36 results about "Markov logic network" patented technology

The invention discloses a deep layer data processing method combined with a knowledge base. The method comprises the following steps that concept tuple sets in a body base are merged; link routes of predicates in different types and corresponding logic rule sets are obtained; the logic rule sets are screened preliminarily, candidate rule sets are obtained; a deep layer probabilistic graphical model is obtained; structuring tuples are obtained based on data to be processed and are mapped to a layering concept space; target tuples are generated, semantic extension is carried out; logic rule sets and evidence tuple sets are obtained; a Markov logical net is subjected to instantiation, the conditional probability for target tuple founding is computed, and data processing results are obtained. The invention further provides a deep layer data processing system which comprises a structuring module, a conceptualization module, a target generating module, an extension module, an activating module and a probability computing module. Context and background knowledge can be fully merged, and accordingly, the purpose of semantic comprehension is really achieved.

The invention relates to a Markov logic network-based knowledge mapping relationship type speculation method and device. The device comprises an inference rule obtaining module, a credibility weight learning module, a probability inference module and a relationship type determination module, wherein the inference rule obtaining module is used for generating inference rules according to path features of known nodes of a data knowledge mapping; the credibility weight learning module is used for carrying out credibility weight learning on the inference rules through a Markov logic network and obtaining inference rules with weights; the probability inference module is used for carrying out probability inference on relationship types existing among to-be-speculated nodes according to the inference rules with the weights, so as to obtain relationship type probability among the to-be-speculated nodes; and the relationship type determination module is used for selecting a relationship type with a relatively large probability value as a relationship type among the to-be-speculated nodes according to the relationship type probability obtained by the probability inference module. According to the method and device, the automatic learning of the inference rules in the knowledge mapping and the probability inference of relationship types among nodes can be realized, so that the correctness of speculating the relationship types which possibly exist among the nodes can be effectively ensured.

The invention discloses a text analysis method and device for large-scale multilingual data, and the method comprises the steps: collecting large-scale multilingual text data, and storing the large-scale multilingual text data in a corresponding database; Performing entity matching on the multilingual text data in the database by utilizing a Markov logic network; using the ML-PIB algorithm to carry out clustering analysis on the matched multilingual text data to obtain a target clustering result among the different language information, so that the association contained in the different language information is effectively mined, and the clustering quality is improved.

The invention comprises a system and article of manufacture to discover potential cyber threats on Twitter. The invention provides a unified framework for modeling and reasoning about the veracity of tweets to discover suspicious users and malicious content. The invention builds on the concept of Markov logic networks (MLNs) for knowledge representation and reasoning under uncertainty.

The invention relates to a Markov logic network-based knowledge mapping relationship type speculation method and device. The device comprises an inference rule obtaining module, a credibility weight learning module, a probability inference module and a relationship type determination module, wherein the inference rule obtaining module is used for generating inference rules according to path features of known nodes of a data knowledge mapping; the credibility weight learning module is used for carrying out credibility weight learning on the inference rules through a Markov logic network and obtaining inference rules with weights; the probability inference module is used for carrying out probability inference on relationship types existing among to-be-speculated nodes according to the inference rules with the weights, so as to obtain relationship type probability among the to-be-speculated nodes; and the relationship type determination module is used for selecting a relationship type with a relatively large probability value as a relationship type among the to-be-speculated nodes according to the relationship type probability obtained by the probability inference module. According to the method and device, the automatic learning of the inference rules in the knowledge mapping and the probability inference of relationship types among nodes can be realized, so that the correctness of speculating the relationship types which possibly exist among the nodes can be effectively ensured.

The invention discloses a deep layer data processing method combined with a knowledge base. The method comprises the following steps that concept tuple sets in a body base are merged; link routes of predicates in different types and corresponding logic rule sets are obtained; the logic rule sets are screened preliminarily, candidate rule sets are obtained; a deep layer probabilistic graphical model is obtained; structuring tuples are obtained based on data to be processed and are mapped to a layering concept space; target tuples are generated, semantic extension is carried out; logic rule sets and evidence tuple sets are obtained; a Markov logical net is subjected to instantiation, the conditional probability for target tuple founding is computed, and data processing results are obtained. The invention further provides a deep layer data processing system which comprises a structuring module, a conceptualization module, a target generating module, an extension module, an activating module and a probability computing module. Context and background knowledge can be fully merged, and accordingly, the purpose of semantic comprehension is really achieved.

The invention discloses an unsupervised data automatic cleaning method, which includes the following steps: A. Data model learning, learning the dependency relationship between attributes from the original data that may contain invalid data, by finding out the implicit non-absolute Or relatively weak dependencies, to obtain a data model expressed in the form of a Bayesian network; B. Generation of data cleaning rules; after obtaining the complete data model of the original data or original data sampling, the data cleaning rules are generated , and specifically generate predicates and first-order predicate rules; C. Generate a Markov logic network based on the predicates and first-order predicate rules generated in step B; D. Generate inference rules based on the Markov logic network generated in step C And data cleaning based on inference results. The method of the invention can effectively improve the data quality of various business systems of the company without consuming a lot of manpower and material resources, and helps the management to make correct decisions.

The invention provides a Chinese microblog sentiment classification method and system based on a Markov logic network. The method comprises the steps that microblog data are obtained from the network; predicate and rules used for expressing sentiment classes are determined according to basic knowledge of the markov logic network, corresponding characteristic words are extracted from the microblog data according to a feeling and emotion dictionary, and a MLNs knowledge base is established according to the characteristic words; according to the MLNs knowledge base and predicate and rule training classifiers, sentiment classification is conducted on the microblog data, and Chinese microblog sentiment classification results are output. The method and system can accurately classify sentiments of microblog.

The present invention provides a method for recognizing indoor daily activities in a scene of multiple occupants, capable of identifying daily activities in a scene of multiple occupants. The method includes: constructing an ontology model of ADL, sensors, and environmental context, and adding user portraits and activity timing relationships into the constructed ontology model, wherein ADL represents daily activities; according to the constructed ontology model, establishing ADL rules, and Assign weights to each ADL rule; obtain the daily activity data collected by the sensor, based on the data collected by the sensor, the constructed ontology model and the established ADL rules, based on the Markov logic network, the reasoning and analysis of daily activities in multi-occupant scenarios can be realized. identify. The invention is suitable for recognizing the daily activities of indoor occupants.

The invention provides a news content sensitive word filtering method and system. The method comprises the steps of S1, preprocessing obtained news texts; S2, filtering the sensitive words of the news texts by employing a sensitive word multi-level filtering algorithm on the basis of the priorities of the sensitive words according to a pre-established sensitive word library; S3, judging the filtered sensitive words through an emotion analysis model based on a markov logic network when there are preset sensitive words in the news texts; and S4, marking the news texts as negative news when it is judged that the filtered sensitive words are bad sensitive words, otherwise, marking the news texts as positive news. According to the method and the system, secondary judgment is carried out on the filtered sensitive words through establishment of the emotion analysis model based on the markov logic network, thereby determining whether the filtered sensitive words have negative information or not; therefore, the negative news is filtered; moreover, the positive news fighting against the negative information will not be filtered; and the reliability of filtering the news content sensitive words can be improved.