Taxonomy Boot Camp London 2026

I was thrilled to participate in the Taxonomy Boot Camp London conference, which was in-person in London this past month for the first time since 2019. A sister conference to Taxonomy Boot Camp in the United States, which has been running since 2005, Taxonomy Boot Camp London had been running “Bite- Sized” online editions of half a day three times per years since 2020, which had been so successful that they continued through last year. The online edition will continue now, once a year, scheduled next for October 7.

Taxonomy Boot Camp London continues to be successfully chaired by London-based taxonomy consultant Helen Lippell, since its first year. She summarized this year’s conference: “I pushed the boundaries of my own knowledge and got to see a huge range of talks by our wonderful speakers …. Our workshops gave attendees the perfect grounding in foundational concepts too.”

As taxonomies are a niche specialty, which are applied to other related fields, the Taxonomy Boot Camp conference is always combined (co-located) with other conferences operated by Information Today Inc. In the United States, this has always been with KMWorld (knowledge management) and additional co-located conferences. For Taxonomy Boot Camp London, from 2016 to 2019 the conference had been co-located with Internet Librarian International to bring in enough attendance to make use of the venue and catering, but the conferences were not similar enough in content or attendance, and did not share keynotes, exhibits, or breaks. This year, for the first time, a new conference of KMWorld Europe was launched, and Taxonomy Boot Camp was fully combined with it, sharing keynotes, meals and breaks, exhibit space, and registration options. This made a lot more sense, due to the overlap of taxonomies and knowledge management. Personally, I also enjoyed seeing knowledge management colleagues, in addition to taxonomy colleagues, at the conference.

Conference Sessions

The format of the conference was the same as in previous years. After a shared keynotes each day, the conference is run in two tracks each day. Tracks are not the same as Taxonomy Boot Camp (Washington, DC (Beginner and case studies, and in two tracks only the first day) but rather on loose themes, which this year were “Components of Successful Semantic Projects”; “Joining Up Data With Semantics;” “Getting the Most of Curating Content, Data, and AI”; and “Taking Structure to the Next Level.” It was difficult to decide what to attend, and I moved between tracks often. 

Taxonomy Boot Camp London differs from Taxonomy Boot Camp (DC) by including preconference workshop options on the afternoon before the main conference. There were four workshops to choose from in the single time slot, two for Taxonomy Boot Camp, and two for KMWorld. “Taxonomy Design Fundamentals,” which I taught, and “Finding a Forever Home: Governance, Ownership, & the Long-Term Care of Taxonomies” were the two taxonomy workshops. 

The keynote speakers, Ben Clinch on the first day and Noz Urbina on the second day, both were excellent in taking up different angles to the topic of AI in knowledge management, while also touching on taxonomy.

What was interesting about the conference sessions was the diversity of presentation subjects. While some provided the expected information on how to create good taxonomies (including my joint presentation with Joseph Busch on Thesaurus Standards for Taxonomies”) and others were case study applications of taxonomies, there were additional, different topics. Bob Kasenchak of Factor presented an interesting perspective of semantic layers as abstraction layers, Teodora Petkova of Graphwise presented on how to embed meaning and consistency in content to support knowledge graphs and shared understanding. Craig Johnson of Xemma presented on how research was done to obtain taxonomist-user input in designing a new taxonomy management system.

Connecting to other knowledge organization systems was a common topic, with presentations on the connections of taxonomies and ontologies by Steve McComb of Semantic Arts and Paul Appleby and Ravinder Singh both of Graphifi, the intersection of taxonomies and terminologies by Jo Chapman, and taxonomies as metadata by Yonah Levenson.

There were, of course, numerous sessions on AI use in taxonomy building. Ahren Lehnart spoke about the ways to identify the best concepts out of those being suggested by machine learning and LLMs.  Panos Mitzias of Squirro presented on how AI can help accelerate tasks like concept discovery, drafting structures, and enriching taxonomies, but success still depends on clear scoping, stakeholder engagement, and ongoing governance. Fran Alexander of Expedia presented on various considerations regarding the use of LLMs in taxonomy creation including, provenance, traceability, authoritativeness, context, and the use of multiple LLM agents. Fran, Bob, Kasenchak, and Stephanie Lemieux came together for an impromptu panel discussion on the use of AI in taxonomy creation (filling in for a cancelled speaker). They spoke on the various positive uses of AI and the ways in which AI was still not so good. I found this panel most interesting, so I decided to submit such a panel topic for Taxonomy Boot Camp in Washington, DC, this November

Sessions are not recorded, but most of the slides are available on the conference website. Ahren Lehnart also blogged on the conference themes. 

