Are you making the most of your collected data? The data you accumulate through your products and services can be a game-changer for your organization. Imagine if you can put that information to the proper use! Knowledge Graphs can allow you to make the most of your information to access, search, and utilize data for your enterprise search needs.
What Is a Knowledge Graph?
A Knowledge Graph is a progressive way of interconnected search, an accurate query search resolution system that combines entities like people, objects, and places. Knowledge Graphs are popular for applications into search engines¹. It is a search method that leads to the most relevant information.
More technically, Knowledge Graph interlinks data pieces related to users’ query keywords and intentions behind. Knowledge Graphs, along with Natural Language Processing (NLP), can come up with accurate answers across the database. They can be applied to extract Semantic Triples: the subject, the predicate, and the object from the information to build efficient question-answer systems².
How can knowledge graphs be used for search?
A Knowledge Graph can establish contextual relationships between search entities, display relevant results, or make search engines accurate. Notably, the core objective of Knowledge Graphs for an organization is to enable users to find contextual information with minimum effort. The whole process of implementing Knowledge Graphs can be generalized as given below:
1. Prepare a data inventory for Knowledge Graphs
A reliable data source is a critical factor in creating efficient Knowledge Graphs. A quality data inventory can allow organizations to map Knowledge graphs in a machine-readable way. One must dive deeper to find and maintain accurate data in this step.
For instance, ask further questions about data entities until the useful pieces of information surface; it can be metadata fields on a report, segmenting the deliverable, or identifying users that worked on a document. Next, the user must identify where this data lives within the system architecture and how to extract it for Knowledge Graphs efficiently. In some cases, Knowledge Graphs require multiple data sources to be linked.
2. Semantic data modeling with Ontology
Once a reliable data source is ready, the next step is to determine how data pieces can answer user queries better. Here, domain experts establish this holistic view of data and build a model to leverage it with Ontology modularity. A model can play a key role in interconnecting data with the help of classes, attributes, and relationships.
Here, domain experts and stakeholders can identify different types of information, relevant attributes, and the relationship between different data pieces. Ontology model design practices will allow you to translate relevant information into a scalable data model.
Tools can help with Semantic data modeling. For example, Neo4j enables entities to be organized with edges that help graph traversals. Additionally, RDF graphs use subjects, predicates, and objects with IRIs (internationalized web addresses) to form graphs offering semantic clarity and ease of integration.
3. User experience & Knowledge Graph accessibility
The third step is to build the end-user application where the UI is designed to leverage Knowledge Graph’s abilities to the full extent. Understanding user stories to determine their priorities and expected results is the right way to make Knowledge Graphs accessible.
Named-entity recognition can identify the particular search subject and extend search results in an accessible manner. For example, Google search shows specific page designs when searching for an organization, celebrity, or product to purchase⁴. A similar implementation can be used for enterprise search solutions.
4. Populate and ingest data into Knowledge Graph
Once the data is sourced, refined, and modeled, it will be applied as a Knowledge Graph search solution. Here, we need to integrate Knowledge Graphs to extract information through APIs or exports. Users must account for indexing needs for the data pipeline and if multiple data sources are linked through NER or Taxonomy. One can address any data standardization or data quality challenges in this step.
5. Implement and improvise
Once the search solution and Knowledge Graph are ready with the indexed data, the next step is to test it with several pilots to get feedback and validation. By now, you will be able to find the relevant information right away without hassle. Therefore, you need to reiterate Knowledge Graph with updated data sources, new user queries, more feedback implementations, and feature changes from time to time.
How Knowledge Graphs Can Benefit Your Search:
Here are the several advantages to implementing Knowledge Graphs for your business:
- Using Knowledge Graphs to link data sources:
Enterprise information is shared across departments, and as such, all that information must be linked to give an entire overview and insights⁵.
- Allow users to summarize relationships and hierarchical data
Sequential representation of hierarchical data is useful to make insightful conclusions. Knowledge Graphs can offer an intuitive framework to connect data pieces and visualize the flow of information⁵.
- NLP and Knowledge Graphs for better problem solving
Search engines like Google leverage NLP to understand search queries and then leverage Knowledge Graphs to efficiently share the most relevant answers².
Use Cases of Knowledge Graphs:
- Knowledge graphs in Google Search
Google uses Knowledge Graphs to improve the search engine results through information gathered from sources such as the World Factbook, Wikipedia, and Wikidata. As per Google, their Knowledge Graph carried over 500 billion facts on almost 5 billion entities by 2020⁶. These “Knowledge Panels” are presented on the right side of search results⁷.
Typically, these Knowledge Panels offer a quick search overview for search queries. They can typically include a brief on the subject, relevant pictures of the query, Key facts, important reference links, and notable figures.
- NASA’s space exploration insights
A massive organization like NASA stores its vast data among different silos. NASA leverages Knowledge Graph to connect millions of nodes to connect information quickly. NASA was able to benefit through Knowledge Graph to identify an issue about the Apollo and Orion eras and to resolve it, saving one million dollars³.
These are just a couple of the popular examples among many! Isn’t it exciting that your business can benefit from Knowledge Graphs quite similarly?
Closing Statement!
Knowledge Graphs are now being widely acclaimed for search solutions. It can allow your users to consume your platform’s information naturally. You can also harness the power of Knowledge Graphs as you evolve your enterprise search abilities.
Connect with us now, and let’s discuss how we can help you through this journey!
Communicating Knowledge, Saltlux.
References:
- https://neilpatel.com/blog/the-beginners-guide-to-the-googles-knowledge-graph/
- https://www.accenture.com/us-en/blogs/search-and-content-analytics-blog/enterprise-search-knowledge-graphs
- https://neo4j.com/blog/top-10-use-cases-knowledge-graphs/
- https://www.searchenginejournal.com/how-google-knowledge-graph-works/400485/#close
- https://engineb.com/2021/02/8-key-benefits-of-knowledge-graphs/
- https://blog.google/products/search/about-knowledge-graph-and-knowledge-panels/
- https://support.google.com/knowledgepanel/answer/9787176?hl=en&ref_topic=9803953
How Knowledge Graphs Can Benefit Your Search was originally published in Chatbots Life on Medium, where people are continuing the conversation by highlighting and responding to this story.