Helen Lippell: Profiles in Knowledge

Originally published April 19, 2024

10 min readApr 20, 2024

This is the 102nd article in the Profiles in Knowledge series featuring thought leaders in knowledge management. Helen Lippell is a taxonomy, search, metadata, and semantic modeling consultant. She says that she helps organizations sort out their messy content and data. Patrick Lambe posted a suggestion that I include Helen in this series, and I am glad to oblige.

She is experienced in designing information architectures, metadata schemas, taxonomies, and semantic models to help organizations fully exploit the value of their content. Helen has designed and implemented taxonomies for websites, intranets, content management systems, enterprise search solutions, and semantic publishing tools. She has tamed unstructured data including financial data, local news, entertainment listings, government information, and operational policing policies.

Helen’s specialties include managing search engines, taxonomy, metadata, content analysis, A-Z indexes, text analysis, search insight, search user experience, findability, information architecture, content audits, categorization, automatic classification, navigation model design, and content modeling. She works on taxonomy development projects, including taxonomy audits, ontology modeling, tagging initiatives, semantic publishing, knowledge graphs, and metadata training. She writes and speaks regularly and is the program chair of Taxonomy Boot Camp London.

Helen lives in London with cats and books. She has been a contestant on quiz shows such as Only Connect and Brain of Britain. She also attempted to visit all 270 London Tube stops in a single day.

Background

Education

Keele University — Bachelor of Arts (BA), Latin and Economics

Experience

Information Today, Inc. — Program Chair, Taxonomy Boot Camp London, 2016 — Present
Electronic Arts (EA) — Taxonomy consultant, 2017–2021
Pearson — Global Taxonomist, 2016–2017
Metropolitan Police — Search Analyst. 2014–2015
Press Association — Strategic Metadata Manager, 2012–2014
Time Out Group — Taxonomy and Metadata Manager, 2011–2012
Directgov — Metadata and Taxonomy Analyst, 2007–2011
BBC — Information Architect, 2004–2007
Financial Times — Index Development Manager, 1998–2003

Profiles

Content

Posts and Articles

The ABCs of the BBC: A Case Study and Checklist

Eight-point checklist for creating terrific A-Z indexes

Know your audience
Show your numbers
Acknowledge articles
Include synonyms
Properly index proper names
Consider your cross-references
Use qualifiers and extra information
Take pride in the index

Articles by Others

A Mastermind of taxonomy by Research Information
Synaptica Insights by Vivs Long-Ferguson

Becoming a Taxonomist: Real Life Stories by Karen Loasby

I started my career without a clear idea of what I wanted to do but was lucky enough to start working life in the Financial Times. One of my very first tasks was to cut out articles from newspapers, stick them on a piece of paper and fax them to clients. I got blisters on my hands from using scissors all day, but it sparked a passion for information and findability, and I’ve never really looked back.

I then moved onto the manual indexing operation, applying index terms from taxonomies onto news articles. The articles came from sources across the world, and I enjoyed the challenge of working out what would be the best index terms to apply. I then moved onto working on the automatic indexing of the content, to provide the scale and consistency that humans couldn’t provide (manual indexers continued to work on high value news sources such as UK newspapers). I also discovered an interest in taxonomies and vowed to pursue that.

Next up was a short contract at the BBC creating a taxonomy of UK place names. The place names were sourced from a third party but the structure and effort to disambiguate them were all my own work, and I had a ball. I enjoyed creating an artefact that was going to be used in a new content management system where metadata was key to its potential.

I returned to the BBC shortly after this, and straight away got to play with a large taxonomy that powered the recommended links system of BBC Search. Again, I enjoyed working with a complex product to try and improve the experience for search users and to ensure that great Web content could be found.

I then went to Directgov, developing taxonomies and metadata to underpin the next phases of the Website, creating intuitive navigation and search, and laying foundations for the challenges of the semantic Web.

I have had strange looks when I tell people I’m a taxonomist, but once we get past the “is it stuffing animals?” conversation, I like to tell them that the job is about problem-solving, product design, user experience, playing with language and many other things. Whatever ups and downs I’ve had, I’ve never been bored by the work I do.

Ontology and Domain Modelling by Conrad Taylor

Taxonomies and ontologies are quite strongly related. The difference, said Helen, is that while taxonomies are concerned with the relationships between the terms used in a domain (also defining which are broader, which narrow, which are equivalent and which are preferred), ontologies focus more on describing the things within the domain, and the relationships between them.