Conference Details

The joint conferences had a total of about 250 attendees, which compares with 170 for Taxonomy Boot Camp London only in the prior years. (It’s not possible to break out Taxonomy Boot Camp registrants only, since many chose a “all access pass” to both conferences.)  The international aspect was great, with representatives from 29 countries. 

For the first time, the London conference (Taxonomy Boot Camp and KMWorld jointly) had a nearby off-site networking drinks reception the evening after the workshops and before the main conference. The semi-enclosed rooftop bar was a great place to meet and mingle. 

The conference facility venue location was better than previous years, being in central London, close to the Tower of London. The only issue is that the conference organizers were not sure how many attendees to expect, so they were conservative with the space, which turned out a little tight. Although there was enough seating the conference session rooms (barely), the showcase area, which was also where breakfast, lunch, and break refreshments were served, became quite crowded at times. So, it was challenging sometimes to meet people and visit all the exhibitors at times.

The vendor showcase was larger, and had better dedicated space, compared to the former Taxonomy Boot Camp London in-person events. I recall the 2-3 vendors back then having tables just outside the conference room doors. The dedicated showcase space where breakfast lunch and coffee breaks were served was a benefit for the exhibitors. As the venue was in the basement level, excavated ancient Roman walls were on display behind the exhibits. More taxonomy/ontology software vendors were present than in the past: Graphwise (formerly PoolParty), Squirro (vendor of Synaptica), Graphifi (vendor of Graphologi), and a brand new entrant Xemma. The taxonomy/ontology vendors were mixed in with the knowledge management vendors without distinction, and it was good to have this cross-over to learn more about what is available.

Taxonomy Boot Camp in London and the United States

The scope of subjects and themes of Taxonomy Boot Camp London are the same as at Taxonomy Boot Camp in the United States, but the many of the presenters are different with different case studies and stories to tell, and those presenters who are the same (like myself) do not give the same presentations at both conferences. The attendees (delegates) are also different. So, if you're just getting started with taxonomies, either Taxonomy Boot Camp London, or Taxonomy Boon Camp in Washington, DC, whichever is more convenient, is appropriate. If taxonomies are your profession, then you should try to attend each conference at least once. It’s worth the trip. I am looking forward to Taxonomy Boot Camp London / KMWorld Europe next time in April 2027.

Helen Lippell reflected: “I thoroughly enjoyed seeing the event come to fruition after all the hard work the team put in over the last year, and one of my abiding memories will be walking around after the last sessions seeing everyone just chatting away while the venue staff tried to tidy up! I take this as a sign of our community being in rude health and ready to grow in future years.”

 

 

 

Taxonomy Sources: Re-Used, Licensed, or AI-Generated

As a taxonomist, I often write about creating taxonomies from scratch, but in practice, many organizations often obtain at least some taxonomies or controlled vocabularies from other sources.  Although internal content about an organization’s business, products, or services requires mostly custom taxonomies, some taxonomies, such as for regions or technologies, may come from other sources. Content that comes from external sources, such as research articles, is also be appropriate for tagging with taxonomies from other sources.

For “other sources,” these could be:

• Governmental agencies or nongovernmental organizations which publish taxonomies, thesauri, and subject heading schemes for their purposes but which are freely available

• Companies which sell their taxonomies

• Taxonomies that are generated by AI

Taxonomies for Re-Use or License

Types of taxonomies available can be categorized in multiple ways that overlap:

•  available for free or for a fee
•  available for commercial re-use or not available for commercial re-use
•  permissible for modification or not permitted to modify
•  designed a created for a specific content set or intended for broader use

I had previously blogged on taxonomies for license, discussing the issues of fees, availability for re-use, and permission for modification. Now I want to focus on the issue of using a taxonomy created for a specific purpose. 

Recently, I worked for a client that had created taxonomies for the life sciences industries with sections based on branches on the National Library of Medicine’s Medical Subject Headings (MeSH), because it was free. MeSH, however, had been designed for indexing medical research literature, and turned out not to be suitable for my client’s purpose of helping biomedical and pharmaceutical companies find articles relevant to their business and market.

