Computer-implemented techniques for entity disambiguation using graph neural networks (GNNs) are provided. According to an embodiment, computer implemented method can comprise receiving, by a system operatively coupled to a processor, an unstructured text snippet comprising an unknown term. The method further comprises employing, by the system, a heterogeneous GNN trained on a knowledge graph associated with a domain of the unstructured text snippet to facilitate identifying one or more similar terms included within the knowledge graph for the unknown term.

The present disclosure provides systems and methods for generating a graph of microservices of a software application. Source code for the application may be parsed using one or more method identifiers to identify a plurality of methods of the software application. Names and path values for the plurality of methods may be stored in a graph database as nodes and edges. The graph database may be queried to determine dependencies of a method, including the microservice that the method belongs to any remote methods called by the method. In addition, properties of the nodes may be transformed into a set of vectors to provide searching of the graph and recommendations.

A code base is parsed to identify methods having changes in a code base since a last code commit. Thereafter, a call graph is traversed to identify test cases implicated by the identified methods having changes in the code base. The call graph can be a directed call graph comprising a plurality of connected nodes in which a first subset of the connected nodes are method nodes representing each method in the code base in which unidirectional edges connecting method nodes correspond to invocations by a calling method to a callee method, and in which a second subset of the connected nodes are test case nodes representing each of a plurality of available test cases to test the code base. The test case nodes are each coupled to one or more method nodes by unidirectional edges that correspond to the test case coverage of the method.

A device can receive a search query. The search query can relate to a geographic location. The device can process the search query to parse the search query into a plurality of strings. The plurality of strings can include text. The device can determine that one or more strings, of the plurality of strings, satisfy a condition. The device can generate, using the plurality of strings, a query graph that includes a plurality of paths relating to the condition. The query graph can further include a plurality of nodes leading to and/or included in the plurality of paths, and a plurality of transitions between the plurality of nodes. The device can perform one or more actions relating to the query graph to obtain, from a data structure, one or more search results that correspond to the search query.

Systems and methods for improving evaluation of graph queries through depth first traversals are described herein. In an embodiment, a multi-node system evaluates against graph data a graph query that specifies a particular pattern to match by determining, at a first node of the multi-node system, in a particular instance of evaluating the graph query, that one or more first vertices on the first node match a first portion of the graph query and that a second vertex that is to be evaluated next is stored on a second node separate from the first node. In response to determining that the next vertex to be evaluated is stored on the second node separate from the first node, the first node generates a message to the second node comprising one or more results of the first portion of the graph query based on the one or more first vertices, an identifier of the next vertex, and a current stage of evaluating the graph query. In response to generating the message from the first node to the second node, the first node ceases the particular instance of evaluating the graph query.

A building system of a building including one or more memory devices having instructions thereon, that, when executed by one or more processors, cause the one or more processors to identify a capability of an entity in a building graph, the building graph including a plurality of nodes and a plurality of edges, the plurality of nodes representing entities of the building including the entity and the capability, the plurality of edges representing relationships between the entities of the building and the capability. The instructions cause the one or more processors to receive a command to perform an operation associated with the entity, the operation associated with the capability of the entity and provide the command to perform the operation associated with the entity to an operating system.

A method of improved input-output searching includes receiving a content file comprising multiple input variables, multiple functions, and multiple output variables. The output variables and the input variables include overlapping variables. The content file is converted into a tree. The tree is converted into a graph for an input-output search. The converting the tree into the graph comprises: traversing the tree to identify each function in the tree. For each identified function, the identified function is assigned as a vertex of the graph. Additionally, any input variables and output variables associated with that function are determined. Finally, the determined input variables and output variables associated with that function are assigned as vertices of the graph connected to the vertex having the assigned identified function by directional edges.

Techniques described herein allow a user of an RDBMS to specify a graph algorithm function (GAF) that takes a graph object as input and returns a logical graph object as output. GAFs are used within graph queries to compute temporary and output properties (“GAF-computed properties”), which are live for the duration of the query cursor execution. GAF-computed output properties are accessible in the enclosing graph pattern matching query as though they were part of the input graph object of the GAF. Temporary cursor-duration tables are generated for the query cursor during compilation of a graph query that includes a GAF, and are used to store the GAF-computed properties. Each temporary table corresponds to one of the primary tables of the input graph, and includes, as a foreign key, primary key information from the corresponding primary table. Thus, the input graph of a GAF may be a “heterogeneous” graph.

A method includes accessing a schema that specifies relationships among datasets, computations on the datasets, or transformations of the datasets, selecting a dataset from among the datasets, and identifying, from the schema, other datasets that are related to the selected dataset. Attributes of the datasets are identified, and logical data representing the identified attributes and relationships among the attributes is generated. The logical data is provided to a development environment, which provides access to portions of the logical data representing the identified attributes. A specification that specifies at least one of the identified attributes in performing an operation is received from the development environment. Based on the specification and the relationships among the identified attributes represented by the logical data, a computer program is generated to perform the operation by accessing, from storage, at least one dataset having the at least one of the attributes specified in the specification.

A method includes accessing a schema that specifies relationships among datasets, computations on the datasets, or transformations of the datasets, selecting a dataset from among the datasets, and identifying, from the schema, other datasets that are related to the selected dataset. Attributes of the datasets are identified, and logical data representing the identified attributes and relationships among the attributes is generated. The logical data is provided to a development environment, which provides access to portions of the logical data representing the identified attributes. A specification that specifies at least one of the identified attributes in performing an operation is received from the development environment. Based on the specification and the relationships among the identified attributes represented by the logical data, a computer program is generated to perform the operation by accessing, from storage, at least one dataset having the at least one of the attributes specified in the specification.