Semantic search is an advanced technology for optimizing the accuracy of our search results when exploring the internet or the internal systems of an organization. It aims to make the meaning of concepts and the relationships between them understandable for machines, which helps them make better sense of the user’s intent and the query context.
For example, if we want to find what European politicians say about global warming, a search query like: “European politicians global warming” is very likely to miss document speaking of Boris Johnson, “climate change”, “rising sea level” or “greenhouse gas emissions”.
In order to offer such enhanced search experience, this type of search employs a set of semantic technology techniques for retrieving knowledge from richly structured data sources. These techniques transform structured and unstructured data into a more intuitive and responsive knowledge paradigm – the knowledge graph – and enable highly contextual and personalized results.
Inevitably, as more and more knowledge populates our digital landscape, it becomes increasingly challenging for machines to process and retrieve information on our behalf. While it is easy for humans to decide whether two or more things are related based on our cognitive associations, computers struggle and often fail to do it.
This is where semantic search can come to the rescue. Unlike traditional lexical search where search engines look for literal matches of the query words and their variants, this new generation of searching works on the principles of semantics. Or simply put, it tries to interpret natural language the way humans would.
In this way, by complementing the standard free text search with a more powerful concept search, semantics allows machines to gain a better understanding of what users may want and then offer more relevant answers.
One of the main mechanisms behind the ability of semantic search to provide more meaningful results is the knowledge graph. It integrates diverse data describing entities (e.g., people, organizations, locations, etc.) and the specific concepts in a target domain as well as the relationships between them. At the end of the process, all this data is represented as a huge network of related facts that could be explored in many different ways.
Knowledge graphs need to be combined with text analysis in order to help free-text search. Semantic annotation and indexing techniques (like those implemented in the Ontotext Platform) discover which concepts in the knowledge graph are mentioned in the text. Documents are indexed by properly identified entities and concepts, rather than just by ambiguous strings.
As a result, by analyzing the concepts in the query and the relationships between them, semantic search is able to provide a suitable response even if the results don’t contain the exact wording of the query.
As we can see, semantics and knowledge graphs empower a much more complete understanding of what our searches mean today. But there are other perks of semantic search:
The impact of semantic search on the way we look for content and query systems has been dramatic. Its implications range from discovering non-obvious relationships between facts, through predictive search, all the way to conversations with cognitive systems as the next step in search technology.
Using semantics and knowledge graphs offers unique capabilities for exploration, query and search as well as other content management tasks such as classification, recommendation, etc. By trying to bridge the language gap between humans and machines, semantic search takes us further on our quest for meaningful information and knowledge discovery.
White Paper: Knowledge Graphs in the Enterprise