How can we organize the taxonomy of a website to make it easier to find in search engines? What can we do with entities and what can we expect when we annotate content with them? This is the story of William Green, who founded Poem Analysis in 2016 with the goal of giving poetry a home online. William had noticed that there were not enough online resources about poetry and that people were still struggling to understand and appreciate it because they were not being helped to study it in depth.
The first important step was to create a well-organized website. But, as we know, a website without visibility is like an empty house. So, the biggest challenge at that time was to gain more and more visibility on Google and the search engines. So while a website must be designed for the user, the content that fills it must also be understandable to search engines. In this way, Google can index it and the website can get a good ranking that will bring it more visitors.
When William came across WordLift, he knew there was untapped potential here. He knew Schema.org and had already tried solutions that allowed him to annotate content and add structured data to the website, but he had not yet found the solution that could really make a difference. Let’s see closer this SEO case study.
WordLift brought its innovation, both in approach and technology.
“The ability to add entities on a scale like no other is something that every website owner should get excited about. Helping Google understand content better, and make the links, will only benefit the website, where schema is becoming a more dominant force for search engines, and into the future.
It was and is a pleasure to work with WordLift on cutting-edge SEO, particularly with such a quick thinking an Agile team. In doing so, we are able to test and create experiments that produce incredible results before most have even read new SEO schemas”
William Green – Poem Analysis
The challenge
The goal of Poem Analysis was to achieve better ranking on Google and search engines and get more organic traffic to the website. The real challenge was to find a solution that would scale quickly and systematically given the large amount of content on the site.
It was also about increasing relevancy, making sure that Google was able to capture the right queries. It seems obvious at the beginning but, really, it is not when you look at each individual poem. Google might know who Allen Ginsberg is but might struggle to connect ‘Howl’ (one of Allen’s poems) to him.
The solution
For PoemAnalysis.com we used a version of WordLift specifically created for publishers in the CafeMedia/AdThrivenetwork.
The solution proposed by the WordLift team started from the idea of using the pre-existing taxonomyand tointegrate and inject structured data through “match terms”. This means that you can enrich your site by using categories and tags without having to use WordLift’s content classification panel to add markup. This way, a tag or category is treated like a recognized entity in the content classification box.
For PoemAnalysis.com we created a custom solution to achieve this goal and in this way, we used the well organized taxonomy of the websites to associate the entity correspondent to the category of the poet (e.i. The category of William Shakespeare has been associated with the Wikidata correspondent entity). Specifically, this was done here with Authors.
In a second testing phase, we decided to add the SameAs of the poem to the page of the poem (e.i. Shakespeare’s Sonnet 19 associated with the correspondent wikidata and DBPedia). The items we marked in the SameAs field are converted by WordLift into meaningful links to Wikidata and DBpedia. Subsequently, the Poem Analysis editorial team added SameAs to all entities on the site.
We are now working to add “Quiz” markup for Education Q&A, a format that helps students better find answers to educational questions. With structured data, the content is eligible to appear in the Education Q&A carousel in Google Search, Google Assistant, and Google Lens results. Stay tuned!
The results
As we saw in the previous section, the WordLift team worked on two actions that had a positive and measurable impact on Poem Analysis’ SEO strategy.
In the first case, the existing, well-organized taxonomy was used with the “Match Terms” feature to convert categories into entities, which were then annotated in the site content. To measure the impact of this first experiment, a control group was created that included a set of unannotated Poets with WordLift markup.
The semantic annotation brought +13% of clicks and +29.3% of impressions when comparing this year to last year. In this case, we did not include the control group, because after we did follow-ups for these URLs as well, the data is not statistically relevant at the moment (in the expansion of the experiment we moved most of the URLs of the control group into the variant group, so to date, only 5 URLs remain within the control group).
In the second case, the WordLift team worked on SameAs of the poem to the page of the Poem. Again, to measure impact, we created a control group containing a set of Poems not annotated. Using SameAS brought +59.3% of clicks and +82.9% of impressions.
