The rise of eight different types of graph

We are witnessing the rise and adoption of graph databases across different verticals. Gartner acknowledged the five different types of graphs as social, intent, consumption, mobile and interest. In a presentation titled: Graph All the things! Introduction to graph databases, the team from Neo4j captured Gartner’s graph classifications in the illustration below. There is a slight difference in how one of the types of a graph is named by Neo4j in comparison to Gartner. To Neo4j it is a payment graph while to Gartner it is the consumption graph.  There is the argument that a consumption graph is a better name as we do not necessarily pay for every consumption. We will now look at each graph and add some additional types

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The world’s leading graph databases

The increase in data has led to a growing need for graph databases or technologies.  With a graph database, the relationships that exist within the data can be stored, refined and queried properly. A graph database, therefore, is a database created to store data without restricting it to a pre-set model. The data in these graph-based technology expresses how each entity is related to others.

Nodes and edges are quite important when looking at graph databases as the later represents the relationship with the former. This nodes and edges setup, makes the retrieval and querying of relationships easier. Retrieving complex hierarchical structures is an advantage that these graph technologies have over relational databases. The software review forum G2 has a list of the top-rated graph databases in the market. The leading graph database technologies on G2 have had more reviews, a higher percentage of positive feedback, more data generated from other online networks and social platforms.

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A domain specific knowledge base for experiences and events

I have been fascinated by the knowledge representation and reasoning field. My presentation on commonsense knowledge graphs instilled a greater desire for knowledge bases in the form of ontologies and knowledge graph. It became clear to me that it would be difficult  to create a deeper knowledge base that embodies commonsense knowledge A knowledge base that will extend the meaning of entities by capturing analogical reasoning elements such as metaphors will have to be domain specific. 

The Hasso Plattner Institut has a great lecture on Knowledge and Engineering with Semantic Web Technologies by Dr Harald Sack on YouTube is a great resource that has inspired me to look at domain-specific knowledge base. There are four types and categories of ontologies. These are viewed according to their level of generality and they are the top-level ontology, domain ontology, task ontology and application ontology. 

A commonsense or distilled knowledge base for the experiences and events will be viewed as a domain ontology with a bit of task ontology. This is because some experiences can be an attraction within the tourism and travel domain (e.g Buckingham Palace) and also tasks like Buckingham Palace tours, Buckingham Palace visit, Buckingham Palace changing of the guards. These Buckingham Palace related terms can be linked to top-level ontology terms such as historic sites or royal palaces.

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An introduction to comparable entity mining at a structural level.

The comparing of entities is usually important in human decision making. 

We are constantly comparing entities daily from holiday destinations, new mobile phone and next family car. Comparing entities look at the relatedness of these objects or concepts. Relatedness does not look at only similarity (analogy) but other relational structures such as resistance (antithesis), discrepancy (anomaly) and incompatibility (antinomy). 

Comparative entity mining is crucial to any keyword, concept, content, product and marketing strategy. This is because no product exists in isolation. It is therefore important for businesses to place themselves in the shoes of potential customers and explore the alternative products that are vying for the same attention and mind space. Conducting this exercise will help brands position their products in a more compellingly through  through engaging branding and compelling storytelling. 

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From keyword research to topic modelling and then concept modelling

Keywords are very powerful in today’s digital landscape. We all love us some keywords, right? The monthly search volume and competitiveness of keywords guide our product, brand, content and advertising strategy. Several blogs have been written on keyword research strategy. In connection with these strategies are tools such as Google Keyword Planner, Ahrefs, Answer the public, Moz Keyword Explorer, Google Keyword Trends and other leading tools.

Google Trends is a great tool from Google to help gauge the search volume of keywords, topics and entities over time. A quick search for London Marathon reveals London Marathon as a search term and a topic. Whilst the search term focuses more on the ‘London Marathon’ searches carried out by users. London marathon as a topic is a collection of London Marathon related search terms and engagement with London Marathon related publications. Whilst Google Trends fails to provide an exact or bucketed search volume data, its scaling system of 1-10 indicates the popularity of a search term over a time range.

