Expert Identification

Expertise is not easily identified and is even more difficult to manage on an ongoing basis, which leaves vast resources of tacit knowledge and experience untapped. There is growing recognition that access to these types of implicit information is critical to the efficient running of enterprise operations. Senexx Expert Map Builder builds communities of expertise to promote collaboration and fuel innovation.

By forming a conceptual understanding of user interaction with information as it is consumed and created, Senexx’s technology identifies tacit knowledge automatically and in context. It builds a conceptual understanding of the relationships between experts and the content with which they interact, automatically clustering similar people and resources into related groups. By integrating into existing business tools from IM, wikis and workflow applications to emails, each of which has its own incompatible proprietary expertise repository, non-uniform schema and distinctive interfaces.

Unstructured Content Analysis

More than 90% of all data in an enterprise is unstructured information. This encompasses telephone conversations, voicemails, emails, electronic documents, paper documents, images, web pages, video and hundreds of other formats. Unfortunately, attempts to leverage this immense and strategic resource often fail because many businesses lack the requisite technology to understand and effectively utilize content that resides outside the scope of structured databases.

Senexx's technology is based on Natural Language Processing and different statistical heuristics. Senexx sees search as not merely looking for a word, but looking for an idea. Therefore, although Senexx embraces traditional methods such as keyword, Boolean, parametric and others in developing its search strategy, it relies on conceptual search and contextual linguistic analysis to deliver the most relevant results.

Natural Language Processing

Natural language processing (NLP) is concerned with the interactions between computers and human (natural) languages. The main purpose for it is to enable computers to derive meaning from human or natural language input.

Modern NLP algorithms are based on machine learning, especially statistical machine learning. The paradigm of machine learning is different from that of most prior attempts at language processing. Prior implementations of language-processing tasks typically involved the direct hand coding of large sets of rules. The machine-learning paradigm calls instead for using general learning algorithms — often, although not always, grounded in statistical inference — to automatically learn such rules through the analysis of large corpora of typical real-world examples. A corpus (plural, "corpora") is a set of documents (or sometimes, individual sentences) that have been hand-annotated with the correct values to be learned.

What customers are saying about Senexx

“We used Yammer, but we decided to look for something a bit more focused. We chose Senexx!”
Bas Muller, KplusV Organization

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