The semantic annotation of the content also allowed another result: if you type the whole poem in Google, the first result that appears is poem analysis🤩
Example: William Shakespeare’s Sonnet 19
Conclusion
The story of William shows the impact of semantic annotation and how building a well-organized taxonomy helps semantic annotation of content have a positive impact on a website’s impressions and traffic.
In this case, we used the well-structured and indexed tags and categories as entities to annotate the website content. Generally, it is important that the entities are relevant and indexed by search engines to create the right sidewalk that moves both Google and the user.
Let us not forget that semantic content annotations add value to the user by providing them with information and insights that they would otherwise have to look for elsewhere. And, of course, it also creates a semantic path for Google, which can thus more easily assemble the concepts surrounding a piece of content and rank it by reinforcing the topical authority on that topic.
The New Customer Journey Paradigm And The Messy Middle
“People don’t make decisions in a neat, linear fashion. A lot happens between the moment they realize they have a need or a desire for something and the moment they make a purchase”
Rennie and Protheroe – Google’s consumer insights team.
During the time of the coronavirus, the percentage of online purchases has increased to record levels. And while the majority of purchases are still made offline, the media and information influencing those purchases are increasingly online, and the complexity of potential decision paths has increased dramatically.
How do consumers decide what to buy and from whom to buy it? Because of the wide variety and availability of information and products on the web, the user may make several visits before actually buying anything. He goes in and out of different stores, comes back to look a second time, and then a third time to take full advantage of the Internet. That’s the reality of Internet shopping today.
To understand how Internet users interpret and manage the increasing information and choices available for online and offline purchases, Google researchers identified the messy middle, a specific area within the maze of searches, ads, links and clicks that play a role in purchasing.
In this model, between the two poles of trigger and purchase lies an intermediate stage in which consumers move between exploring and evaluating available options until they are ready to buy. This process takes place against the backdrop of exposure, which represents all the buyer’s thoughts, feelings, and perceptions about categories, brands, products, and retailers. The purchase is followed by the experience with the brand and the product, which feeds into the sum of the display. You must learn to navigate this maze to develop an effective marketing strategy to grow your business and outperform your competition.
Messy Middle And Search Intent In SEO
Understanding the messy middle is an essential step in determining an effective marketing strategy. But it’s not enough. In SEO, the goal is to give visibility to content by appearing among the first search results in Google’s SERP. To achieve this, it is first important to know what the user’s search intent is when they are on the path to purchase. Starting with the messy middle, we have analyzed the search intent in each of the phases that make up this new model.
In particular, we found that in the exploration phase, the predominant search intentions are informational and navigational. Namely, the user is looking for information about the “what”, “how”, and “who”. In the evaluation phase, the predominant search intentions are commercial and transactional. In this second part of their journey, the user is getting closer to buying and is therefore looking for information about the “best” and the best offers for that particular product or service.
Based on this analysis, I’ll show you an example of SEO content optimization and explain how you too can use semantic keyword research to create content that ranks better on Google, generating more conversions and therefore sales!
How To Rank On The First Page Of Google With Semantic Keyword Research
In this case, I started with the search query I wanted to cover and for which I wanted my content to appear in Google’s SERP. By analyzing the search intent and the messy middle, I was able to determine the user’s position within the customer journey.
As you can see, we are in the evaluation phase: the user wants to understand what the best solution is for their needs. In this case, that need means he’s looking for the best plug-in for SEO in WooCommerce. So we are in the commercial search intent. This gives me a clear indication of what kind of content I need to create.
To optimize my content in terms of SEO, I performed semantic keyword research. For this I used the SEO Add-on for Google Sheets™ by WordLift. Let us discuss in detail how I used it and what I could do with it for SEO content optimization.
How I used the SEO Add-on by WordLift
SEO Add-on for Google Sheets™ by WordLift is the extension that lets you perform semantic keyword research and create a JSON-LD to help Google understand what your content is about.
In a few simple steps, you can install the extension and analyze the SERP to find out which search queries rank higher on Google. I select the search queries for which I want to rank and run the analysis to see which one ranks better on Google.
