Home Events Source:GTCOMDate: 1 August 2018views:4426
The traditional data-management modes and query methods have become subject to certain constraints in the era of big data, particularly due to the cross-application of multiple data sources. By comparison to conventional databases, knowledge-graph databases can quickly call the relationships between data, assist with data analysis and obtain results through systematic reasoning so as to lower the threshold of use and enhance the efficiency of search methods.
Global Tone Communication Technology Co., Ltd. (GTCOM) released JoveMind, a knowledge graph building and analysis platform for enterprise customers, at the "Hi, Five" brand-strategy conference held on July 31, 2018. JoveMind enables visualized data search and analysis based upon the creation of knowledge graphs, providing custom-tailored algorithms and dimensions of analysis for customers in a wide range of industries and application scenarios.
In-depth mining of massive information and the intelligent creation of knowledge graphs
A specific set of rules is used to build rational knowledge so that wise people can attain broader, clearer purviews on the subjects of their interest. So, JoveMind constantly integrates the global data in multiple realms, extracting knowledge from the data in real time. It builds large-scale-enterprise knowledge graphs and provides decision-making support for the challenges of competition, business territory and business association along with warnings of risk to reputation, strategy and credit.
Additionally, JoveMind introduces and creates knowledge graphs in the areas of biopharmaceuticals, financial technology and others, doing so by means of multiple-mode, full-format data integration. It uses cross-language knowledge fusion and reasoning techniques to facilitate the perfect correlation and integration of knowledge graphs in different languages and realms, thus customizing different dimensions of analysis such as auxiliary diagnosis, technological foresight and the social context.
Analyzing enterprise correlation and showing the enterprise context
The power and accuracy of AI technology are collectively the premise of knowledge-graph application, which involves the extraction of entities and the establishment of relationships between entities. It is also necessary to organize and store the extracted entities and relationship information so that they can be quickly accessed and operated. Based on GTCOM's technological strengths and data legacies accrued through years of development, JoveMind empowers knowledge portraits for field-specific enterprises, and conducts multidimensional, in-depth correlation analysis for enterprises through the interactive exploration of man-graph information. Thus it offers efficient decision-making support for enterprises in competition analysis, business-territory confirmation, cooperative network identification and growth deduction. For example, if one enters a keyword such as "Musk" in JoveMind, the companies, people, things and objects associated with "Musk" will be displayed, and the relationships between entities--including investment and financing relationship, colleague relationship and executive relationships--can be analyzed.
Moreover, JoveMind can provide enterprises with customized algorithms to accurately process the massive amounts of data that enterprises need. It assists in knowledge-based reasoning calculations, thereby avoiding duplicitous investment.
Deep mining of panoramic graphs; identification and management of enterprise risks
JoveMind delivers the multidimensional integration of fragmented data from different channels to help decision-makers in the financial, insurance and securities markets analyze potential crises in complex relationships. Working through automatic semantic analysis algorithms, it scours large amounts of information to intelligently identify and tag risks to reputation, credit and capital. Moreover, each risk is traceable, which is convenient for enterprises to explore the cause of each risk and accurately control the source of each risk, eliminating any risk in the bud. Additionally, through the use of hotspot clustering JoveMind effectively identifies the scope of influence and diffusion situation for each risk. The enterprise can then manage the risk by determining the direction of PR and control, conducting rapid, real-time data capture and analysis, and issuing risk warnings at the earliest possible moment.
GTCOM CEO Eric Yu, speaking at the conference, took an electric vehicle company as an example to demonstrate the panoramic view and risk-control functions of JoveMind. The hypothetical company, he said, faces various risks in terms of reputation, strategy and credit. Additionally, the relationships of companies, events and people associated with the electric vehicle company were mined, and panoramic enterprise graphs integrating business-clustering information were displayed. Consequently, with JoveMind it is easy to find buried structural relationships and information links so that the enterprise can gain keen foresight.
As intelligent upgrading and transformation have become the predominant demands in all walks of modern life, the shift from IT application to intelligence application has become a hot trend. Relying on the innovation strengths of big-data analysis and AI language, JoveMind has an increasingly important role in promoting the implementation of knowledge graphs as it drives the intelligent development of industries and sectors.