A system for the categorization of interlinked information items, the system comprising: a trust flow module which is configured to receive a seed trust list of one or more first information items, the seed trust list associating the one or more first information items with one or more categories; and a trust flow module configured to: associate a respective trust value with each of the one or more categories for the one or more first information items; and iteratively pass at least part of the or each trust value to one or more further information items to generate, for each of the one or more further information items, at least one accumulated trust value associated with a category of the one or more categories, such that the one or more further information items can be categorized based on the at least one accumulated trust value and associated category.

This application is directed to an indexing system for graph data. In particular implementations, the indexing system uses a database index infrastructure that provides for flexible search capability to data objects and associations between data objects. Particular embodiments relate to an indexing system for storing and serving information modeled as a graph that includes nodes and edges that define associations or relationships between nodes that the edges connect in the graph.

The present application discloses a method of representing semantic definitions on a computing device. Semantic definition statements are composed using operators. The semantic definition statements include semantic concept statements using semantic concept operators and semantic context statements using semantic context operators. The semantic definition statements are saved in a metaset. The metaset is converted into a digital data structure and stored in a memory storage device of a computing device. The present application further discloses a method of semantically searching for a visual using a metaset.

A computing server may receive master data, transaction data, and one or more existing process models of a domain. The computing server may aggregate, based on domain knowledge ontology of the domain, the master data and the transaction data to generate a fact table. For example, entries in the fact table may be identified as relevant to the target process model and include attributes and facts that are extracted from master data or transaction data. The computing server may convert the entries in the fact table into vectors. The computing server inputting vectors into one or more machine learning algorithms to generate one or more algorithm outputs. One or more algorithm outputs may correspond to one or more improved process models that are optimized compared to the existing process models. The computing server may provide the improved process model to the domain to replace one of the existing process models.

In an aspect, a system for generating a virtual assistant in a messaging user interface, the system comprising a computing device designed and configured to initiate a virtual message user interface between a user client device and the computing device; receive a user message entered by a user into the virtual message user interface; select a conversation profile for the virtual assistant as a function of the user message, wherein the conversation profile comprises behavior that the user uses to communicate; prioritize an agenda action of a plurality of agenda actions contained within a user agenda list as a function of the user message; and generate a response to the user message comprising the prioritized agenda action, wherein the response is further generated as a function of the selected conversation profile.

An information retrieval technique uses one or more machine-trained models to generate one or more metadata embeddings. The technique then combines a query embedding with the metadata embedding(s). In some cases, the technique performs this operation using a graph convolution operation. This yields an augmented embedding. The technique then uses the augmented embedding to retrieve at least one item. The augmented embedding lies in the same vector space as target-item embeddings associated with candidate target items. Otherwise, the vector spaces associated with the query embedding and metadata embedding(s) can be different. In some implementations, the technique use dense retrieval, which enables the technique to deliver output results in real time.

Provided are techniques for performing a search using a hypergraph. Entities are identified. A knowledge graph using the entities is generated, wherein nodes of the knowledge graph represent the entities and edges between the nodes represent pair-wise relationships, and wherein each of the edges carries an edge score that quantifies a degree of coherence between a pair of the entities. A hypergraph using the knowledge graph is generated, wherein nodes of the hypergraph represent the entities and hyperedges represent relationships between multiple entities, and wherein each of the hyperedges carries a hyperedge score that quantifies a degree of coherence between the multiple entities. A search request is received. A search result is generated using the hypergraph, wherein the search result comprises a set of coherently related entities. The search result is returned.

A system and method for indexing applications accessible through a user device are provided. The system includes crawling through a plurality of data sources to detect applications accessible through a user device; for each detected application, generating metadata characterizing the application; analyzing the generated metadata to classify the application to at least one category; and updating an application index to include at least the index application and the respective classified category.

Techniques are provided for shard reorganization in sharded storage systems based on a user-specified dimensional description or key range information. An exemplary method processes data in a sharded distributed data storage system that stores data in a plurality of shards by obtaining a dimensional description for a shard reorganization of the data in the sharded distributed data storage system from a user; and reorganizing a storage of the data on one or more nodes of the sharded distributed data storage system based on the dimensional description. The dimensional description comprises, for example, a semantic description of desired array dimensions or key range information. The semantic description of desired array dimensions comprises a striping of a given data array along one or more of a horizontal face, a vertical face and a sub-array of the given data array. The reorganization can be employed, for example, for key-value objects and multidimensional array objects.

Method, system, and programs for hybrid information query. A request is first received from a user associated with a hybrid query. The hybrid query is expressed in accordance with an input in terms of one of a user, a feature, and a document, and a desired hybrid query result in terms of one of a user, a feature, and a document. A mapping is then determined between the input and the desired hybrid query result. A hybrid model is established based on hybrid information collected and associated with one or more users. The mapping is performed based on the hybrid model to obtain the desired hybrid query result based on the input. Eventually, the desired hybrid query result is provided as a response to the hybrid query.