The rapidly increasing data – both structured and unstructured – in the pharma industry creates several challenges as well as opportunities for large-scale data mining. Semantic query search - currently used by most search-engines such as Google, Yahoo, and Bing - holds great promise in simplifying and enhancing various data mining processes across functions such as research, vigilance, compliance, intellectual property, and legal.
In previous blogs, we discussed how in Named Entity Extraction and similar algorithms, AI can be leveraged to extract the information and make information structured from unstructured documents. In this blog, we learn about yet another application of AI that works as a bridge between structured and unstructured data. Searching information with semantic query makes the search exercise easy and precise and quickly returns required information in less effort.
Challenges of traditional keyword-based search
With increasing amounts of unstructured data available in publications, legal, and regulatory documents, the process of data mining is getting more complex. Traditional data mining challenges relate not only to high levels of effort and large number of duplicates but also with the specificity of search queries and errors in search methods that result in the loss of requisite information. Let’s take a look at two major types of challenges.
The first type of challenge is a by-product of the skill level of researchers. Typically, search phrases comprising keywords and Booleans are used for running queries and extracting data. While generating queries in this format, the synonyms of important key words are also considered for use within the same or a different query. Generating relevant and comprehensive output using search phrases therefore requires the expertise of skilled data scientists. However, many researchers are not scientifically trained to work on literature and database search. The result: higher chances of retrieving irrelevant information or loss of required information.
The second type of challenge in traditional search pertains to the effort required across multiple activities in the data mining process. The magnitude of effort depends on four factors: number of keywords and synonyms used, number of databases and search engines in play, the formats in which each of the database or the search engine accepts the query, and the skill of the person conducting the query. When a search is conducted using keywords and its synonyms across different databases and search engines, there may be overlaps, resulting in duplicate records in the final output.
Why AI-based semantic search is the answer?
How can organizations make the process simple and crisp by reducing the skill requirements and the need to create multiple queries for synonyms or specific search engines? The answer lies in using sematic queries. A semantic query is built using artificial intelligence (AI)-based natural language processing (NLP). This means the query can be built in the user’s own language - in a conversational format - without the limitation of words and Boolean logic. This characteristic of the sematic query also makes it usable across all the databases and search engines, eliminating the need to construct queries in multiple formats.
The artificial intelligence component automatically creates meaningful search phrases of the users’ words and converts it into the required keyword and Boolean format. It also uses the synonyms of the key words provided in the semantic query statement for building queries in the background. The result: simplified search process that saves time and effort, and enhanced quality of results for superior decision making.
The use of AI affects another aspect of the semantic query-based search. The semantic query can be automatically built by the system from the designated fields in the process flow. For example, in case of a vigilance system, the fields related to the complaint terms and the product can be used along with other predefined fields to automatically construct the semantic query. This method is extendable for regulatory requirements such as continuous literature or social media monitoring to identify any complaint or adverse event related to a manufacturer’s product.
Yet another application of the AI-based system is constructing the query from previous learnings. In this case, AI uses predictive intelligence to add or modify the query. For example, the system can continuously monitor the public domain information on regulatory changes. If any change is observed, it can self-learn information about the change at a high level - such as the geography or regulatory agency, the type of change, date of release, and the impacted areas in the regulatory process.
The inevitable shift towards intelligent search
As opposed to text-based search, NLP and AI can create a frictionless and seamless user experience while retrieving the most pertinent results - by understanding the intent and context around the query. At a time when the pharma industry is under pressure to ensure compliance and deliver superior customer experience, semantic search represents the next frontier in retrieving information in a more agile manner across various activities and functions.