Matching jobs

Background to our work lies in a rule engine that we have developed at Profium. The rules we support are in the expressive power of Datalog. This means new facts can be inferred from existing facts. The goal of this article is to initiate discussion about possible application areas for an algorithm we have developed. Read more

Artificial Intelligence and intelligent Fraud Detection

The Coalition Against Insurance Fraud estimate that frauds costs $80 billion a year across all lines of insurance. In a highly competitive industry reducing such a cost is an obvious path to improving profitability. Any reduction in fraud will directly impact an insurance company’s bottom line. Companies have invested into IT to streamline their claims processing. Sometimes an automated claim should merit human analysis. Intelligent Fraud Detection is a solution that helps you determine the conditions or rules that stop claims that may be grounded on false data or where the data is correct but the underlying elements of the claim are not justified. Profium Sense for Intelligent Fraud Detection is an ideal tool for this purpose. Data used by the rules to detect fraud may come from a diverse range of sources such as photographic metadata, geographical data and social media. With Profium solution your analysts can develop rules which take different fraud scenarios into account and save those rules to test and production environments without delays from IT. Read more

Semantic Search in employment matching

Job portals have become a popular place for corporations to place their ads for new vacancies in the hope that skilled individuals would find them and apply for them. Often such portals provide traditional search interfaces based on forms. Both applicants and employees will then have to try several values for fields such as title to see if there are any interesting results that match their criteria. This traditional search can be greatly improved with semantic search. A new growing trend for semantic employment matching is skill based matching. Read more

Artificial Intelligence (AI) powered graph computing and semantic big data

In the era of 'big data' software systems are commonly dealing with large amounts of complex data which may be generated by a diverse range of sources. Conventional relational databases require specifying the structure of the data up-front, and the complexity of the database design rapidly increases with the complexity of the relationships between concepts in the data. Alternatively, a graph database can come closer to modeling the real-life relationships between data and represent data in a structure that more closely models real world relationships between concepts. Profium Sense, a NoSQL in-memory graph database and Rule Engine, processes real-time flows of data, and offers rule-based inferencing and Semantic AI for real time content distribution. Read more

Graph evolution from the 90s

"I have seen approaches such as deductive databases where the graph can be modelled as a logic program using binary relationships, I've seen topic map implementations, I've seen proprietary implementations and I've guided myself a semantic web based graph database implementation at Profium." Read more

Rules from regulations

This blog post examines regulative environments and how they can be used to distill formal rules that drive applications which make it easier for human beings to make correct decisions. Read more