Discover the power of semantic search
In companies, around 25% of working time is spent searching for relevant information – a considerable amount of time and effort that is caused by inefficient search methods. At the same time, 80% of company data is unstructured and grows three times faster than structured data. This unstructured data includes PDFs, emails, videos and audio files, which makes searching even more difficult.
Efficient search tools are therefore the key to increasing productivity, improving decision-making processes and tapping into new economic potential. However, conventional search methods often reach their limits, especially with semi-structured documents such as user manuals. Inefficient results and the need for specific keywords make the search time-consuming.
Semantic search offers an intelligent solution here: it understands the context and meaning of queries, delivers more precise results and makes the entire search process more efficient.
How semantic search works:
The most common problems encountered when searching for relevant information in such documents include:
- Inefficiency in processing semi-structured documents.
- Unnatural search queries.
- Requiring prior knowledge of document keywords.
- Limited ability to search within tables.
- Time-cinsuming reading of documents after retrieval.
To overcome these challenges, semantic searches organise and categorise information as soon as documents are uploaded. This optimises the efficiency of future searches and ensures a more precise display of results.
- Organising content:
The text is divided into sections. This allows the application to better understand the context and meaning.
These sections are analysed and saved. This makes it easy to find similar content again later. The application finds what it is looking for even when different words are used to describe the same term. - Identification of keywords:Important words and expressions are picked out of the text. These keywords act like tags that help to quickly identify relevant parts of the document during a search.
The application also checks the layout of the document and identifies titles and paragraphs separately. This helps to provide more accurate search results as the structure of the document is understood.
Making searching easy
When searching for information, the application goes through a two-stage process to find the best answer:
- Find relevant information:
- It searches for document sections that match the meaning of the search terms. Advanced algorithms help to understand the context of the query.
- These sections are ranked to find the most relevant ones. This means that the application not only searches for exact word matches, but understands the entire topic.
- Generate an answer:
- The most relevant section is combined with the search query to create a prompt for an intelligent language model. This model is designed to understand and generate human-like text.
- The model processes this prompt and produces a clear and accurate response based on the information contained in the document. This response is then sent back in real time, making the search process fast and efficient.
A powerful language model that can understand and generate human-like text is used to apply this process. This model has been trained on a huge amount of data so that it can provide accurate and contextualised answers to queries. It’s like having an expert assistant that quickly finds and summarises information for you.
Future extensions
There is great potential for improving and expanding this application. Here are some exciting possibilities:
- Support for more document types:
The application can easily be updated to handle different types of documents by converting them to PDF. This means that in the future you can search images, handwritten notes, web pages and more with the same ease and accuracy.
- Use better models:
Upgrading to more advanced models can improve performance and support multiple languages. This would make the application even more powerful and versatile, allowing it to handle more complex queries and provide even better answers.
- Improving search prompts:
If we refine our queries, the model can provide even better answers. For example, using structured formats for tables or specific instructions for certain types of queries can significantly improve the quality of the answers generated.
The importance of semantic search
Semantic search represents a significant advance in the way information is handled. Traditional search engines rely heavily on keyword matching, which often leads to irrelevant results unless exactly the right words are used. Semantic search, on the other hand, understands the meaning behind the words and delivers more accurate and relevant results.
This technology has the potential to transform various fields, from academic research to everyday information retrieval. The possibilities are endless.
Application of semantic search in the EAM system
Integrating semantic search into an Enterprise Asset Management (EAM) system can provide significant benefits by improving the efficiency and accuracy of information search and processing. Here are some specific benefits:
- Efficient inforamtion search:Faster data access: employees can find relevant information about assets, maintenance logs and operating instructions faster without having to know exact keywords.
Context-based results: Semantic search understands the context of search queries and provides more accurate results that go beyond simple keyword matches. - Improved decision making:
Access to historical data: Semantic search allows historical maintenance data and reports to be easily retrieved, enabling informed decisions about future maintenance strategies.
Linking information: the ability to link and analyse different data sources helps to identify patterns and trends that are critical to optimising plant performance. - Increased productivity:
Reduced search times: Employees spend less time searching for information and can focus more on their core tasks.
Automated document processing: Semantic search can automatically organise and classify documents, making it easier to manage and access important documents. - Improved maintenance processes:
Preventive maintenance: quick access to relevant data means that preventive maintenance measures can be planned and carried out more efficiently.
Fault diagnosis: Semantic search can help diagnose system faults by bringing together relevant information from various documents and reports. - Ease of use:
Natural language processing: users can make search queries in natural language, which increases the user-friendliness and acceptance of the system.
Intuitive result: Semantic search results are intuitive and easy to understand, reducing training requirements for new users.
By implementing semantic search into an EAM system, organisations can increase the efficiency of their maintenance processes, improve decision making and increase overall productivity. This ultimately leads to better asset availability and performance.
Semantic search is an exciting innovation that brings us closer to truly intelligent information retrieval. By understanding the meaning behind our words, it delivers more accurate and relevant results, making our searches faster and more efficient. As this technology continues to develop, we can look forward to even more powerful and versatile search tools that will transform the way we search for and interact with information.