Meaning Based Computing

The last few years have seen explosive growth in the use of unstructured information, which includes documents, emails, telephone conversations and multimedia. More than 80% of all information inside an enterprise is now unstructured and comprises the majority of Electronically Stored Information (ESI). This "human-friendly" information has traditionally been difficult for computers to understand. Meaning Based Computing solves this problem.

Meaning Based Computing enables eDiscovery, archiving and policy management software to understand the relationships that exist between disparate pieces of information and perform sophisticated analysis operations automatically. For example, concepts contained in an email can automatically be linked to a recorded phone conversation which can be associated with a stock trade. Or, all information in an archive can automatically be categorized based on related concepts contained in hundreds of different file formats. A compliance policy can be enforced automatically based on the software understanding that a patient's medical history is contained in a file.

How is Meaning Based Computing Different from Traditional Methods Like Keyword Search?

Keyword search engines cannot comprehend the meaning of information, so they only find documents in which a specific word occurs. Unfortunately, this inability to actually understand information means that other documents that discuss the same idea (i.e. are relevant) but use different words, are often overlooked. Equally, documents with a meaning entirely different to that which the user searches for are frequently returned, forcing the user to alter their query to accommodate the search engine.

While keyword search is still extremely useful in legal cases, it cannot give lawyers and investigators a 360 degree view of their organization's information. Meaning Based Computing extends far beyond these traditional methods, forming an understanding of context and concepts, extracting meaning rather than just individual words. In addition, some of the key functionality of Meaning Based Computing, such as automatic hyperlinking and clustering are simply not available in keyword search engines. For example, automatic hyperlinking which connects investigators to a range of pertinent ESI that is contextually linked to the original document, requires that the meaning of the original document is fully understood. Similarly for computers to automatically collect, analyze and organize information, computers have to be able to extract meaning. Only Meaning Based Computing software can do this.

How Does Meaning Based Computing Work?

Meaning Based Computing identifies the patterns that naturally occur in text, voice or video files based on the usage and frequency of terms that correspond to specific concepts. By studying the preponderance of one pattern over another, Autonomy's technology understands that there is X% probability that the content in question deals with a specific subject. In this way, Autonomy extracts the content's digital essence, encodes the unique "signature" of the concepts, and enables a host of operations to be automatically performed on emails, phone conversations, video, documents, and even people's interests.

Who is Using Meaning Based Computing?

More than 17,000 blue-chip corporations and government agencies rely on the powerful pattern-matching algorithms in Autonomy's products to extract meaning from unstructured information. For example, the US Department of Homeland Security uses Meaning Based Computing across 21 agencies to monitor suspected terrorist groups, create a consolidated terrorist watch list, and alert authorities in real-time to potential terrorist activity. Zurich Financial Services, which has offices in more than 60 countries, uses Meaning Based Computing to prioritize research from more than 500 sources so risk managers can uncover potential threats and opportunities.