Then I went to see the SERP analysis. Here are two important things that the SEO add-on helps me understand and take action to optimize my content.
First, it allows me to identify entities that are already in my vocabulary. In this case, to improve my SEO content optimization, I can insert links from these entities to my content in a way that creates a semantic path for Google to better index my article and thus gain more visibility. It is important that the entities from which I create internal links to my content are relevant and already indexed by the search engine, in order to create a cluster of relevant links around the topic I am talking about and strengthen my topical authority.
On the other hand, it helps me identify new relevant entities that are not yet in my vocabulary. In this case, I can add the entities to the vocabulary, paraphrase the content by adding a mention of these entities, and add the annotation.
What result did I get with semantic keyword analysis and SEO Add-on by WordLift?
My content ranks first in the Google SERP with Top Rich Snippets. This ranking has had a positive impact on conversions: in the weeks following the article’s publication, sales of our WooCommerce plugin increased!
What Results Can You Get With Semantic Keyword Research?
As you have seen, this is an example of entity based SEO. It shows that semantic keyword research and analysis can help us optimize our content, both new content and content we have already published. Here is how you can achieve the following results:
Optimize content, both new and previously published
Improve user experience and engagement
Rank better on Google
Increase organic traffic to your website
Increase conversions.
Start performing Semantic Keyword Research with the New SEO Add-on for Google Sheets™
Peak of impressions in one day with Google Discover
Espiritismo.tv is a Brazilian platform for spiritual videos and lectures. The editorial project is aimed to provide a complete television channel with a range of insights, seminars and readings focused on the world of spirituality and inner growth. Let’s find out more about this SEO case study.
The Challenge
Alex Rodrigues, the SEO consultant for Espiritismo.tv, had three goals when decided to use WordLift for this project:
Increase the website visibility and getting good rankings on the search results.
Bring more visitors to the website.
Increase the time spent by viewers on the website pages.
The Solution
Alex Rodrigues and the team of Espiritismo.tv started using WordLift to create their own vocabulary, enriching the taxonomy of the website with entities linked to Spiritism and revolve around this subject. The creation of Espiritismo.tv’s vocabulary, as well as the strategic enrichment (and marking) with the quality content of each of the entities created, has been at the heart of the strategy for almost a year.
Espiritismo.tv’s growth with WordLift. Image courtesy of Alex Rodrigues, the SEO consultant for Espiritismo.tv.
The Results
After one year using WordLift, the number of website users has increased by 198%, almost tripling the average accesses to the website. Moreover, using WordLift, Alex and his team were able to get 3 videos from the site featured on Google Discover, which earned them 80,000 impressions in two days.
Espiritismo.tv’s sharp growth on their videos on Google Discover, using WordLift. Image courtesy of Alex Rodrigues, the SEO consultant for Espiritismo.tv.
The users’ engagement grew consequentially: page views per session have increased 68,68% (from an average of 25,338 to an average of 42,741).
To learn more about semantics applied to SEO and the Espiritismo.tv experience, check out Alex’s presentation.
Ramia Marielle El Agamy has dedicated her professional journey to her family’s business. Through activities in education solutions, publishing, content marketing and family business networking the El Agamy family is growing its companies between Europe and the Middle East. Ambitious, business-focused, and charismatic, Ramia is the consummate modern leader.
Ten years ago she co-founded Tharawat Magazine, a quarterly family business magazine, with her family. In 2015, the editorial project went online with the aim of inspiring family business owners and entrepreneurs globally. Two years later, she added a new branch to her business with Orbis Terra Media, a content studio enabling brands to achieve narrative consistency across multiple platforms to reach their audience.
Back in 2017, Ramia was looking for a solution to reach a wider online audience to establish Tharawat Magazine as the preeminent publisher on family business topics.
That’s when we met. Tharawat Magazine’s team wanted to introduce an SEO approach in their editorial workflow.