Compared to taxonomies, ontologies aspire to greater rigor in the semantic rules which tie entities together, and to a degree of logical formalism which, ideally, lets machines join us as partners in navigating the web of relationships and meanings, though the construction of ontologically founded Description Logics and the application of machine reasoning which follows those logics.

Taxonomies, ontologies — neither is inherently better: you choose what is appropriate for your business need. An ontology offers greater capabilities, and a gateway to machine reasoning, but if you don’t need those, the extra effort will not be worth it. The two can also be combined, where the ontology gives you the structure and the classes and allows for interoperability with other data sets, and the taxonomy provides the controlled vocabularies which help with navigation and search.

Some definitions

Class: this is a central concept in ontology work. A class groups together a set of things which have properties in common (at least one property, ideally more). Example: the class of ‘domestic cat’ (Felis sylvestris catus) groups together millions of individual cats around the world.

Because an ontology is designed to be functional within a particular domain of practice — it’s a business tool — different groups will define classes differently to match their purposes, even if they describe the same sort of thing. Helen’s example was that an ontology of animals for managing a zoo will require very detailed information on different species of animal. But if you’re running a pet shop, you can get by with a handful of categories of animal.

Subclasses are also recognized in an ontology. (Gerbils and guinea pigs are both rodents, and all rodents are mammals.) But ‘subclassing’ is not a matter of simple mono-hierarchies: cats are mammals, boas are reptiles and owls are birds, but all also belong to the class of obligate carnivores.

Instances is a term used to denote the individual members of a class. (‘Bilbo Baggins’ is an instance of the class of hobbits; The Battle of Lincoln, 1217 is an instance of the class of military conflicts.)

Relation or relationship: this refers to what links the different entities, whether those entities are instances, classes, or concepts. These can be as broad or as detailed as your business purposes demand. Helen confesses that quite often she just makes do with a relationship statement such as ‘is a [member of class]’, or ‘has’:

guinea pig [is a member of class] rodent
Charlie [is a member of class] guinea pig
Guinea pig [has] fur

strong>Inverse relationships are those which can be read in both directions (‘Patty is the sister of Suzy’). Other relationships are reversible but with inversely matching terms: parent and child for example. And if the aim is to support machine reasoning such as in search and discovery, the ‘stupidness’ of machines may require any derived description logics to have more elaborate rules e.g. HasUncle = HasParent (who) HasBrother.

Attributes: these are properties of the entities, or indeed properties of the relationships (because relationships can also be treated as entities). Attributes might record features of the entity, characteristics, or parameters such as permissible data type (an example of a data type parameter for a credit card number is that it must consist of 16 digits). One attribute could be cardinality, where you specify whether an entity can be linked to multiple values, or there must be only one (a place might have a Welsh and an English name but can have only one OS map grid reference).

What are ontologies for?

Helen listed a number of business scenarios in which ontologies can be helpful — information retrieval, classification, tagging, data manipulation. She is doing a lot of work currently on an ontology that will help in content aggregation and filtering, automating a lot of processes which are currently manual/mental.

Some use cases from Helen’s career

A London listings magazine wanted to automate restaurant recommendations within an online app; the project created an ontology and linked algorithms. The criteria for recommendations took into account proximity, plus what kind of food, atmosphere, price range and so on the user preferred. Concepts were also linked in such a way that, for example, if someone had searched for Vietnamese restaurants, then Korean and other East Asian cuisines might additionally be suggested.
An educational publisher is seeking to move away from the old textbook model for managing and presenting its content. At the same time, they want to get rid of a plethora of earlier-generation content management systems. This ongoing project aims to achieve a single content management platform, linked to a single ontology. This is helping to establish, for example, strict definitions for parts of books and parts of courses; outsourced digital assets such as stock photography; arrangements for content re-use; and associated rights management issues.
A news agency wanted to get journalists to populate fields of metadata relating to the news stories it was curating — but with a user interface that would hide the complexity of what lay beneath! Helen worked on the UI aspect of the system, which incorporated Linked Data features. For example, if a journalist noted that a story had something to do with Doug Engelbart, the system would retrieve structured information about him from DBpedia, and also search across the agency’s own multimedia databases to pull in links to images, video etc.
A video game producer surprisingly took up ontologies big time. Their ontology is around the domain of the video game: for a given title, it applies a model about which platforms it runs on, if it is part of a larger franchise, if there are special editions, who are the characters, what weapons you can buy ‘in game’, and so on. Their aim is to drive personalized content, and to intervene with pop-up suggestions: if you are stuck on a level, the game could suggest a weapon or tool you could buy to get you further on. Ultimately, of course, the aim is to make money from the gamers.