Cognition Search Has Launched - Cognition Technologies

The combination of formal linguistic algorithms with semantic representations is at the core of Cognition's patented technology and combine to create a fast semantics via computational parsing. What makes this works is the use of 4.000.000 semantic representations, 350,000 word stems, 376,000 word senses or concepts, 17,000 ambiguous word definitions, 100,000 phrases, 7,000 nodes for the ontology or tree structure of the taxonomy, and 50,000 thesaural concept groups. Cognition's clients using the software are provided with tools to add their own specialized terminology. In the case of very large term expansions, Cognition will also supply them with a consulting service to augment the CognitionSearch service.

The Advanced Search mode will seem familiar to professional searchers with a lot of experience dealing with database services. The Advanced Search mode offers five basic search approaches: plain English search, linguistic Boolean search, quoted search, pattern search, and fuzzy search.
Another thing is the timeline to achieve results, like in the short, mid, or long-term. No unrealistic expectations here. One way I like to approach this is to optimize by levels. For example, I will choose 3 levels of difficulty and select the according keywords.
As I achieve the easiest level, I move on to the next and so on.

The Semantic Indexing Project (SEO)
Semantic indexing is our name for a family of techniques for searching and organizing large data collections. The goal of semantic indexing is to find patterns in unstructured data (documents without descriptors such as keywords or special tags) and use those patterns to offer more effective search and categorization services.

During the past five years, a team of linguists and computer scientists at NITLE and Middlebury College has developed a prototype Semantic Engine. This prototype was designed to address the universal problem of accessing and organizing large amounts of unstructured digital text. Using mathematical algorithms to index the latent semantic content of documents, the prototype engine has been demonstrated to drastically reduce, if not eliminate, the need for expensive and time-consuming metadata tagging, and to produce results superior to keyword searches in limited test domains.

The Semantic Engine is at the center of our research and development mission. It is designed to enable scholars and educators to manage the already overwhelming and ever-increasing volume of data that we encounter in every field of inquiry. We are developing a set of tools that will enable researchers to quickly search large datasets that may be distributed across multiple databases, to interact with the engine to refine the search, and to contribute their knowledge to the collection. We also are creating a set of visualization and archiving tools for the researcher to use, to facilitate the organization and dissemination of the search results.