She had a conversation with our business developer Gennaro — at first, she was sceptical. She tried WordLift, however, and after using it for a few months, she became one of our first VIP clients, adopting our tailored SEO services as a part of Tharawat’s editorial workflow. Joining forces has proven highly beneficial: in the last 5 months, Tharawat Magazine has grown by +321% in terms of traffic. It is one of our best SEO case studies.
We also had the chance to refine a bespoke workflow built around a rich and well-organized editorial plan, but this is another story.
We spoke with Ramia to learn more about the fascinating world of family business.
Let’s begin with a very simple question: what is Tharawat Magazine, and how is it structured?
Today, Tharawat Magazine is one of the world’s foremost publications on family-owned businesses. With over a decade of experience and a thousand published articles, we have established our publication as a source of inspiration for business owners and experts alike.
Family businesses and their sustainability is integral to economic stability worldwide. We tell family business stories to teach, inspire and celebrate their successes.
Today, Tharawat Magazine is a part of Orbis Terra Media (OTM), a global content production and marketing studio based in Switzerland. OTM is a family-owned company operating with a decentralized team. They add significant value and a global perspective to the organisation.
Tharawat Magazine Editorial Team. From the left side: Ramia El Agamy (Editor in Chief), Sam Harrison and Alice Fogliata Cresswell (both Senior Editors), and Brianna Lish (Brand Manager).
In 5 months, your organic traffic has grown by a staggering +321%. This is a 64% increase month over month – how did this happen?
We attribute this success almost entirely to our collaboration with the WordLift team. When we came across the WordLift solution two years ago, we were under-utilizing the wealth of content we had. WordLift stepped in, cleaned up and structured over 1000 articles to increase their visibility. The accelerated returns over the last 5 months are a result of our editorial team’s understanding of how to read the traffic data to make editorial planning more SEO friendly.
Speaking of the editorial team, how do you organize their work?
We are fully decentralized; OTM team members work from around the world and come together around our magazine. We work with SaaS like Asana and HubSpot to coordinate our workflow and are also fully integrated with the WordLift team on Slack. We create original content, so we always start with the audio from recorded interviews, which is then transcribed and worked into written articles for the website and print and then resourced for our podcast the Family Business Voice.
We also field submissions from all over the world — the work of experts and academics who wish to share their latest insights on matters related family business.
From WordLift, we’ve learned the importance of creating SEO friendly content like industry-specific listicles that allow easy structuring and get rewarded by high rankings on Google SERP.
Tharawat Magazine is devoted to a very specific vertical: family business. To its credit, Tharawat Magazine also is a family business. Does it make your editorial work easier?
My family owns Orbis Terra Media, and we founded Tharawat Magazine. So let’s just say we really know what we are talking about when we publish family business stories.
However, the real force behind the success of Tharawat Magazine is our editorial team and the many family businesses who agree to share their stories with us.
Tharawat Magazine is the editorial side of a larger project, Orbis Terra Media, which you define as a content studio. Why did you decide to leverage your experience in content creation to provide a set services?
We founded Tharawat Magazine around 11 years ago in the middle of a major disruption in the publishing industry. After a few years, it became apparent to us that publishing alone would not result in the growth we wanted, and so we thought about what our strengths were. We knew that our skills in creating high-value and original content lent themselves well to content marketing services. So, we built Orbis Terra Media, which is now a global content studio. Coupled with a company culture keen on integrating technology and involving strategic partners such as the team at WordLift, we provide these services combining the best of our creative and editorial capabilities with data-based insights. At the end of the day, whether it’s for Tharawat Magazine or OTM’s content marketing clients, our goal is to create content that moves.
Tharawat Magazine is an international magazine which focuses on family businesses and provides unique inspiration for entrepreneurs throughout the world. The editorial project is aimed to inform and inspire family business owners, helping them to start, grow and sustain their businesses for generations.
The Challenge
Tharawat Magazine is keen on producing high-quality content about family business. Ramia El Agamy had three goals in mind when she became one of our first VIP clients:
reaching a wider online audience
establish Tharawat Magazine as the preeminent publisher on family business topics
After analyzing the website and its significative wealth of content, we designed an entity-based content model for Tharawat Magazine. We integrate our SEO best practices within the existing editorial workflow and, as new content is produced, we make sure it becomes SEO-friendly, clear to read and well connected with other pages. We also provide our best in-house support to ensure that the platform runs smoothly and with top notch performances.
