Organized Serendipity: Powering Your Lab with Semantic Search

Serendipity, the miracle of making fortunate discoveries by accident, has long been recognized as a critical catalyst for innovation. Some of the most famous have included smallpox vaccination (including the very idea of vaccination itself), X-rays and penicillin.

Powering Your Lab with Semantic SearchAs ground-breaking as such discoveries can be, however, relying solely on chance encounters isn’t a sustainable approach to fueling growth and progress. Fortunately, recent advances in artificial intelligence (AI) have inspired a better way to uncover the hidden links that can lead to major breakthroughs: semantic search.

In our last blog, we explored how seamlessly integrating a semantic search engine into R&D workflows offers the promise of accelerating precision medicine. In this post, we’ll delve more deeply into how semantic search and related cutting-edge technologies can foster innovation by nurturing organized serendipity.


The Search for Better Search

Until about a decade ago, the only way to hunt through vast digital datasets was a technique known as “lexical” search. Search engines looked for literal matches of the search terms: either letter-by-letter duplicates or common variations. For example, you might get matches including the name “Sue” if you search for “Susan.” But while a lexical search engine can recognize words by matching sequences of letters, it does not understand of the meaning behind the words.

This innovation transformed search by enabling computers to understand natural human language better. Type the phrase “I’ll be back” into a search engine today and you won’t just get results related to Arnold Schwarzenegger and the movie The Terminator. The engine will also know that these iconic words have something in common with “Hasta la vista, baby,”, “Go ahead, make my day,” and “Houston, we have a problem.”

In this way, semantic search is helping Google give us more of the answers we’re really looking for. But the level of semantic search is limited from a research perspective. Cultivating a more fertile environment for innovation in pharma research requires an even deeper application of this technology.


Revolutionizing Semantic Search and Discovery

Advanced forms of semantic search are beginning to empower R&D organizations to make new and exciting breakthroughs while reducing the time needed to seek and analyze information. Leveraging explainable AI, natural language processing, and a rich knowledge base can help researchers identify and connect seemingly unrelated data points, transforming unexpected findings into valuable insights and opportunities.

“We are excited to work with Biomax to effectively manage and share knowledge across our Science and Innovation community,” says Dorus van der Linden, Head of Knowledge Management and Sharing at DSM, a company specializing in health and nutrition based in the Netherlands.

“Based on our previous conventional keyword search we only retrieved 45% opposed to 95% of our R&D documents with Biomax’ semantic search platform. Knowledge which is otherwise hidden and disconnected is now accessible to our scientists and this saves us time and resources for gathering relevant information,” van der Linden said.

Results like these are being made possible by the integration of semantically structured and unstructured data from a wide array of sources, including proprietary research initiatives, scientific literature, patent databases, and clinical trials, as well as external databases like ChEMBL, PubChem, UniProtKB, PDB, and Open Targets. This comprehensive data integration enables organizations to tap into a vast pool of knowledge, accelerating development cycles and enhancing the quality of their products and services.


Unveiling the Hidden Connections with Knowledge Graphs

Specialized knowledge graphs designed specifically for research lie at the heart of innovative approaches to fostering organized serendipity. These graphs are structured, interconnected networks of concepts, entities, and relationships that represent real-world knowledge in a machine-readable format. Knowledge graphs enable users to discover patterns, make connections, and derive new insights from seemingly unrelated data points.

Knowledge graphs serve as powerful tools for semantic integration by providing a comprehensive map of the contextual landscape and connecting disparate data sources. The result is a continuously growing global knowledge graph that reveals the semantic relationships between various data points, allowing researchers to infer hidden knowledge from explicit information.


Bridging the Data-to-Insights DivideBridging the Data-to-Insights Divide

The power of a knowledge graph is further enhanced when it is used as a “semantic layer” that sits on top of diverse data streams, data lakes, and data warehouses. This semantic layer serves as a metadata management system, providing identity, classification, and contextualization for all the different points in these sources.

The semantic layer plays a critical role in enabling end users to access the right information at the right time across vast amounts of enterprise and public data through query and question-answering capabilities. By adhering to FAIR (Findable, Accessible, Interoperable, and Reusable) data principles, the semantic layer facilitates the reusability of digital assets typically collected within the life sciences, allowing for the rapid development of dedicated apps for specialized use cases and creating well-understood analytical data marts for downstream processing.


Fostering Organized Serendipity with an Engine of Innovation

LabVantage Biomax is closing the gap between the current value R&D organizations are deriving from data and the potential value they can achieve with AILANI (Artificial Intelligence LANguage Interface), a semantic integration and search platform.

By integrating structured and unstructured data from a multitude of sources, AILANI enables researchers to access a vast knowledge base that would otherwise be inaccessible and siloed. The platform isn’t limited to text analysis — information can also be extracted from charts, graphs, chemical formulas and other images using optical structure recognition. Its specialized knowledge graph, in turn, reveals the contextual relationships between various data points, allowing researchers to draw connections and derive insights from seemingly unrelated information. Data is not prioritized by popularity but validated by the amount of supporting evidence associated with it.

The semantic layer’s ability to bridge the gap between raw data and actionable insights further amplifies the potential for organized serendipity. By making complex datasets more accessible and understandable, the semantic layer empowers researchers to uncover hidden patterns and make informed decisions, driving innovation and growth.

With its knowledge graph-powered semantic layer, AILANI is a game-changer in fostering innovation by nurturing organized serendipity. By harnessing the power of advanced technologies such as explainable AI, natural language processing, optical structure recognition and semantic integration, AILANI unlocks new opportunities for organizations to capitalize on unexpected connections and unbiased insights.

To learn more about AILANI, check out this short introductory video. If you’re ready to embrace the future of data-driven decision-making, contact us today.