A model is supposed to be a simplification of reality.It is meant to help people understand and act.But sometimes models do the opposite and cause confusion.If the model becomes an artifact of its own, it can be hard to see its connection to what it is supposed to represent.Over recent months, several people have raised questions on Twitter about the relationship between a domain model, a content model, and a data model.We also may also encounter the terms ontology or vocabulary, which are also models. With so many models out there, it’s small wonder that people might be confused.
From what I can see, no consensus yet exists on how to blend these perspectives in a way that’s both flexible and easy to understand.I want to offer my thinking about how these different models are related.
Part of the source of confusion is that all these models were developed by different parties to solve different problems. Only recently has the content strategy community started to focus on how integrate the different perspectives offered by these models.
The Different Purposes of Models
Domain models have a geeky pedigree.They come from a software development approach known as domain-driven design (DDD).DDD is an approach to developing applications rather than to publishing content.It’s focused on tasks that a software application must support (behavior), and maps tasks to domain objects that are centered on entities (data).The notion of a domain model was subsequently adopted by ontology engineers (people who design models of information.)Again, these ontology engineers weren’t focused on the needs of web publishers: they just wanted a way to define the relationship between different kinds of information to allow the information to be queried. From these highly technical origins, domain models attracted attention in the content strategy community as a tool to model the relationships of entities that will appear in one’s content.The critical question is, so what?What value does a domain model offer to online publishers?This question can elicit different and sometimes fuzzy answers.I’ll offer my perspective in a moment.
A content model sounds similar to a domain model, but the two are different.A content model is an abstract picture of the elements that content creators must create, which are managed by a CMS.When content strategists talk about structuring content, they are generally referring to the elements that comprise a content model.Where a domain model is concerned with data or facts, a content model is concerned with expressive content — the text, images, videos and other material that audiences consume.Compared with a domain model, a content model is more focused on the experience of audiences.Unsurprisingly, content strategists talk about content models more than they talk about domain models.
Content models can serve two roles: representing what the audience is interested in consuming, and representing how that content is managed. The content model can become confusing when it tries to indicate both what the machine delivering content needs to know about, as well as what the audience needs to see.
Regrettably, the design of CMSs has trained authors to think about content elements in a certain way.Authors decompose text articles into chunks, presented as fields in a CMS.The content model can start to look like a massive form, with many fields available to address different aspects of a topic or theme.Not all fields will display in all scenarios, and fields may be shared across different views of content (hence rules are needed to direct what’s shown when). It may look like a data model.But the content model doesn’t impose strict rules about what types of values are allowed for the fields.The values of some fields are numbers, some are pick list values.Many fields are multiple paragraphs of text representing thousands of characters.Some fields are links to images, audio, or to videos.Some fields may involve values that are phrases, such as the text used on a button.While all these values are “data” in the sense of being ones and zeros, they don’t add up to a robust data model.That’s one reason that many developers consider content as unstructured — the values of content defy any uniformity.
A content model is not a solid foundation for a data model about the content. The structure represented in a content model is not semantic (machine intelligible) — contrary to the beliefs of many content strategists.Creating a content model doesn’t make the content semantic. Structured authoring helps authors plan how different pieces of content can fit together. But author-defined structures don’t mean anything to outside parties, and most machines won’t automatically understand what the chunks of content mean.A content model can inform a schematic of the content’s architecture, such as what content is needed and from where it will be sourced (it could come from other systems, or even external sources).That’s useful for internal purposes.The content model is implemented with custom code.
The primary value of content models is to guide editorial decisions.The content model defines content types — distinct profiles of content that address specific user purposes and goals.A content model can specify many details, such as a short and a long description to accommodate different kinds of devices, or alternative text for different audiences in different regions. A detailed content model can help the content adapt to different contexts.
Domain models are strong where content models are weak. Although domain models did not originally rely on metadata standards (e.g., in DDD), domain models increasingly have become synonymous with metadata vocabularies or ontologies.Domain models define data models: how factual information is stored so it can be accessed. They supply one source of truth for information, in contrast to the many expressive variations represented in a content model.Domain models represent the relationships of the data or information relating to a domain or broad subject area.Domain models can be precise about the kinds of values expected.Precise values are required in order to allow the information to be understood and reused in different contexts by different machines.Because a domain model is based on metadata standards, the information can be used by different parties.Content defined by a content model, in contrast, is primarily of use to the publisher only.