The Results
Joining forces has proven highly beneficial: in the last 5 months, Tharawat Magazine has grown by +321% in terms of traffic.
For the first time this year we can finally say that knowledge graphs and semantic technologies are hype. People like me, who played with the semantic web stack for several years now, have long predicted that one day we would have a Graph for Everything. We did wait for long and hopefully not in vain ? until recently Gartner finally shout out loud that 2018 is indeed the “Year of the Graph”. We, here at WordLift, are far beyond the hype. We have built technologies, open source frameworks, companies and products on this vision of semantic web, knowledge representation and ontologies.
Knowledge Graphs in the Gartner’s Hype Cycle for 2018.
For many years, way too many, talking with large enterprises or public institutions like the Italian Parliament about the importance of creating taxonomies and labeling information has been extremely frustrating, and yet I am very thankful to everyone who has listened to me and helped us get to the point of writing an article like this one.
Knowledge graphs are real and bring a competitive advantage to large enterprises like Amazon, Google, LinkedIn, Uber, Zalando, Airbnb, Microsoft, and other internet powerhouses but no, this article is not about giant graphs from large enterprises. It is about our direct experience in helping travel brands like bungalowparkoverzicht in the Netherlands, the largest tour operator in Iceland and SalzburgerLand in Austria.
WHAT IS A KNOWLEDGE GRAPH?
A knowledge graph is a way of representing human-knowledge to machines. In short, you start by defining the main concepts as nodes and the relationships among these concepts as edges in a graph. READ MORE
Not all Graph are created equal and each organization has its own business goals and ways of representing relationships between related entities. We model data and build knowledge graphs to create a context, to improve content findability by leveraging on semantic search engines like Google and Bing and to provide precise answers to certain questions. When you have organized your data semantically and you have built your own taxonomy there are many applications that can be implemented: from classifying items to integrating data coming out of different pipelines, from building complex reasoning systems, to publishing metadata on the web. When we built the knowledge graph for a travel brand like bungalowparkoverzicht our main focus was on the type of information that a traveler would need before reaching the destinations.
We model data for the so-called “planning and booking moments”. Planning, accordingly to a research from Google, starts when a digital traveler has chosen a destination and is then looking for the right time and place to stay. Then the booking will follow, and that’s the moment when the travelers move into reserving their perfect hotel, choose a room and reserve it.
Types of Information to model for the planning and booking moments
When modeling hotel-related information in Web content using the schema.org vocabulary you basically work with three core type of nodes (entity types):
A lodging business, (e.g. a hotel, hostel, resort, or a camping site): essentially the place and local business that houses the actual units of the establishment (e.g. hotel rooms). The lodging business can encompass multiple buildings but is in most cases a coherent place.
An accommodation, i.e. the actually relevant units of the establishment (e.g. hotel rooms, suites, apartments, meeting rooms, camping pitches, etc.). These are the actual objects that are offered for rental.
An offer to let a hotel room (or other forms of accommodations) for a particular amount of money and for a given type of usage (e.g. occupancy), typically further constrained by advance booking requirements and other terms and conditions.
Schema Markup for hotels and lodging businesses.
Relationships (edges in the graph) between these entities are designed in such a way that several potential conversations between a lodging business and a potential client become possible. We simply:
a) encode these relationships using an open vocabulary and, by doing so,
b) easily enable search engines and/or virtual assistants to traverse these connections in multiple ways.
As seen above we can map – using the vocabulary – all the hospitality infrastructures as schema:Organization and create a page listing all the different companies behind these businesses or we can list these hotels and lodging facilities using their geolocation and the properties of the schema:Place type.
