For information to bring value to your business, it needs to be available to the right people at the right time.
When customers have questions about your services, they should be able to find answers easily through your company. When employees have to access important documents to complete their tasks, they need a convenient and fast way to search through the company’s knowledge base. For both the customer and the employee, a delay in retrieving the information will lead to a negative experience, even if the information is correct and helpful.
Most of the valuable information contained in your company documents is in the form of text data. As we know, text data is often unstructured or semi-structured. This makes manually cataloging the data and delivering meaningful results a daunting challenge. Most companies rely on information retrieval systems to make their knowledge management systems (KMS) more efficient.
In this guide, we will delve into the myriad ways information retrieval (IR) can be an invaluable asset for your business’ document organization.
What is information retrieval?
Some clues about IR’s purpose can be drawn from its name. Information retrieval can be defined as a business process that allows users (either internal, external, or both) to efficiently retrieve data from large repositories. While primarily used for text data, IR can also be applied to other forms of data almost as effectively.
Ever since humans have been compiling data, we have used IR systems to access it. The Dewey Decimal System, used to catalog library books for over 100 years, is an example of an archaic IR system. You’ve probably used an IR system yourself at some point today — search engines like Google are nothing more than an upgraded IR system for a digital world.
Today, most IR systems are software or a web/mobile application. Many of these digital IR systems would use structured query language (SQL) to scan through all the documents in a database. However, the latest IR products harness the power of artificial intelligence (AI) and natural language processing (NLP) to boldly move beyond the old and limiting paradigm of structured data queries.
Key components of information retrieval
When discussing IR, it’s important to remember that it consists of several components.
Document collection: Putting together a set of documents that forms the IR’s data repository.
Indexing: All the data in the repository is processed by mapping search terms and keywords to all documents that contain them.
Query processing: This is the IR system’s method for analyzing user queries and keywords, processing them, and locating matches within the indexed documents.
Results ranking: This is a ranking algorithm that assigns a value to documents in the repository according to a search query, which will determine the order in which the documents are displayed as results.
User interface (UI): The user-facing aspect of every IR system, UI is where queries are entered and results are displayed.
The need for information retrieval systems
A robust AI-powered IR system is a must-have for any business looking to succeed in a digital world. With increased online shopping and digital interactions, every business activity generates new data. To get an idea of the volumes we’re talking about, ponder this: global data creation is expected to reach 180 zettabytes by next year. Specifically, one zettabyte equals a trillion gigabytes.
Even on a smaller scale, individual companies must find ways to manage large volumes of text data. Customer feedback, industry research, product development reports, and other company resources are just some documents that will go into your data repository. An IR system will allow you to retrieve them when needed without losing time searching for them manually.
AI-powered IR systems give users a convenient platform to access relevant information, which, in turn, benefits your business.
How information retrieval helps with document organization
Document organization, or document management, is how your organization stores and tracks its documents. Your company’s document management system (DMS) refers to its approach to storing, sharing, and distributing documents in the company database. Ideally, your DMS and IR system should work in tandem.
Better organized repositories will yield more relevant results to user queries. More pertinent, high-quality results will drive user engagement, which, in turn, generates more data. This data completes the cycle by containing insights that can be used to help refine the IR system and document classification.
Some other benefits of integrating IR and document organization are:
Easy access to information
Ability to deliver personalized results
Scalable for large datasets
Eliminates the chance for human error in IR
With the right IR system, even the most cluttered data repository can be neatly organized for analysis.
Different kinds of information retrieval models
At the heart of every IR system is its IR model. This refers to the method it uses to rank documents according to relevance for each search query. Many types of IR models are in use, each with its own perks and drawbacks. Broadly speaking, IR models can be separated into three main groups: classical, non-classical, and alternative.