The core value of a domain model is to represent entities — the key things discussed in content.Metadata vocabularies define entity types that provide properties for all the important values that would provide important information.Some entity types will reference other entity types.For example, an event (entity type 1) takes place at a location (entity type 2).The relationships between different entities are already defined by the vocabulary, which reduces the need for the publisher to set up special rules defining these relationships.The domain model can suggest the kinds of information that authors need to include in content delivered to audiences.In addition, the domain model can also support non-editorial uses of the information.For example, it can provide information to a functional app on a smartphone.Or it can provide factual information to bots or to search engines.
The Boundary between Domain and Content Models
What’s the boundary between a domain model and a content model?
A common issue I’ve noticed is that model makers try to use a content type to represent an entity type. Certain CMSs aren’t too clear about the difference between content types and entity types.One must be careful not to let your CMS force you to think in certain ways.
Let’s consider a common topic: events.Some content strategists consider events as a distinct content type.That would seem to imply the content model manages all the information relating to events. But an event is actually an entity type.Metadata standards already define all the common properties associated with an event.There’s little point replicating that information in the content model.The event information may need to travel to many places: to a calendar on someone’s phone, in search results, as well as on the publisher’s website which has a special webpage for events.But how the publisher wants to promote the event could still be productively represented in the content model.The publisher needs to think about editorial elements associated with the event, such as images and calls-to-action.
Event content contains both structured editorial content, as well as structured metadata
The domain model represents what something is, while the content model can represent what is said or how it is said.Let’s return to the all important call-to-action (CTA).A CTA is a user action that is monitored in analytics.The action itself can be represented as metadata — for example, there is a “buy action” in schema.org.Publishers can use metadata to track what products are being bought according to the product’s properties, for example, color.But the text on the buy button is part of the content model.The CTA phrasing can be reused on different buttons.The value of the content model is to facilitate the reuse of expressive content rather than the reuse of information.Content models will change, as different elements gain or lose their mojo when presented to audiences.The elements in a content model can be tested.The domain model, centered on factual information, is far more stable.The values may change, but the entities and properties in the model will rarely change.
When information is structured semantically with metadata standards, a database designed around a domain model can populate information used in content.In such cases, the domain model supports the content model.But in other cases, authors will be creating loosely structured information, such as long narrative texts that discuss information.In these cases, authors can annotate the text to capture the core facts that should be included.The annotation allows these facts to be reused later for different contexts.
Over time, more editorial components are becoming formalized as structured data defined by metadata vocabulary standards.As different publishers face similar needs and borrow from each others’ approaches, the element in the content model becomes a design pattern that’s widely used, and therefore a candidate for standardization.For example, simple how-to instructions can be specified using metadata standards.
The Layered Cake
How domain models can support content models
One simple way to think about the two models is as layers of a cake.The domain model is the base layer.It manages the factual information that’s needed by the content and by machines for applications.The content model is the layer above the domain model.It manages all the relevant content assets (thumbnails, video trailers, diagrams, etc), all the sections of copy (introductions, call outs, quotes, sidebars, etc.) and all the messaging (button text, alternative headlines, etc.)On the top of these layers is the icing on the cake: the presentation layer.The presentation layer is not about the raw ingredients, or how the ingredients are cooked.It’s about how the finished product looks.
The distinctions I’ve made between the domain model and content model may not align with how your content management systems are set up.But such decoupling of data and content is becoming more common. If factual information is kept separate from expressive content, publishers can gain more flexibility when configuring how they deliver content and information to audiences.
Suppose your organization wants to become the preeminent source of information about a topic. It aims to give audiences the ability to look at any dimension of a topic they might be interested in. How would you offer this?
To deliver informationally rich content, numerous content items need to be associated to one another. Content needs to be modular, with components that work together. But how do these things relate to each other? Where does one start?
Content models define how units of content should interact. Content modelling can be difficult to grasp and practice, partly because it is not a single uniform method. It encompasses a spectrum of related approaches that can be adapted to different needs.