Making it happen
The content management system in the back-end uses a relational database, and this is just great as most of the data needs to be used with transactional processes (versioning, reviews are all based on efficiently storing data into tables). Our work is to apply to each data-point the semantics required to:
publish metadata on the web using structured data that machines can understand
index each item of the property inventory (i.e. all the proposed hotels, all the locations, …) with a unique identifier and a corresponding representation in an RDF knowledge graph
semantically annotate editorial content with all the nodes that are relevant for our target audience (i.e. annotating an article about a camping site in the Netherlands with the same entity that connects that location with the related schema:LodgingBusiness)
have a nice and clean API to query and eventually enrich the data in the graph using other publicly available data coming from Wikidata, GeoNames or DBpedia
provide search engines and virtual assistants with the booking URL using schema:ReserveAction(see the example below) to make this data truly actionable.
1. Publishing metadata on the Web: data quality becomes King
Since major search providers (including Google, Microsoft, Yahoo, and Yandex) joined forces to define a common language for semantic markup, semantic web technologies became an important asset of online business of all sort. At the time of writing this article, 10 million websites use Schema.org to mark up their web pages.
While there is a growing interest in adding structured data in general, the focus is now shifting from providing whatever form of structured data to providing high-quality data that can have a real impact on the new entity-oriented search.
WHAT IS ENTITY-ORIENTED SEARCH?
Entity-oriented search, as defined by Krisztian Balog in his book, is the search paradigm of organizing and accessing information centered around entities, and their attributes and relationships.
Ranking high on long tail intents like the ones we see in the travel sector is – in several cases – about providing consistent and reliable information in a structured form.
How structured data might be used in Google synthetic queries.
The importance of geocoding the address
To give you a practical example, when making explicit the data about the address of the lodging business for the Dutch website, we realised that the data we had in the CMS wasn’t good enough to be published online using schema and we decided to reverse geocode the address and extract the data in a clean and reliable format, using an external API. A simple heuristic like this one improves the quality of the data describing thousands of lodging businesses that can now be unambiguously ranked for various type of searches.
Using well-known datasets to disambiguate location-specific characteristics
In schema, when describing most of the hotel-related types and properties – e.g. telling hosts that the hotel might have a WiFi Internet connection – we can use the amenityFeature property that is derived from the STI accommodation ontology (our friends in Innsbruck at the Semantic Technology Institute that have greatly contributed to the travel extension of Schema).
Unfortunately, there is not common taxonomy yet for describing these properties (the wifi or the presence of a safe in the room). In order to help search engines and virtual assistants disambiguate these properties at best, in WordLift we’re providing a mapping between these hotel-related properties and entities in Wikidata. In this way, we can add an unambiguous pointer to – let’s say – the concept of WiFi, that in Wikidata corresponds to the entity Q29643.
2. Creating unique IDs for entities in the graph
When representing the nodes in our graph we create entities and we group them in a catalog (we call it vocabulary). All the entities we have in the catalog belong to different types (i.e. Lodging business, Organization, Place, Offer). The entity catalog defines the universe we know and each entity has its own unique identifier. The fact that we can have an ID for each node turns out to be surprisingly useful as it allows us to have a one-to-one correspondence between a node (represented by its ID) and the real-world object it represents.
An accommodation like the Strand Resort Ouddorp Duin in the South of Holland, for example, has its own unique ID in the graph on http://data.wordlift.io/wl0760/vakantiepark/strand_resort_ouddorp_duin.
3. Bridging text and structure
Combining structured and unstructured information is key for improving search breadth and quality from external search engines like Google and Bing. It also becomes very important to provide a consistent user experience within the site. Let’s say that you are referring, in an article from the blog, to South of Holland or to the Landal Strand Resort we talked about before: you want your users to see the latest promotions from this resort and/or offers from other properties nearby. Connecting editorial content from the blog using the data in the graph is called entity-linking. It is done by annotating mentions of specific entities (or properties of these entities) being described in a text, with their own unique identifiers from the underlying knowledge graph. This creates a context for the users (and for external search engines) and a simple way to improve the user experience by suggesting a meaningful navigation path (i.e. “let’s see all the resorts in the region” or “let’s see the latest offers from the Strand Resort”).
