How using a commonsense knowledge graph can enhance an understanding of the search journey

Human behaviour is complex and never linear. Users employ a variety of queries at each stage of their journey. Marketers have tried creating frameworks or adopting conventional ones to explain the user journey. Sometimes we are guilty of oversimplifying the journey for our own convenience and satisfaction.

A blog written on Moz got into search journey using the popular AIDA (Awareness, Interest, Desire, Action and Loyalty) model. And the author used the cough medication example to illustrate how users would always start their journey with a query like “how to stop coughing” and proceed to “cough medicine” “best cough medicine for dry cough” and so on.

This journey or framework makes logical sense. Marketers and non-marketers can simply understand how search by a user can progress through the different stages. But in reality, human search behaviour is never linear as our life experience, location and external triggers are different. Some users may start with a query at the 3 stages of this journey and flipflop between a few to before making a decision.

Search Journey mapping using a before and after structure

Interestingly, whilst looking at the different ways search journeys are mapped, I ran into a blog from SEO Clarity that expressed a before and after keywords to a target keyword. For example, the target keyword “bronze glass” is predicted to have such keywords like “bronze glass mirror, bronze glass vase, bronze glass table lamp” as the preceding search term and keywords such as “bronze mirror, bronze tinted glass, bronze glass panels” as some of the next terms users are more likely to search for. Whilst this paints a logical picture of how users are likely to search, it is very lexically structured that it might fail to some degree to mimic human reasoning. But it does make a case that humans are more likely to progress or evolve with how they search for information.

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Rethinking keyword clustering through a semantic and cognitive lens

Keyword clustering remains an important task for search marketers and is key to driving organic traffic, improving rankings and enhancing the authority of the website on a given topic.

There are a handful of practical and great tools that exist in clustering keywords such as Keywordsinsights.io, SurferSEO, Inlinks and the incumbent greats such as Semrush and Ahref. For clustering people also asked questions the likes of AlsoAsked and AnswerThePublic will come in handy. The former clusters by PAA questions from SERP while the latter uses Google Autosuggests and has also added PAA to its collection.

These and a few more that haven’t been mentioned are conventional tools used for keyword clustering and have yielded ranking benefits and an inspiring source of content generation opportunities for most search marketers. But, I have a problem with the approaches and logic utilised by these tools. They almost pigeonhole human search behaviour into some lexical and linear route which ignores how searchers think, reason and act. They are more focused on clustering based on word cooccurrence and cosine similarity of the lexical nature of words but can often lack the semantic and sequential depth akin to human cognition and behaviour.

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Introducing the concept of semantic authority in search marketing

As an industry, the importance of providing in-depth coverage on a topic is essential for brand relevance and ranking. With Google’s helpful content updates, there have been cases of companies experiencing reduced rankings and traffic because they employed a spray-and-pray approach. That is employing a content marketing approach that involves creating articles for high search volume topics for the sake of generating website visits. A traffic-first approach that does not cater to the user’s needs and fails to build a solid depth or authority in a particular niche topic. With E-E-A-T (Experience, Expertise, Authoritativeness and Trustworthiness), there is no room for a blanket content marketing approach. Companies have to build an authority in a given topic that is closely related to their brand, product or service. But the journey does not need to stop there, more concepts need to be developed to help us better understand the intent of searchers and analyse how the available content is catering to these needs. For this, I am proposing the concept of ‘Semantic Authority’, an evolution of topical authority. This concept will be introduced below.

What is Semantic Authority

Semantic authority is the degree to which a website demonstrates a contextual, cognitive, and causal understanding of a domain by aligning with user intent, interpreting meaning accurately, and structuring information relationally and dynamically. To put this simply, semantic authority involves a more contextual and cognitive understanding of a topic or concept and developing the required content to cater to the user’s needs. Websites that have fully broken down the semantic relations of a concept or seed term are more likely to create contents that go a step above topical coverage. These brands and websites can be viewed to attain an authority in the meaning or semantic space.

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Moving from topic clusters to semantic clusters

As an industry, topic cluster is being heralded as one of the greatest templates to setting up a website to rank favourably for target keywords by interlinking in a well knotted format. Hubspot has put together a great resource on topic clusters and provided an experiment carried out by their former team members that indicates that effectively interlinked pages had better placements on Google’s SERP. A quick note to add is that, internal linking is always a great SEO optimisation strategy and irrespective of topic clusters or not.

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Why the search intent is more complex to classify

Analysing and modeling the user search intent goes beyond the simplistic categorisation of informational, navigationals, transactional and commercial. Users are quite complex and the purpose of their search can’t be easily inserted into the four boxes of categorisation that is common in most search platforms, articles and commentaries.