People sometimes start to model their content before they know all the content they really need. They focus on what content has been already created, and not explore what content is not yet available that might be of interests to users.
Content models are often more robust when they are backed by a domain model. A domain model enables content designers to untangle a messy topic and explore and define requirements and design solutions.
The role of content modelling
A content model is the end goal of a domain model. Rachel Lovinger has been instrumental in developing and advocating the practice of content modelling, so I will rely on her definition. She states: “A content model documents all the different types of content you will have for a given project. It contains detailed definitions of each content type’s elements and their relationships to each other.” She recommends using content models to bridge perspectives on a team.
“A content model helps clarify requirements and encourages collaboration between the designers, the developers creating the CMS, and the content creators.” — Rachel Lovinger
In addition to facilitating project delivery, content models improve how content is delivered to audiences. Content models can enable personalization, adaptive content, and content APIs. Cleve Gibbon, a collaborator of Rachel Lovinger in evangelizing content models, notes: “Great APIs are founded upon solid models. So if you’re building a Content API, be sure to create a content model FIRST that conveys the required level of structure and meaning.”
The spectrum of content modelling
Models can represent different dimensions of a topic: either conceptual, or formal and structural. Content models can indicate how to assemble content components. But first one needs to know how solidly your content types are defined.
On one end of the spectrum, you may have well defined, fixed content. In such cases, one can develop what Deane Barker calls a relational content model. He defines it as “the concept of how different, separately-managed pieces of content relate to each other. (This is distinct from ‘discrete content modeling,’ which is how you structure a single piece of content.” He explains the goal as “the idea of taking multiple discrete content objects (articles, sections, issues) and ‘rolling them up’ into a more complex content object (publication).”
On the other end of the spectrum, you may have fluid content, where the exact requirements are still emerging and many different hubs of content are possible. In such cases, a domain focused, ontology based form of modelling can be helpful. This approach has been used by the BBC for several large projects. Mike Atherton emphasizes the importance of the domain of the topic in content models: “A content model maps our subject domain, not our website structure.” He advises: “Concentrate on modelling real (physical and metaphysical) things not web pages.”
One way to consider the differences in a content model and a domain model is the metadata they emphasize. Rachel Lovinger states: “The Content Model is primarily concerned with structural metadata, while the Domain Model is largely concerned with descriptive metadata.”
A domain model and content model are complementary. A domain model helps you describe things that will be represented by content, while the content model helps you structure the content. Using both allows you to understand the relationship of a real world entity with a content entity.
A domain model is a useful place to start when content does not yet exist, or one is looking for a fresh redesign of content. Domain models may be considered as the prequel to content models. By focusing on entities in the real world, and the relationships between these entities, one can see opportunities to develop content associated with these entities, and what elements would be needed for that content. The correspondence of domain entity type, and content type, is illustrated in the table.
The relationship between a domain model and content model
Domain models in the real world: Italian wine
Domain models can clarify one’s understanding of a topic, and offer insights into how different items of information relate to each other. Domain modelling emerged as strategy in software development to bridge analysis and design of complex business domains by using a shared verbal and visual language between experts, endusers and developers. Domain models can be especially useful for complicated and messy topics. They would seem perfect for understanding Italian wine.
When you live in Italy, as I do, understanding Italian wine is a practical problem. Wine is ubiquitous, but understanding Italian wine is not self-evident. Walk into an Italian wine store and you are confronted with walls of bottles whose contents are largely unrecognizable. It’s not that all wine is difficult to understand. When I lived in New Zealand, I had a good idea what different wines were about. It’s Italian wine that is the challenge.
The famous wine critic Hugh Johnson once wrote: “the already bewildering complexity of Italian wines has become tangled enough to drive a critic to drink.” Italian wine is particularly hard to understand because of its heterogeneity. Even the imposition of standardized nomenclature to designate where a wine is from results in a bewildering array of non-standard implementations of these standards. Idiosyncratic traditions, politics, and rogue approaches mean that wines are described in great detail, but in richly differing ways.