For example, MeSH organizes drug types by their chemical types (Heterocyclic Compounds, Enzymes and Coenzymes, etc.). For a biomedical drug discovery company or a pharmaceutical company, however, the focus and classification of drugs is instead based on what kind of disease they treat (Cancer Drugs, Alzheimer’s Drugs, etc.). Thus using concepts from MeSH is not so suitable for pharmaceutical industry taxonomy.

Previously, I worked at Gale, which developed and managed many controlled vocabularies (or taxonomies) for indexing periodical and reference literature, which it sold to libraries. For a time, Gale also offered for license subject-domain subsets of its subject thesaurus of over 10,000 preferred terms. I realized that the business terms to index articles in business news sources were not necessarily the same terms that a company would want to tag its business documents and intranet pages. Others seemed to realize this too, and Gale didn't sell any stand-alone taxonomy licenses as long as I worked there. 

Taxonomies that are designed purely for sale and not designed with specific content and user type in mind are more suitable for licensing and re-use. I’ve seen a few small scale examples of this with sets of keywords for sale for tagging photos. The only commercial business I am aware of that licenses full taxonomies (with alternative labels and multiple hierarchies) in various business and industry domains is WAND. These taxonomies, which are also enriched with alternative labels (synonyms/variants) are a decent way to get started. The taxonomies can then be edited or supplemented as needed. WAND taxonomies, which are manually developed, are particularly useful for product and services categories in various industries.

AI-Generated Taxonomies

When I first explored the use of GenAI to create taxonomies (described in my prior blog post), I felt that the results were quite inadequate, as LLMs were pulling from multiple sources, where the same term could have different meanings in different contexts, different terms could refer to the same thing, and even the hierarchy would vary for different use cases.

More recently, I’ve used ChatGPT and Claude and found that the results, especially when focused in areas of science, technology, and medicine, have improved with respect to specific taxonomy hierarchies. Even when I did not ask for a taxonomy, the LLMs often return respectable three-level hierarchies of concepts in such topic areas as medical devices, drug types, and cell receptors. I also found AI tools useful for disambiguating similar terms or providing synonyms for technical terms I was not sure of. 

AI-generated taxonomies are a potential competitor to WAND’s taxonomies for sale, but this depends on the size and subject area. The WAND taxonomies are large and detailed in the number of concepts, hierarchical levels, alternative labels, and they have already been expertly created by humans. Using AI to create taxonomies works better on single hierarchical trees, and always requires human editing to refine and complete the taxonomies. Hierarchies and alternative labels are created in separate steps. For multiple smaller taxonomies or taxonomy facets, AI is likely the more practical option than licensing full taxonomies. 

So, it shouldn’t be a surprise that taxonomy management software is starting to integrate GenAI and LLMs to automate taxonomy creation. For example, Graphwise Modeling (formerly PoolParty) introduced a Taxonomy Advisor feature in 2024, which allows users to request suggestions for narrower concepts, alternative labels, and definitions. This month, Graphwise announced the additional Taxonomy Builder feature, which enables the generation of a complete taxonomy hierarchy. It can be used for small portions or larger portions of the taxonomy, as needed, and it’s convenient to have the capabilities within a single tool. It also takes care of the prompt creation, based on the existing hierarchy and the user-entered description of the taxonomy and any additional instructions. I do not create taxonomy hierarchies with AI tools often enough to become good at writing the best prompts, so I appreciate it when a tool helps with that. There will be more about this later, as I working on white paper and will be speaking in a webinar in April on GenAI/LLMs in taxonomy creation. 

When to use Other Sources

As mentioned previously, taxonomies published from external sources are best used for content from external sources. When it comes to AI-generated taxonomies, though, it’s not necessary to generate an entire taxonomy, hierarchy, or facet. AI methods are quite suitable for smaller components of a taxonomy, such as narrower concepts to a single concept. As such, AI uses in taxonomy development are more widely applicable, including for enterprise taxonomies. For example, AI could be useful for generating a list of document types for a document type facet, and then after review, those AI-suggested document types that are not applicable can be removed. The starter list of terms can get people thinking of what might be missing, which is easier than trying to come up with a list of terms from scratch. 

