The importance in Knowledge Graphs in Artificial Intelligence

The process of presenting knowledge and the logic behind conclusion drawn from it has been the foundation in Artificial Intelligence (AI) for many decades. Knowledge graphs (KG) is an effective data structure that presents information in a graphic format. The open-source DBpedia knowledge graph defines a graph as “a particular kind of database that can store information in a machine-readable format. It also allows information to be gathered, organized to be shared, searched, and used.” The most important thing is that knowledge graphs can also be described as graph databases, facilitating relationship-based reasoning between the data points.

The formal definition of a KG is an inversely labeled graph that represents relationships among data elements. A node on the KG is a symbol for a data point. The information point can be an individual or place or a website. An edge is a connection between two data points (for instance, a relationship between two persons or links between web pages.)

In this way, KG quite resembles a relational database, in that both databases store data that illustrates the connection between two data points (e.g. entities). However, both may be different in their goals in reasoning. While relational databases are based on logic using the attributes of a data point (i.e., columns of tables with data), KG focuses on reasoning using data points.

A Short History of Knowledge Graphs

In 1956 a semantic network, an eminent ancestor of KGs, was initially developed to be an “interlingua” to facilitate computer-generated language translations. In the mid-80s, Groningen and Twenty Netherlands universities joined forces to create the project known as “Knowledge graphs.” The KGs were mostly semantic networks that had additional constraints to allow algebraic operations. As of 2012, Google called their Knowledge Base Google Knowledge Graph.

Principal Uses of KGs

In the past ten years or so, major tech companies such as Google, Amazon, and Facebook have invested millions of dollars in developing their KGs that enhance their search engines and comprehend the context of an inquiry and understand specific user intentions.

Google makes use of KG to provide its search results using data gathered from various sources. The information collected by KG is shown to users as a knowledge panel alongside the search results. Thus, if you conduct a query, Google blends previous outcomes and information other users might have come across using KG to help you with your search.

Facebook uses KG to track connections between people’s networks and between entities that are socially relevant, including the things that are most talked about among its members. In addition to using KGs to identify social connections among users and give users advice on social activities, the graph search feature on Facebook utilizes KG to provide answers to users’ natural language questions. A significant reason KGs are becoming so essential realizes that relationships between data points are just as substantial as data itself, especially when building social networks.

Netflix utilizes KG to organize the data on its massive content catalog, identifying the connections between movies, TV shows, and the producers, directors, and actors, the people who connect them. In addition, the KG assists in determining the kind of content users would like to watch next and helps develop this “binge-watch” strategy of a business.

Siemens employs KG to build representations of the information they generate and stores. It also uses it to manage risk and process monitoring. They also employ KG to create “digital twins,” a simulation model of real-world systems. They hire the graph to build prototypes, test, and complete. KG is also utilized in the financial sector to monitor fraudulent transactions and tasks such as investment analysis and marketing.

However, the maintenance and storage of the vast majority of the real-world KGs are becoming a daunting task because of their increasing size. The KGs that are currently available are massive in scale. For example, a current version of Wikidata included more than 80 million objects and more than 1 billion relationships. Many knowledge graphs for the industry are much more significant. For example, a new edition of the Google knowledge graph included more than 560 million entities and 18 billion connections. The sheer number of knowledge graphs makes the effectiveness and scalability of the graph algorithms vital.

Examples of Knowledge Graphs

In addition to the previously mentioned special-purpose KGs, Some of the most commonly-used KGs include:

WordNet is an array of words paired with English Dictionary and Thesaurus phrases related in terms of relationships like type_of and part_of as well as has_part, etc. WordNet is usually used to boost the performance of tasks that require NLP.

DBPedia is an encyclopedia that covers objects like people, places, films, books and organizations and diseases, species, etc. The KG utilizes the structure built of Wikipedia infoboxes to construct an ontology comprised of 4.58 million items.

Geonames is a KG of over 25 million geographic entities and has many features.

Kgs as well as Machine Learning

To deal with the massive KG in the AI community, machine learning (ML) is used to create and organize KGs and identify connections between data elements that could not otherwise be apparent. But this isn’t only a one-sided issue, as ML also reaps benefits from KGs to comprehend data, such as audio, video, and text, which can’t be incorporated easily in the database structure of a relational database.

This union between KGs and ML is being praised because of its numerous benefits. KGs embedded words method is a well-known input representation method used to represent symbolic data. KGs are used as an addition to model-based machine learning to help make AI systems more accessible and understandable. They also provide the ability to think about objects, which is essential in developing the capacity to respond to questions, comprehend images, and access information.

There is still a lot of untapped potential for the combination of KGs and ML. For instance, a considerable amount of information (like resource description frameworks, RDFs) is accessible via the Internet (e.g., Wikipedia) and accessible in large part for free and is not being used by the current AI systems. The hybrid KG and ML systems will significantly benefit from this information to gain a better understanding of the world around us and identify and organize the knowledge that is not available.

Leave a Reply