The word semantic means ‘meaning’ and semantic search means focusing on the meaning of a term in order to bring the most relevant results. Search has come a long way from the days of yore when the keyword-based approach was the norm and it was all that people had in order to find the information they wanted. However, technology has evolved a lot ever since, and today NLP, AI, machine learning and semantic technology are the key concepts in the world of data science, search and enterprise data. Organizations across the globe are waking up to the necessity of advanced search engines like 3RDi Search, AddSearch and Algolia that are powered by these technologies in order to deal with and make the best use of the ever growing volumes of data.
Semantic search is important because it’s powerful when it comes to analysis of unstructured data, and most of the data that enterprises have today is unstructured. However, organizations cannot afford to ignore this data altogether because it often contains deep insights hidden within – insights that can help enterprises come up with important business decisions. This article is all about the many ways in which semantic search engines can redefine the experience for the users.
1. Results that are Context Driven
- Same query can call for different results depending on context.
- Understanding the query context is the path-breaking approach of semantic search.
- Semantic technology leverages the relationship between entities in the data.
- Context driven results are far more relevant or closer to what the users are looking for.
- This adds to the quality of search provided by the semantic search engine.
2. Faceted Search for More Refined Results
- Faceted search is all about allowing the user to refine the results using different filters.
- This is referred to as multi-level filtered navigation.
- More useful when there are many elements in the data, and they are related in some ways, but different in other ways.
- The software provides relevant results but the user should be allowed to have the power to use filters to remove the irrelevant information.
- This concept is very useful for ecommerce sites where users need to find relevant results from thousands of products.
3. Enterprise Knowledge Graph for Important Insights
- The Enterprise Knowledge Graph can only be possible with semantic search technology.
- It is the most useful graph database technology in the world today.
- Enterprise knowledge graphs are of 3 types – Internal operations knowledge graph, External customer knowledge graph, and Intermediary products and services knowledge graph.
- Internal operations knowledge graph provides insights into the skills and knowledge available within the company and how they can work in tandem for the betterment of the company.
- External customer knowledge graph offers a 360-degree view of the clients of the organization, with detailed insights about each customer.
- Intermediary products and services knowledge graph provides the insights needed to take the products and services offered by the company, to the next level.
4. Insights to Make Best Use of Business Data
- Businesses today have enormous volumes of data that is increasing day by day.
- Most of this data is unstructured and so is difficult to analyze.
- Semantic search technology makes it possible to analyze unstructured data effectively.
- This makes it easy to derive deep insights from unstructured data, which can be used by the company to make business decisions.
5. Scalability to Face the Challenges of the Future
- The future will be all about the growth of Big Data which means the data volume will keep on increasing.
- Semantic search technology ensures the business is ready for it.
- Irrespective of the growth of data, semantic search engines today offer businesses the scalability they need.
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