In conclusion, an AI-generated taxonomy, after human review and editing, is usually a better solution than a licensed taxonomy that was created for a different purpose, such as using MeSH for the commercial side of healthcare. A taxonomy that is partially generated by AI or fully generated by AI that uses multiple sources and appropriate prompts (such as what is built into Taxonomy Builder) is typically a better source than a taxonomy that was created for a specific and different use case or than a taxonomy whose license prohibits editing or commercial re-use. If you choose to generate taxonomies with AI, I am happy to offer my services to review and edit them!

What a Taxonomy is Not

Although taxonomies have become increasingly common within enterprises and on websites, they are not always well understood. Taxonomies are sometimes confused with other knowledge organizations systems, such as classification systems, website navigation schemes, business glossaries, or ontologies.

A taxonomy is a controlled, structurally organized set of unambiguous concepts, which may describe content, information, or data, and which users may be interested in querying about. A taxonomy links users to the information they seek by bringing together various users’ terms with the terms that occur in the content or data. Prior to the emergence of modern taxonomies in applications for digital information, indexes at the back of printed books had been serving a similar role (and they still do). I have already written a blog post on Taxonomy Definition, so to further clarify what taxonomies are, it is also useful to explain what taxonomies are not. 

 

Taxonomies are not the same as classification systems/schemes (such as industrial classification codes for economic analysis or medical classifications for health data collection or health insurance purposes), as the latter have mutually exclusive classes to which items are assigned for non-redundant analysis. Classification thus allows comparison, analysis, identification, location, and other actions associated with things based on their class. Taxonomies are organized sets of concepts tagged to content or associated with data, where the taxonomy organization serves merely for finding the desired concept or providing context for tagging. Thus, a concept may have more than one broader concept and thus appear in more than one place in the taxonomy hierarchy. 

Taxonomies are not the same as navigation systems, which are common in websites or web applications. A taxonomy is more similar to an index, while a navigation system is more similar to a table of contents. Menu labels in a navigation can link to only one page, whereas concepts in a taxonomy are tagged to multiple pages, content items, or data records. Navigation systems are only used in browsing, but taxonomies may be both browsed and searched for their concepts. Navigation systems reflect paths and established links to content, whereas taxonomies comprise concepts that become metadata when tagged to content. Navigation systems, like classification systems, are not frequently or easily changed, whereas taxonomies can grow and change continuously, as needed.

Taxonomies are not the same as business glossaries, which are lists of terms of relevance to an organization’s business along with their definitions, although there is usually considerable overlap between the terms an organization gathers for its glossary(s). Not only is there usually the difference of a taxonomy’s hierarchical structure (although categories could be assigned to glossary terms), but the ultimate objectives differ, resulting in differences of scopes of term inclusion. A business glossary includes all terms of importance to the business but may not be understood by everyone, so definitions need to be provided. There could be terms of importance, that need no definition, such as Marketing, so they are not included in the glossary. Technical terms and acronyms are usually included. A taxonomy, on the other hand, includes only the terms/concepts of which there are sufficient documents, pages, or content items to be tagged for retrieval. Sufficient content on a subject is a leading criteria for including a concept in a taxonomy.

Finally, taxonomies are not the same as ontologies. The confusion between the two may arise because taxonomies and ontologies are increasingly used in combination, and software (now referred to as TOMS for taxonomy-ontology management system) allows you to create a taxonomy and ontology as a single project or knowledge model. An ontology can be an upper-level model of a knowledge domain, but domain-specific ontologies may include multiple hierarchical levels of subclasses, and thus include what are essentially taxonomies. A taxonomy, however, can stand on its own without an ontology and serve the functions of tagging and retrieval via browsing and/or searching without the extension of an ontology. Ontologies support complex, multi-part queries involving reactions, and they support reasoning and inference, which taxonomies do not. Each utilizes different data models: SKOS for taxonomies and RDFS and OWL for ontologies. 

Prior blog posts I have written that compare taxonomies to other knowledge organization systems in more detail are: 

• Taxonomies vs. Ontologies
• Taxonomies vs. Thesauri
• Taxonomies vs. Thesauri: Practical Implementations
• Navigation Schemes vs. Taxonomies
• Classification Systems vs. Taxonomies
• Subject Headings vs. Taxonomies

Taxonomy Benefits Over an Ontology

In a recent conversation based on a LinkedIn post, someone asked “Why choose a taxonomy over an ontology?” This is a good question, since there has been a growing understanding that ontologies build upon taxonomies by adding more semantics, which enable additional benefits. I have presented at conferences on the topic of extending a taxonomy with an ontology. Taxonomies, however, have benefits that ontologies alone cannot provide.