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A Count-based and Predictive vector models in the Semantic Age

Computer applications such as search engines and dialogue systems usually process a large volume of text on a regular basis. These systems have progressed over the years due to advanced word embeddings, progressive research and modern machine learning libraries.  It is believed that audio and visual datasets have dense high-dimensional datasets encoded as vectors of a separate raw pixel. Text generated from natural language is usually understood to contain vectors that are sparse when compared to video and visuals.  

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Introducing the concept of relational intent

I have been reading research articles and thesis on the concept of relational reasoning. It is quite an interesting concept that is deeply rooted in the fields of cognitive science, neuroscience and artificial intelligence. Humans are generally regarded as relational beings as we constantly seek interaction and affection from others. The ability to discern meaningful patterns in a stream of data ensures we are not prisoners of our own senses. We utilise our senses such as sights, smell, sound and touch to encode data on a daily basis. A small portion of these data attains a sense of meaning when we find useful patterns. These patterns enable our ability to understand concepts and take necessary actions.

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10 key differences between relational thinking and relational reasoning

Our brain encounters billions of objects or precepts on a daily basis. These data come in varying forms such as sound, smell and images (vision). Our sensory systems such as eyes, nose, ears, tongue and touch encode these signals that could either be transmitted to our brains or ignored. It is absolutely impossible to process all the signals our senses come in contact with on a daily basis. All of these signals are known as precepts and a few of these become concepts. Entities or signals that assume the role of concepts are those we can develop a relational identity. Example, you visit a sports store and see running shoes displayed in the far right corner, these are all precepts. When you relate or associate the shoes with running a 5k, 10k, half or full marathon, they now become a concept. It takes relational thinking and reasoning to transform precepts into concepts. But there are differences between relational thinking and reasoning.

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The importance of fine grained named entity recognition

Name entity recognition is usually viewed as a low level NLP task but could be crucial to other tasks such as named entity disambiguation and linking. It is also relevant for information retrieval and question and answering applications. Standard named entity recognition classes are usually person, location and miscellaneous. I used the AllenNLP demo application to run a quick NER test for the Hacksaw ridge storyline. The text was extracted from the IMDB website and the below image indicates the entities. Previous research led to the identification of three core classes – person, location and organisation.  During the Computational Natural Language Learning conference of 2003, a miscellaneous type was then added to the mix

The below reveals the four main entity classes or the non-fine grained, All four (person, organisation, location and miscellaneous) entity tags are highlighted. Desmond T. Doss is the name of the star character in the story and it is accurately identifies him as a person. When his surname was mentioned (Doss’s), it also has the accurate personal tag.  The miscellaneous tag was used for events like the ‘Battle of Okinawa’ and a thing ‘Congressional medal of honor.’

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Relational reasoning as the basis for human intelligence

A blog and research paper from Google Deepmind brought our attention to the concept of relational reasoning. As humans we have the innate ability to connect the dots or plot a narrative from piece of information to make a decision either to run a search, make a purchase or predict the outcome of a movie. Artificial agents are yet to attain the creative human ability to connect entities together via a narrative exercise that leads to human action. A few days ago my wife told me an interesting story. She ran into a friend at the entrance of a shop, he’s just grabbed himself a drink and some popcorn. She asked him where he was headed and he said to West India Quay. It is a place in east London which boasts of a handful of restaurants, bars and a cinema. She pieced together the the popcorn, drinks and West Indian Quay and asked him if he was headed to the cinema? He was quite surprised and affirmed he was headed to the cinema. She then predicted or stated that he owns a monthly Cineworld membership. He was quite shocked and nodded to having a monthly Cineworld membership. She told me, her experience of buying popcorn, drink and preferring the West India Quay Cineworld with her Meetup movie mates, assisted her in creating a relationship and narrative from the little information she received to correctly predict the intention of her friend. This is relational reasoning at work as the deepmind team clearly mentioned “ We carve our world into relations between things. And we understand how the world works through our capacity to draw logical conclusions about how these different things – such as physical objects, sentences, or even abstract ideas – are related to one another.”

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