarters), Ridge, NY, and Washington, DC.