I have compared taxonomies and ontologies in a past blog post (Taxonomies vs. Ontologies). Comparing their uses to taxonomies, ontologies support more complex multi-part searches, enable searching on data and not just content or full documents, and can connect across data in different repositories and sources, which leads to creating knowledge graphs or a semantic layer. Additionally, ontologies support modeling and exploration of complex relationships, graph visualizations, and support for reasoning and inferencing based on logic. Meanwhile, ontologies also include the basic feature of taxonomies of unlimited hierarchies of classes and subclasses. Thus, it may seem as if ontologies are superior to taxonomies and provide greater benefits than taxonomies.

Taxonomies, however, especially those based on the SKOS (Simple Knowledge Organization System) data model, have features and benefits not supported by ontologies alone which are based only on OWL and RDFS standards.  These taxonomy (or more broadly “controlled vocabulary”) features include the incorporation of synonyms to support searching and tagging, the support of multilingual concepts, the inclusion of definitions and notes in a standardized manner, the ability to map and link taxonomies together based on equivalent or related concepts, the alignment of the taxonomy with end-user applications including browsable hierarchies and facets for filtering, and finally the ease of implementation into various content systems.

Taxonomies are richer than ontologies in their linguistic aspects, including both synonyms and labels in other languages. Taxonomies are traditionally based on thesauri, which include the feature of having “equivalence” among multiple terms, whereby a preferred term may be “used for” other nonpreferred terms. The SKOS data model specifies a preferred label and any number of alternative labels and hidden labels for a concept. Furthermore, concepts may have labels in multiple languages, and this supports tagging content in different languages and retrieval by users of different languages.

In ontologies, there exists the OWL property of sameAs for equivalence of individuals and equivalentClass for equivalence of classes, but both tend to be used to declare equivalence across different datasets rather than for use within a single ontology, as there is no designation of preferred and alternative names. So, these OWL properties are more like mapping properties than support of synonyms within a controlled vocabulary. As such they do not support the basic purpose of alternative labels in a taxonomy, which is to enable matches to support searching on variant labels and tagging despite different words in texts for the same thing.

The SKOS data model for taxonomies defines properties for scope notes, editorial notes, history notes, examples, and definitions. These are standardized fields and thus the meanings of these notes fields are consistent across taxonomies, supporting interoperability and migration. In OWL ontologies there exists an annotation property, but its use broadly includes labels, definitions, synonyms, attribution, notes, or comments.  With such inconsistent use, annotations are not well supported in importing, exporting, or linking of ontologies.

SKOS also has a set of mapping relationships. While OWL supports equivalence with SameAs and equivalentClass, SKOS taxonomies have not only equivalence relationships, exactMatch, but also closeMatch, narrowMatch, broadMatch, and relatedMatch, and thus all concepts in two separate taxonomies can be mapped to each other, unlike two ontologies which may share only a few matches. The full mapping of one taxonomy for another supports various uses, including using one taxonomy in the front end and the other in the back end, tagged to content.

Finally, taxonomies are better suited for various content-based implementation and applications, especially with out-of-the-box systems, such web content management systems, digital asset management systems, SharePoint, etc. A taxonomy modeled is several SKOS concept schemes can designate each concept scheme as a facet in faceted search/browse system, in which a facet serves as a filter. A taxonomy built as a hierarchy tree can be implemented so that users can expand the tree to browse to narrower concepts and then they can retrieve content tagged with the most specific concept desired. Ontologies, even if they contain hierarchies of classes and subclasses, are typically visualized as graphs, and any hierarchies are not displayed in a front-end application. Furthermore, ontology visualizations are usually not linked to actual content or data as they serve just for visualizing.

In sum, while ontologies add richer semantics/ meaning to relationships and attributes, taxonomies have richer semantics/meaning for concepts. Combining a taxonomy and ontology can bring the best of both worlds, and semantic web standards of SKOS, OWL, and RDF-S are all compatible for combining within a single project, since they are all based on the RDF (Resource Description Framework) data model. However, in many cases, a taxonomy with rich meaning for concepts, support for synonyms in search and tagging, along interactive displays of hierarchies and/or facets, is all that is needed. You can always add an ontology later.

