TalentBrowser’s search engine uses Domain-Specific Language Processing (DSLP) and patented Weighted Topic Scoring (WTS) with fully editable topics and keywords to deliver industry-specific results you can see, control and measure powered by DataScava’s unstructured data mining technology.
Customizable to any business, TalentBrowser uses your own finely-tuned searches to delivers strong matches and excels at jargon-intensive specialty jobs in technology, finance, healthcare and more.
Navigational vs. Research Search
Ramanathan V. Guha is responsible for products such as Google Custom Search. In their paper on Semantic Search, he and his colleagues distinguished between two major forms of search, navigational and research:
“Before getting into the details of how the Semantic Web can contribute to search, we need to distinguish between two very different kinds of searches.
In navigational search, the user is using the search engine as a navigation tool to navigate to a particular intended document. In this class of searches, the user provides the search engine a phrase or combination of words which s/he expects to find in the documents. There is no straightforward, reasonable interpretation of these words as denoting a concept. In such cases, the user is using the search engine as a navigation tool to navigate to a particular intended document. We are not interested in this class of searches.
In research search, the user provides the search engine with a phrase which is intended to denote an object about which the user is trying to gather/research information. There is no particular document which the user knows about and is trying to get to. Rather, the user is trying to locate a number of documents which together will provide the desired information. Semantic search lends itself well with this approach that is closely related with exploratory search.”
This article on Github What is Semantic Search? references Guha’s work and discusses why Semantic Search is not suitable to navigational search.
When a person “navigates” to a resume or profile for recruitment and other people initiatives, they are trying to find the best overall “match.” They are not interested in “researching” all resumes that have a mention of any one of their topics of interest.
TalentBrowser excels at Navigational Search.
Domain-Specific Language Processing
We don’t use off-the-shelf semantic toolkits, NLP, fuzzy logic or machine learning. The system mines the unstructured text on resumes and profiles using Domain-Specific Language Processing (DSLP) and Weighted Topic Scoring, which allows users to set minimum “required” and “nice-to-have” score thresholds to be met in each topic, and produce highly precise match results.
People who may match a one-dimensional Boolean search but lack the depth of experience in key skills required to do the job are filtered out. In addition, the high percentage of false matches associated with Semantic Search, a frequent complaint of recruiters who use it, disappears.
With TalentBrowser, what you ask for is what you get.
Encapsulate Business Intelligence
Our users are excited at how their specific requirements and nuances of ongoing needs are encapsulated in their own customizable search engine that’s available at the earliest stage of the decision-making process.