Florian Bauhuber from Tourismuszukunft presenting SLT Knowledge Graph at Castelcamp Kaprun 2018.
4. Discovering new facts by linking external data
Kaprun in GeoNames.
Having a graph in RDF format is also about linking your data with other data. A great travel destination in Salzburgerland like Kaprun has its own entity ID in the graph http://open.salzburgerland.com/en/entity/kaprunbuilt by the Region of Salzburg using WordLift. This entity is linked with the equivalent entities in the Web of data. In GeoNames it corresponds to the entity http://sws.geonames.org/2774758/ (GeoNames is a freely available geographical database that contains a lot more properties about Kaprun that what we store in our graph). We can see from GeoNames that Kaprun is 786m above sea level and belongs to the Zell am See region in Salzburgerland. These informations are immediately accessible to search engines and can be also stored in the index of the website internal search engine to let users find Kaprun when searching for towns in Zell am See or destination in Salzburgerland close to a lake. This wealth of open data, interlinked with our graph, can be made immediately accessible to our users by adding attributes in Schema that search engines understand. An internal search engine with these information becomes “semantic” and we don’t need to maintain or curate this information (unless we find it unreliable). Wow!
WHAT IS RDF?
The Resource Description Framework (RDF), is a W3C standard for describing entities in a knowledge base. An entity such as a hotel can be represented as a set of RDF statements. These statements may be seen as facts or assertions about that entity. A knowledge graph is a structured knowledge repository for storing and organizing statements about entities. READ MORE
SLT Knowledge Graph in the Linked Open Data Cloud.
5. From answering questions to making it all happen: introducing Schema Actions
We use nodes and edges in the graph to help search engines and virtual assistants answer specific questions like “Where can I find a camping site with a sauna close to a ski resort in Germany?”. These are informational intents that can be covered by providing structured data using the schema.org vocabulary to describe entities.
In 2014 Schema.org, the consortium created by the search engines to build a common vocabulary introduced a new extension called Actions. The purpose of Schema Actions is to go beyond the static description of entities – people, places, hotels, restaurants, … and to describe the actions that can be invoked (or have been invoked) using these entities.
In the context of the knowledge graph for a travel brand, we’re starting to use Schema Actions to let search engines and virtual assistants know what is the URL to be used for booking a specific hotel.
Here is an example of the JSON-LD code injected in the page of a camping village providing the indication of the URL that can be used on the different devices (see the attribute actionPlatform) to initiate the booking process.
As we’re continuing to explore new ways to collect, improve and reuse the information in the knowledge bases we are building with our clients in the travel industry, a new landscape of applications is emerging. Data is playing a pivotal role in the era of personal assistants, content recommendations and entity-oriented search. We are focusing on making knowledge as explicit as possible inside these organizations, to help searchers traverse it in a meaningful way.
The semantic web is a branch of artificial intelligence specifically designed to transfer human knowledge to machines. Human knowledge, in the travel sector, is really what creates a concrete business value for the travelers.
When planning for a next vacation we are constantly looking for something new, sometimes even unusual, but at the same time we need full reliability and we want to complete the planning and booking process in the best possible way, and with the least amount of effort.
For travel brands, destinations, online travel agencies, and resorts building a knowledge graph is truly the best way to improve the traveler experience, to market the travel offers and to prepare for the “AI-first world” of voice search and personal assistants.
Are you ready to build your travel-oriented knowledge graph? Contact us
Credits
Thanks to Rainer Edlinger and Martin Reichhartthat this year invited me to the Castel Kamp in Kaprun where every year the travel community from Austria, Germany, and Südtirol gathers to share their experiences, best practices and challenges in the digital marketing world. I have been also very happy to meet again Reinhard Lanner with whom I started this journey back in 2014. A great “Grazie” also to our wonderful team that is constantly working to improve our technology and to help our clients get the most out of our stack.
Feel free to connect if you want to know more about SEO for travel websites and if you have any more questions about my experience with Knowledge Graphs for your travel brand!
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