At the core of why Italian wine is difficult to decipher is its product architecture: how specific wines are labelled. Consumers need an easy way to know the basic characteristics of a wine based on its label. Do consumers think of wine in terms of where it’s from (Burgundy) or what grapes it is made from (Chardonnay)?[1] High volume wine producers have attempted to solve the product architecture problem by promoting brand awareness of a grape variety or a region. What happens when consumers are not familiar with either the origin name or the grape name?
Unfortunately, Italian wine labels are uncharacteristically difficult to decipher. Italian labels will show the producer + (grape variety and/or geographic indication) + year. That seems reasonable enough, until the consumer realizes that the only items on the label they might recognize are the digits of the year. Even if they have familiarity with another proper name on the label, that is not sufficient to make a selection decision.
The most significant piece of information about the kind of wine is indicated by the grape variety and/or the geographic indication (a regional designation similar to an appellation in France). Between these two items, there are nearly 1000 different varietals and zones that indicate the basic composition of the wine. [2] To get a sense of how good the wine is, the most reliable information is the producer, and the year of vintage. Yet there are many thousands of wine producers in Italy of varying abilities, and the correlation of product quality to year of vintage is very specific to the variety of wine and where it was produced.
The complexity of Italian wine would seem tailored for digital content. But existing digital-only information sources on the web tend to be shallow — both in terms of their range of attributes, and their selective coverage.
Good information about wines, producers, and regions are available from several well known printed guides, such as those by L’Espresso, Gambero Rosso, Touring Club, Bibenda, and Slow Food. Despite the editorial quality of the content, the information is not as usable as it could be. Depending on the specific organization of the book, the information is stovepiped in one way or another. The editors of each guide assumes a fixed path of entry that generally leads to a producer profile. Users are expected to think like the editors to uncover information of interest to them.
In some cases there are iPad versions of these printed guides, but they don’t feel natively digital, and require lots of tapping to move around from screen to screen. They are less usable than the print version, because they are slower to move through, and one’s orientation can get lost when hopping between screens. The content, while structured editorially, is not structured digitally with digital metadata. There is no ability to move laterally through the content: navigation is hierarchical. Unfortunately shovelware that ports a printed product and dumps it into a tablet format is too common, due to the false promises embedded in Adobe InDesign.
What users need is not simply a catalog of items, but a way to make sense of the bigger picture, in addition to exploring the detail. The heavy focus on profiles means that the user doesn’t see easily how these items relate to other things. They also miss seeing collective behaviors of similar items, which is possible when one digitally aggregates items sharing the same metadata. Thinking through these relationships and behaviors is one benefit of domain modelling.
Understanding the domain
How do people think about a subject? Mike Atherton suggests: “Experts map the world, users mark points of interest.” It helps to know how experts think about a topic like wine, and then during design, figure out what more typical users consider high priority goals. What aspects of wine do people consider significant? How might different aspects be pulled together into interesting items of content?
The topic of wine is distinctive because many people want to become experts, in contrast to other products. Getting information about the product is rarely a perfunctory task, but a connoisseurial pastime. Some people want to develop a broad knowledge about all styles of wine, while other people want to have a deep knowledge about a few specific producers or product areas, perhaps tied to places they go on holiday. Many things people might be interested in are non-obvious. For example, soil characteristics can influence how a grape variety tastes. Others may be interested in the environmental credentials of a producer.
How to break things down so they can be managed
The most important task when developing a domain model is to identify appropriate entities. An entity is a thing, either tangible or conceptual, with a distinct identity. It’s not the same as an existing item of content — the content may not exist yet. Entities, to use the words of Cleve Gibbon, are “first class citizens in the business domain” — they are the actors in the drama on the stage.
Entities have attributes — characteristics. Attributes do not necessarily become a field in the content, but they often do. That decision needs to be made when the content is designed. Taste is certainly an attribute of wine, but is not necessarily a field in a description of a wine.
Once entities have been identified, it is necessary to determine where to put attributes, and whether to break entities into smaller units. Often, one discovers intermediate zones that straddle two entities. The horticultural characteristics of the vineyard reflect the interaction of the producer and the wine produced. The interplay between region and varietal defines the vintage for a given year. These intermediate areas may not deserve to be entities themselves, but one should consider how to make sure their role remains visible.
What a domain model for Italian wine looks like
It is helpful to first consider the relationships between entities, then examine the attributes associated with each entity.
