| |December 201919Babelfish NLP solves these NLP limitations by using a proprietary algorithm for semantic role labelling which allows in extracting meaning from queries10 MOST RECOMMENDED SOLUTION PROVIDERS- 2019BUSINESS INTELLIGENCE er to learn and be contextual. The current techniques used in NLP use-cases are limited because the query dimensions are limited; it works purely on keyword match and the queries are pre-populated with fixed keys. This creates a lim-itation with the kind of answers you can expect. Most NLP queries are descriptive and are not build-ing for predictive or cause-based queries. Babelfish NLP solves these NLP limitations by using a proprietary algorithm for seman-tic role labelling which allows in extracting meaning from que-ries. This algorithm is integrated to the knowledge graph to achieve multidimensional analysis cov-ering predictive, prescriptive and causal analytics.Additionally, Babelfish learns user behavior which extracts key-words and events from user que-ries to learn the topic of interest of a particular user. Based on these topics, the machine can discover anomalies, or personalize pre-dictions or insightful recommen-dations, making the experience completely contextual for a given user.Data discovery comes as one of the current hot-test trends in business intelligence but it is be-coming more and more complicated by nature. In what better ways does Babelfish BI system con-verge, simplify and filter raw data for predictive analysis for businesses?Babelfish BI uses an explicit data model (Knowledge Graph) to uni-fy available data sources of an en-terprise. It is more of a designed experiment than a random obser-vational study. During the data preparation phase, we ensure that all the physical data sources are mapped to the nodes of the model. This data organization uses streaming pipelines to run analytical workflows to compute and update the aggregate nodes, so it is insight ready for real-time insight delivery. Data stored se-quentially by time (time series) allows in extracting temporal patterns from past data, which is used to predict the next probable event or value. These values are further compared globally and prioritized using a collaborative filtering algorithm. As we can ex-pect more predictions either as a sales forecast or a probable prod-uct recommendation, Babelfish can configure these analytical workflows within the system so we can answer NLP questions that require to be predicted.Further, tell us about your future innovation goals.Babelfish's primary goal is Data Democratization for all type of business. We are in the process of offering NLP wrappers that work seamlessly with point applica-tions like CRM, ECom, Martech and ERP allowing businesses to reverse integrate all their data sources to a single knowledge graph. This will help provide uni-fied insights from data federated from multiple data sources that are collected from point applications.
< Page 9 | Page 11 >