Abstract: A method for pattern discovery and real-time anomaly detection based on knowledge graph, comprising: based on a dataset including messages collected within a certain period, constructing a local knowledge graph (KG); applying a statistical relational learning (SRL) model to predict hidden relations between entities to obtain an updated local KG; from all SPO triples of the updated local KG, discovering a normalcy pattern that includes frequent entities, frequent relations, and frequent SPO triples; and in response to receiving streaming data from a message bus, extracting a plurality of entities, a plurality of relations, and a plurality of SPO triples, from the streaming data for comparison with the normalcy pattern using semantic distance, thereby determining whether there is an abnormal entity, relation, or SPO triple in the streaming data.

Abstract: A method for pattern discovery and real-time anomaly detection based on knowledge graph, comprising: based on a dataset including messages collected within a certain period, constructing a local knowledge graph (KG); applying a statistical relational learning (SRL) model to predict hidden relations between entities to obtain an updated local KG; from all SPO triples of the updated local KG, discovering a normalcy pattern that includes frequent entities, frequent relations, and frequent SPO triples; and in response to receiving streaming data from a message bus, extracting a plurality of entities, a plurality of relations, and a plurality of SPO triples, from the streaming data for comparison with the normalcy pattern using semantic distance, thereby determining whether there is an abnormal entity, relation, or SPO triple in the streaming data.

Abstract: Presented are a system, method, and apparatus for automatic topic relevant content filtering from social media text streams using weak supervision. A computing device utilizes heuristic rules allowing topic filtering and a data stream data chunk identifier. A plurality of messages are transmitted as streaming message data from a social media network in real-time. The messages are split into a plurality of data stream data chunks according to the data stream data chunk identifier. A rule-based labeled data set L0 is built from one or more data instances in the first stream data chunk. An initial classifier is built based upon features of L0. The initial classifier is applied to a next data stream data chunk to build a labeled data set L1. A subset of representative instances S1 is selected from labeled data set L1. A first representative classifier C1 is constructed from representative instance S1.

Abstract: Presented are a system, method, and apparatus for automatic topic relevant content filtering from social media text streams using weak supervision. A computing device utilizes heuristic rules allowing topic filtering and a data stream data chunk identifier. A plurality of messages are transmitted as streaming message data from a social media network in real-time. The messages are split into a plurality of data stream data chunks according to the data stream data chunk identifier. A rule-based labeled data set L0 is built from one or more data instances in the first stream data chunk. An initial classifier is built based upon features of L0. The initial classifier is applied to a next data stream data chunk to build a labeled data set L1. A subset of representative instances S1 is selected from labeled data set L1. A first representative classifier C1 is constructed from representative instance S1.