In recent times semantic search has started challenging the predominance of keyword search by gaining wider acceptance as an alternative to it.
What is Semantic Search ?
Semantic search, also know as Vector search, is a type of search that goes beyond conventional keyword-based search and tries to understand the intent and meaning behind a user’s query. It leverages the capabilities of Natural Language Processing (NLP) and Machine Learning (ML) algorithms. Semantic search understands the context and relationships among words and concepts in user’s query and identifies the most significant results derived from their semantic meaning. This is commonly used on chatbots, virtual assistants and enterprise search to provide accurate and personalized results to its users.
Semantic search can recognize the hidden meaning of user’s query and recognize associated concepts, its synonyms and dubious terms as well. It helps to cater more exhaustive and related results even though user’s intent is not well-defined or its has multiple meanings.
Pros & Cons of Semantic Search
Semantic Search is picking up steam due to its ability to grasp the intent and meaning behind user’s query, generating more suitable and personalized results. However it doesn’t fit all use cases. By knowing its pros, cons and trade offs with existing keyword search we can choose the best use cases of an organization for semantic search.
- Search results are more comprehensive and inclusive due to identification and matching of term synonyms and variations.
- It offers more suitable results by considering query context. It can understand relationship between terms. e.g. verbs, adverbs, nouns, adjectives, part-of-speech etc.
- Provides a more user-friendly experience. Users can express their intent using their natural language phrases, synonyms etc.
- Semantic search is compute intensive. To speed up the process optimization algorithms needs to be used. However when speed increases accuracy decreases.
- One or two term queries produce less relevant results compared to search phrase queries. Therefore current search pattern needs to be analyzed first.
- Language model needs to be fine tuned in the context of business domain to improve accuracy of search results. This is a time-consuming and resource-intensive process.
Features of Semantic Search
At present, semantic search is considered as one of the brightest techniques to improve search and organizing information. It has proven its effectiveness in a sort of fields, say Computer Vision and Natural Language Processing.
Moreover, there are many features of semantic search that make it suitable for enterprise search. For example,
- Semantic search can retrieve content regardless of the language of the content or user query by using multilingual language models.
- Untagged video, audio or image can be retrieved by natural language queries.
- Semantic search can take content as query input.
Semantic search will be a better solution when users are searching in their natural language and not all text-based content to be retrieved or when not only a person (an API) is consuming the search.