When looking at entities, two things are important. First, how many instances are there for each entity type? The entity map shows that most of the entities, there are hundreds or even thousands of instances. This large number suggests that establishing meaningful relationships between entities will be important if users are to be successful navigating through such a large volume of content. Second, what is be essential character of relationships between entities? We want to know how many connections there are between entities: the more connections to other entities, the richer the potential interaction of information. We also want to know if the relationship between entities is a one-to-one relationship, a one-to-many relationship, or a many-to-many relationship. The “crow’s feet” in our entity map indicates numerous many-to-many relationships. That may make the design of content a bit more challenging, but it also indicates many interesting connections. Our content is a valuable resource when it’s not easy to see these connections in one’s head.
Relationships between different entity types associated with the domain of Italian wine.
Next, explore the attributes associated with each entity. The goal is to identify and associate attributes of entities. Each entity has a number of attributes. Some will be short fields, others will involve longer text descriptions. There is no right number of attributes, provided all attributes are meaningful. The number of attributes to implement in design will depend on both business and design decisions. There will be a business decision concerning the cost of acquiring the information related to the attribute, and the usefulness to consumers of that information. There will also be a design decision relating to which attributes to expose to which audiences.
Typical attributes of each entity type relating to domain of Italian wine
Our model shows attributes that are commonly associated with the domain of Italian wine. For example, it can be interesting to know the number of bottles produced of a wine. That can indicate how widely available the wine is to buy, or perhaps its scarcity (that one needs to reserve purchase). Some wine guides will indicate the total number of bottles according to producer, while others will indicate total number of bottles by label. This difference means that one can answer different questions, such as who is the largest producer within a geographic indication zone, or who is the largest producer of a specific kind of wine. Ideally, one would like data at both the producer and product levels, but that may not be easy to obtain for all producers.
Lessons from domain modelling
Even though domain modelling attempts to represent the real world, reality is often less orderly than we would like it to be.
Not everything can be easily expressed as a regularized attribute. Audiences will want to know: What does the wine taste like? It would be wonderful to provide a reliable, easy-to-understand way to explain taste that allows easy comparison between wines, zones, and producers. Sadly, taste is — surprise — a bit subjective. Different experts will say different things about the same wine, even when they agree on an overall judgment. Terminology is not standard either. The same words can mean different things. Critics may use the word “cherry” to describe a taste as “spicy black cherry” or as “cherry rhubarb.” There is no controlled vocabulary for wine, no limited set of descriptors with precisely defined and agreed meanings.
Map of geographic designation zones within a single region. Screenshot from a certification body website.
By their nature, models simplify reality. The geographic indication signifies where a wine is made, and the criteria by which it is made. Whereas most geographical entities are based on either political administrative geography or physical geography, geographic indications exist outside these frameworks. A geographic indication can straddle two administrative regions. It can exist in two different, discontinuous locations. Some geographic indication zones have subzones. Wine producers also can behave in complex ways. Sometimes a wine producer is a brand “house” that has vineyards in several locations, or a consortium that sources from different vineyards. The informational details associated with these exceptions may not be important to users, and can add design complexity.
The identity of items can be constructed in several ways. One needs to be able to distinguish one entity from others belonging to the same entity type — items need to be uniquely identified. Despite the challenges of deciphering Italian wine, specific entities fortunately are identified with meaningful, human readable names, rather than numeric product codes. The domain model can use existing identifiers, which are based on several approaches:
Collectively defined names (the names of regions, geographic indications, and grape varieties), though some producers use alternate names for grape varieties.
Self described (the name of producer), though sometimes producers choose to use both a house and proprietor name
Inherited identity (the environmental profile for a producer)
Names composed of compound attributes , such as dry sparkling rosato as a wine category entity.
Thinking about design
The domain model can support early design discussions. Many questions that are interesting to audiences will span two or more different entities. For example:
What year produced the best wine from a region?
What geographic indication commands the highest average prices?
What grape varieties produce the most wine?
What wines for a given year and geographic designation are ready to drink?
Some answers require computations of structured data. Questions of interest to audiences need to be translated into content types that will be represented in the content model.