Schema Vocabularies and Value Vocabularies

There are different types of controlled vocabularies for information and knowledge management. Usually, we think of the various kinds of controlled vocabularies for purposes of tagging and finding information, such as term lists, authority files, thesauri, and taxonomies. In the broader context of information and knowledge management, there also exist higher-level controlled vocabularies called schema vocabularies. In this context, the better known (default) controlled vocabularies comprising specific concepts or terms for tagging content are called value vocabularies, since their terms/concepts are considered values.

This dichotomy of schema and value vocabularies occurs particularly within the context of metadata. Metadata management comprises two components: (1) a list of metadata types, also called elements, properties, or fields; and (2) the terms or values possible for each metadata element. I discussed types of metadata in more detail in my last blog post, "Types of Metadata Schema." Thus, a schema vocabulary comprises the names of metadata elements, and a value vocabulary is list of terms/concepts for a specific metadata element. For example, a schema vocabulary, might include Country, Language, Source, and Topic; and the multiple values vocabularies would be the lists of approved countries, languages, sources, and topics. It should be noted that in some systems, e.g. RDF, OWL, etc., the distinction between metadata elements and metadata values can be fuzzy. Furthermore, not all schema vocabulary elements have a corresponding value vocabulary (a controlled vocabulary), though, as some metadata elements may be for such values as title, description, and date. 

In my observation, we speak of “vocabularies” rather than “controlled vocabularies” in this context, especially with respect to schema, for various reasons. Schema vocabularies are referred to simply as “vocabularies,” rather than “controlled vocabularies,” because they are not traditional controlled vocabularies used for tagging, and also because their “control” is different from the control of value vocabularies. Value vocabularies can be changed but through defined policies and procedures, which depend on the implementation and ownership, and changes can be frequent, e.g. weekly, monthly, quarterly, or annually. Schema vocabularies, on the other hand, are intended to be standard, and are updated only very infrequently, such as once per 5-10 years, and usually by a standards body. Schema vocabularies provide control by their very nature. Meanwhile, it is often necessary to call out the controlled feature of value vocabularies, since some metadata properties may have uncontrolled keywords as their values.

Schema vocabularies may be metadata schema, such as Dublin Core (for published resources) or IPTC metadata (for photos), but other kinds of information and content management schema can also be considered as schema vocabularies in that a “vocabulary” defines the various elements. Such other schema vocabularies include SKOS (Simple Knowledge Organization System), DCAT (Data Catalog Vocabulary), and iiRDS (intelligent information Request and Delivery Standard), among others. Our panel “Using Schema and Value Vocabularies to Provide Consistency Across Structured Content” addressed these schema and other data frameworks, which are similar to but not the same as schema, such as OWL and DITA, at the recent DCMI (Dublin Core Metadata Initiative) conference in Barcelona in October.  Other speakers were Joseph Busch, who had the idea of this topic for a conference panel, Lief Erickson, Noz Urbina, and Peter Winstanley.

DCMI 2025 Panel: "Schema and Value Vocabularies for Consistency"

My presentation the DCMI panel, was "Schema and Value Vocabularies for Thesauri and Taxonomies," which explained that SKOS is a schema vocabulary, and specific SKOS-based taxonomies and thesauri are value vocabularies. SKOS (Simple Knowledge Organization System) is the W3C data model schema for knowledge organization systems, especially taxonomies and thesauri. It can also be considered a schema vocabulary, because it has standard elements with defined display names and machine-readable concatenated forms. In fact, the designation “elements” is what is used in the SKOS model. SKOS, however, is a special kind of schema vocabulary, and it’s not a metadata schema. When SKOS-based taxonomies or thesauri serve as the value vocabularies for metadata elements, those metadata elements are managed as specific SKOS Concept Schemes. In a faceted taxonomy, each Concept Scheme serves as a facet.

Taxonomists don’t usually think of vocabularies being classified as either "schema vocabularies" or "value vocabularies." However, as taxonomies have increasingly been integrated with metadata and serve purposes beyond just browsing, searching and retrieving content, it’s important to see the bigger picture of where taxonomies as value vocabularies fit in, and where taxonomies can provide more benefits.