Get Relevant Answers Every Time with Semantic Search

Semantic search uses natural language processing (NLP), machine learning (ML) and AI to understand the meaning and context of user queries to drive faster and more efficient search. Semantic engines can interpret the relationships between words and concepts in user queries by leveraging contextual understanding, entity recognition, knowledge modeling and classification queries to determine a user’s intent. The result is more relevant, accurate results that align with what users are really looking for.

The history of semantic search is rooted in the need for more intelligent and context-aware search engines. Through advancements in AI and machine learning, search technologies have evolved from basic text search to more sophisticated question-answering cognitive assistants. Companies must leverage knowledge graphs to elevate their search experiences, offer more accurate, relevant and personalized user interactions and drive business value. Explore this blog to learn more about the evolution of search technologies in the age of AI.

While traditional search relies heavily on keyword matching, semantic search goes further by understanding the meaning, context and intent behind user queries, resulting in more accurate and personalized results. In the results of traditional keyword searches, only terms included in the query are highlighted in the retrieved text. Semantic search engines comprehend the relationship between words and the meaning behind them to deliver tailored information that matches a user’s query rather than focusing solely on exact words and phrases. For instance, when a user searches for "Amazon," semantic search uses the context of the query to determine whether the user is referring to the online retailer or the rainforest. Since it uses concepts rather than specific terms, it can also return results for "Prime," which would be difficult with keyword-only search.

Vector search finds documents with similar content based on vector proximity, while semantic search understands the user's intent based on context and delivers results that match that intent. Vector search uses mathematical representations of words and phrases, allowing the search engine to calculate the similarity between terms based on their positions in this vector space. Vector search leverages embeddings, where words with similar meanings are positioned closer together. This enables the search engine to understand and retrieve documents with related content, even if the exact search terms are not present.

For companies that want the results to be more aligned with the user's true intent and have human oversight over their data, semantic search is the preferred option because it understands the nuances of human language.

By understanding the meaning, context and intent behind queries, semantic search provides more accurate and relevant results so users can find the answers they need faster. Since semantic search also takes into account a user’s search history and patterns, results are more tailored to their needs and preferences. This personalization makes it easier to discover new, related content users might not have explicitly searched for but are likely to find useful.

Basic search relies on the user to manually add accurate metadata and cannot derive the context from the words or phrases in the query. Using the Semaphore platform to create a knowledge model allows you to define the concepts relevant to your organization as well as the relationships between the concepts. From the knowledge model, rules are published and used by the Semaphore Classification Server to automatically classify the documents. The result is precise and accurate metadata-tagged content that can be leveraged by an enterprise search engine to enhance search results.