The search intent is usually conceived as the purpose behind a search. Keyword search data on third party software tools, Google Search Console and Google Keyword planner are a goldmine for data-centric marketers. The user intent should focus on the purpose, motivations and reasoning behind a search. There are user stories, prompts and triggers around the public and private keyword data. Reducing use intent to general actions that suit us as marketers deprives us from gaining deeper insights as to why users are searching in the first instance.

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A simplified analysis of the 0-1 knapsack problem

In our previous article, we touched on the Travelling Salesman problem and highlighted how it belongs to the subfield of combinatorial optimisation. The topic of combinatorial optimisation seeks to discover the most efficient object from a finite set of items. The three main combinatorial optimisation problems are Travelling Salesman Problem (TSP), Knapsack problem and Minimum Spanning Tree (MST). Our focus will be on the Knapsack problem in this article. A subsequent piece will touch on MST.

Diving into the Knapsack Problem

Let’s now assume a deeper look into the logic of the Knapsack Problem. The decision version of the Knapsack problem is considered NP-Complete. Firstly, we will briefly examine the problem statement. Providing a set of objects with attributable value and weights (Vi, Wi), what is the maximum value that can be attained when the sum of the subsets of these objects are selected to be within the knapsack capacity.

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Exploring the Traveling Salesman Problem (TSP)

Graph theory seeks to address different situations or problems in business application or organisational setups. TSP (Traveling Salesman Problem) is usually considered NP-hard (nondeterministic polynomial time) in solving decision problems. This is because there are more than one possible action or directions when deciding to traverse through every city or vertex in a given graph with the goal or returning to the original. Taking some journey down the historical lane, the TSP problem was formulated in1800s by an Irish mathematician W.R Hamiltion and his British counterpart Thomas Kirkman.

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Understanding Articulation Points in a graph with examples

Graphs can be directed or undirected in nature. Articulation points are quite important in a graph as they signal possible vulnerabilities in a given network. Removing a node from a connected undirected graph is likely to split the network into different components of an undirected graph.

A simple illustration of articulation points

The undirected graph below contains seven nodes and there are two articulation or critical points. Node B is very important to the network as it directly connects with five nodes. Removing node B will break this graph into three disconnected components. The three disconnected graphs after removing node B will be (A) , (C and D) and (E, F and G). The second articulation point on this graph is node C. A decision to remove node C will lead to two disconnected components which are nodes (A, B, E, F, G) and (D). This clearly shows that node B and C are the two articulation points with B being slightly more critical. Node B is the most critical because if removed it renders the remaining graphs into three disconnected components. On the other hand, removing vertex C splits the graph into two disconnected components.

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A practical understanding of topological sorting and ordering

The shortest path problem is pivotal in graph theory. It aims at discovering the “most efficient’ or ‘best’ way of moving from x to y, where x and y are both nodes in a given graph. The most efficient or best in this context is evaluated by the lowest sum of edge weights between the path of both vertices. The shortest or quickest path is arrived at by summing the lengths of the individual edges. A best-case scenario is a graph with edges having positive weights. There is also the concept of single-source shortest path problem with s as the source node. For clarity, the source node initiates the transversal within the graph.

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Exploring Breadth First Search and Depth First Search in a graph

You might have encountered the words, breadth and depth in real-world scenarios. Breadth implies the complete range of knowledge of any given subject or topic. On the other hand, depth in terms of learning touches on the degree to which a particular subject is magnified or explored. Let’s begin with the breadth first search or the BFS of a given graph. Now BFS does not refer to Best Friends from school but Breadth-First Search.

Exploring Breadth First Search or Breadth First Traversal

BFS is an algorithm that is designed to search for a graph or tree data formation. It usually travels in a breadthward motion and utilises a queue as a prompt to identify the next vertex to commence a traversal. If a roadblock is encountered or no adjacent node is found, the tree root or the source node is removed for the queue. The traversal of the graph usually begins with a ‘search key’ or the initialising node. Imagine a hotel with so many floors and rooms as nodes, a breadth-first traversal algorithm is like a cleaning staff that will clean rooms floor by floor. All neighbouring nodes at the current depth or floor with the example above will be visited to clean before moving to the vertices or rooms on the next floor. No node is expected to be revisited as one would not expect hotel staff to clean the same room twice in the same period. Once a room is cleaned, it is ticked on a sheet as a visited while with BFS, the neighbouring reversed node is enqueued or marked as visited,

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