Classical IR models
These were the earliest IR models developed, beginning in the 1970s. Classical models form the foundation of the IR field as a whole. Some popular classical IR models include:
Standard Boolean model
Vector Space model
Non-classical IR models
Developed in response to classical IR models, non-classical IR models attempt to address the limitations of their predecessors. They do not operate based on mathematical principles of similarity and probability like classical IR models. Some common non-classical IR models include:
Information Logic model
Situation Theory model
Alternative IR models
The newest development in IR, alternative IR models incorporate exciting new technologies such as artificial intelligence (AI), machine learning (ML), and large language models (LLM). With more companies adopting alternative IR models, the following types are used more frequently:
Fuzzy Set model
Latent Semantic Indexing (LSI) model
Knowing how your IR model works will help with document organization since you will be able to find relevant results through your IR system’s UI more efficiently.
Seven key information retrieval techniques when searching for documents
Once you’ve mastered the workings of your IR model, you can optimize the search and retrieval of relevant documents from large datasets using the following IR techniques.
Simply relying on keyword matching is an outdated way to retrieve important documents. Newer IR systems leverage NLP, a feature that lets users find information based on the meaning of their queries. Rather than simple lexical matches, semantic search allows IR systems to understand the meaning and intent behind every user search query.
Personalized information delivery
No two users of an IR system are alike. They will have different areas of interest while searching (various reasons to look for documents) and individual patterns of interaction with the system. With ML technology, IR systems can tailor their results according to each user’s unique interests, preferences, and needs.
This adds value for both internal and external users of an IR system.
It’s unlikely that all the documents in your repository will be text-based. It’s much more common for databases to contain information across a range of modalities, from text to image and video. Cross-modal retrieval lets users search across modalities. For example, they can enter a text query and receive results in multimedia format. Common cross-modalities include image-text, video-text, and audio-text.
AI-powered IR systems can read the situational context of a user’s query. By including context in its results ranking, the system ensures users receive relevant and timely information that adds value based on their current environment.
Document clustering and categorization
By using automated document clustering and categorization, IR systems improve the experience of navigating the database. Document clustering works similarly to text clustering in that individual documents with similar content are grouped together in clusters.
AI-driven IR systems can perform document clustering without supervision. The IR system predicts the document type by analyzing its features in document categorization.
IR systems are already a powerful means of locating information within your data repository. But with metadata enrichment, you can improve their performance even further. Metadata enrichment involves adding additional information to documents that helps determine their relevance to queries, improves their discoverability, and allows for more detailed categorization of documents.
Temporal information retrieval
While document relevance is important in IR, temporal relevance deserves attention too. Temporal information retrieval aims to enable users to retrieve information relevant to specific periods. For example, when researching an emerging trend, your marketing team will want access to the latest data. On the other hand, a team that is compiling a performance review will need to locate historical data. The best IR systems work under the principle that a document’s timeliness deserves the same importance as its contents.
Diverse applications for information retrieval
Information retrieval is an enterprise technology that’s extremely valuable to customers and employees alike. It satisfies an essential need for information that is common across all business interactions. Many industries have continued to perfect different applications for IR depending on their specific use cases.
Ecommerce: The UI on ecommerce websites, like Amazon’s search bar, is essentially an IR system that directs customers toward product pages that are most relevant to their search query.
Legal: Extremely secure, private, and specialized IR systems are used in the legal field to retrieve official documents and other important information.
Healthcare: Doctors and other healthcare professionals find it much easier to review patient records, browse medical literature, and check clinical guidelines with access to an IR system.
Traditional IR systems are great for searching through text databases, but the amount of digital multimedia content increases every day. Despite organizations’ growing capacity to host and browse through multimedia content, searching for a specific file is much more difficult. Traditional text-based search methods like keyword search don’t work as effectively for video and images.
Visual information retrieval (VIR) is a response to the drawbacks of traditional text-based IR systems. It is a more efficient method of retrieving images, videos, or related visual content relevant to search queries. VIR systems automatically or semi-automatically build an index of multimedia databases, streamlining the search functions even for non-text-based documents.
Enhance your information retrieval with the right software
Developing and maintaining an IR system requires a certain level of technical know-how. For companies that don’t have the skills in-house, the safest bet is partnering with an expert.
The software KnowledgeBase, a product from Text, is an excellent IR system that caters to external and internal users alike. With KnowledgeBase, you get an AI-powered search tool that transforms how your organization accesses information.