In addition to supporting interesting exploration, the design needs to support common tasks. The domain model helps to identify information available to support common tasks. Some common points of entry audiences will seek when exploring wine include:
By rating
By price
By category
By variety
Users often focus on one specific criteria when starting the process of seeking information. In some cases, these are entities, in others, these are attributes. Considering task starting points can help identify potential groupings of content elements. Depending on the depth of content, these groupings may not be manageable for users without providing additional parameters to narrow the pool of candidate content. The most salient criteria is not the only factor that’s important to the user.
In contrast to starting points, another perspective is to consider the end goal of the task. Examining the end goal, the content designer can consider the orientation of different users. Users of wine information may be:
Bottle centric — interested in the characteristics of specific bottles of wine
Producer centric — interested in the story of the producer, perhaps with an intention of visiting them
Food centric — mostly interested in wine styles as a complement to food dishes.
Domain depth and domain scope
The depth of a domain reflects both the number of attributes for an entity type, and quantity of items. Both aspects can impact the design. The quantity of items will influence content types that presents lists and links. The number of attributes will impact content type structures for content items.
Content designers decide how much of the domain model to present to users. A fixed content type may show all attributes as part of in content type. With a flexible content type, attributes may be optionally available, or have serval variations. Designers may choose progressive disclosure of content that hides details, which are revealed only when wanted. Or they may implement an adaptive approach, where different variations of content types are shown depending on the interests of an audience segment, or device formats.
The other aspect of the domain model, thus far unmentioned, is how it might connect with other domains. The domain model offers the possibility of enlarging the scope addressed by considering related domains. Different variations of content may draw on common content, while including different content as well (see diagram). Three different apps may share common core content. But they provide different functionality depending on their focus (touring vineyards, pairing wine with food, or knowledge enhancement of wine). The domain model can also be used to guide the planning of releases of content and functionality.
Relationship between the depth of a domain, and its scope. Content can be deep, covering many attributes. And content can we wide, connecting with other domains.
Relating entities: Comparisons to other approaches
Domain modelling is not the only approach to sorting through complex content. Before closing this discussion, it is worth talking about two other well known approaches that look and behavior similarly, but have some differences.
Faceted search, an approach popular in library science and information architecture, allows users to locate specific content by filtering on facets. Facets can be attributes or entities. The idea is that users can locate content that has the qualities of A & B & C. Faceted search is a popular technique, common on ecommerce sites, and is often helpful. The utility of the technique rests on several assumptions. First, faceted search assumes users know the two to four most important criteria, and will get a manageable set of results. If the set of results is large, users generally take a satisficing approach, happy with the first result encountered that is minimally acceptable. Second, faceted search presumes that each facet is independent of each other, which in the case of wine isn’t true. It is possible to get null sets if facets aren’t deep. While faceted search has been implemented on some wine ecommerce sites, it is not an effective approach for helping users discover content they might be interested in but not know about, and tends to focus on a limited range of aspects.
Linked data is an approach to modelling content that has close associations to domain modelling, thanks to the BBC’s integration of the two approaches. To simplify, linked data allows users to find content with characteristic A that has B, which has C. Organizing content using a linked data approach has both benefits and drawbacks. One drawback is that queries can be path dependent. Whether results appear promising or discouraging depends on how you construct the query. Linked data queries are generally more open ended than predefined structured queries that answer fixed questions with predictable sets of results. A bigger concern is that linked data treats all aspects of an entity as other entities, and each entity gets its own page. But not all attributes are meaningful entities — things worthy of their own content destination. On the positive side, linked data is good for what-else questions. One can link outside of a domain to other domains, such as to geophysical data.
Model behavior
Models aren’t reality, according to the cliche. Domain models may appear esoteric to some people, given that they aren’t actually something implemented directly, but are an input to other deliverables. To get buy-in for domain models, it may be best to use it as a discussion document, and note that it will evolve into the content model. While it l lacks the appeal of being code-ready, a domain model can play an important role on a project. It can uncover hidden requirements and opportunities, help align different stakeholders around a common vision, and accelerate the design process.
— Michael Andrews
Chardonnay grapes originated in Burgundy. Even though most people associate Burgundy with red wine, there are also white Burgundy wines made from Chardonnay. ↩
