MACHINE LEARNING USING SEMANTIC CONCEPTS REPRESENTED WITH TEMPORAL AND SPATIAL DATA
Various embodiments may include a machine-readable medium, computing device, and/or computer-implemented method for deriving inferences associated with semantic concepts using machine learning. In one embodiment, a machine learning model is trained to derive inferences associated with semantic concepts using a distributed knowledge graph (DKG) data structure. The DKG data structure represents semantic concepts from a training dataset as a set of vectors in a vector space, wherein elements of the vectors correspond to meta-semantic parameters associated with the semantic concepts. The meta-semantic parameters include: a temporal parameter to represent timestamps associated with the semantic concepts; and a spatial parameter to represent physical locations associated with the semantic concepts. An input vector with elements corresponding to the meta-semantic parameters is obtained based on data captured by sensor(s). An inference associated with semantic concept(s) corresponding to the input vector is then derived based on the machine learning model.
1 . A product comprising one or more tangible computer-readable non-transitory storage media comprising computer-executable instructions operable to, when executed by at least one computer processor, enable the at least one processor to:
access a distributed knowledge graph (DKG) data structure stored in memory circuitry, wherein the DKG data structure represents a plurality of semantic concepts associated with a training dataset as a set of vectors in a vector space, wherein elements of the set of vectors correspond to a set of meta-semantic parameters associated with the plurality of semantic concepts, wherein the set of meta-semantic parameters includes:
a temporal parameter to represent timestamps associated with the plurality of semantic concepts; and
a spatial parameter to represent physical locations associated with the plurality of semantic concepts;
train a machine learning model to derive inferences associated with the plurality of semantic concepts based on the DKG data structure;
obtain an input vector corresponding to data captured by one or more sensors, wherein elements of the input vector correspond to the set of meta-semantic parameters; and
derive an inference associated with one or more semantic concepts corresponding to the input vector, wherein the inference is derived based on processing the input vector using the machine learning model.
2 . The product of claim 1 , wherein the temporal parameter is to represent the timestamps based on linear temporal coding.
3 . The product of claim 1 , wherein the temporal parameter is to represent the timestamps based on log scale temporal coding.
4 . The product of claim 1 , wherein the temporal parameter is to represent the timestamps based on variable compressive temporal coding.
5 . The product of claim 1 , wherein the temporal parameter is to represent the timestamps based on periodic temporal coding.
6 . The product of claim 1 , wherein the temporal parameter comprises a plurality of temporal parameters to represent the timestamps based on a plurality of temporal coding formats, wherein the plurality of temporal coding formats comprises two or more of:
linear temporal coding;
log scale temporal coding;
variable compressive temporal coding; or
periodic temporal coding.
7 . The product of claim 1 , wherein the spatial parameter is to represent the physical locations based on linear spatial coding.
8 . The product of claim 1 , wherein the spatial parameter is to represent the physical locations based on log scale spatial coding.
9 . The product of claim 1 , wherein the spatial parameter is to represent the physical locations based on variable compressive spatial coding.
10 . The product of claim 1 , wherein the spatial parameter is to represent the physical locations based on periodic spatial coding.
11 . The product of claim 1 , wherein the spatial parameter comprises a plurality of spatial parameters to represent the physical locations based on a plurality of spatial coding formats, wherein the plurality of spatial coding formats comprises two or more of:
linear spatial coding;
log scale spatial coding;
variable compressive spatial coding; or
periodic spatial coding.
12 . A computing device, comprising:
memory circuitry to store a distributed knowledge graph (DKG) data structure, wherein the DKG data structure represents a plurality of semantic concepts associated with a training dataset as a set of vectors in a vector space, wherein elements of the set of vectors correspond to a set of meta-semantic parameters associated with the plurality of semantic concepts, wherein the set of meta-semantic parameters includes:
a temporal parameter to represent timestamps associated with the plurality of semantic concepts; and
a spatial parameter to represent physical locations associated with the plurality of semantic concepts; and
processing circuitry to:
access the DKG data structure stored in the memory circuitry;
train a machine learning model to derive inferences associated with the plurality of semantic concepts based on the DKG data structure;
obtain an input vector corresponding to data captured by one or more sensors, wherein elements of the input vector correspond to the set of meta-semantic parameters; and
derive an inference associated with one or more semantic concepts corresponding to the input vector, wherein the inference is derived based on processing the input vector using the machine learning model.
13 . The computing device of claim 12 , further comprising the one or more sensors.
14 . The computing device of claim 12 , wherein the temporal parameter is to represent the timestamps based on linear temporal coding, log scale temporal coding, variable compressive temporal coding, or periodic temporal coding.
15 . The computing device of claim 14 , wherein the temporal parameter comprises a plurality of temporal parameters to represent the timestamps based on a plurality of temporal coding formats, wherein the plurality of temporal coding formats comprises two or more of:
linear temporal coding;
log scale temporal coding;
variable compressive temporal coding; or
periodic temporal coding.
16 . The computing device of claim 12 , wherein the spatial parameter is to represent the physical locations based on linear spatial coding, log scale spatial coding, variable compressive spatial coding, or periodic spatial coding.
17 . The computing device of claim 16 , wherein the spatial parameter comprises a plurality of spatial parameters to represent the physical locations based on a plurality of spatial coding formats, wherein the plurality of spatial coding formats comprises two or more of:
linear spatial coding;
log scale spatial coding;
variable compressive spatial coding; or
periodic spatial coding.
18 . A computer-implemented method of deriving inferences associated with semantic concepts using machine learning, the method including:
accessing a distributed knowledge graph (DKG) data structure stored in memory circuitry, wherein the DKG data structure represents a plurality of semantic concepts associated with a training dataset as a set of vectors in a vector space, wherein elements of the set of vectors correspond to a set of meta-semantic parameters associated with the plurality of semantic concepts, wherein the set of meta-semantic parameters includes:
a temporal parameter to represent timestamps associated with the plurality of semantic concepts; and
a spatial parameter to represent physical locations associated with the plurality of semantic concepts;
training a machine learning model to derive inferences associated with the plurality of semantic concepts based on the DKG data structure;
obtaining an input vector corresponding to data captured by one or more sensors, wherein elements of the input vector correspond to the set of meta-semantic parameters; and
deriving an inference associated with one or more semantic concepts corresponding to the input vector, wherein the inference is derived based on processing the input vector using the machine learning model.
19 . The computer-implemented method of claim 18 , wherein the temporal parameter is to represent the timestamps based on linear temporal coding, log scale temporal coding, variable compressive temporal coding, or periodic temporal coding.
20 . The computer-implemented method of claim 19 , wherein the temporal parameter comprises a plurality of temporal parameters to represent the timestamps based on a plurality of temporal coding formats, wherein the plurality of temporal coding formats comprises two or more of:
linear temporal coding;
log scale temporal coding;
variable compressive temporal coding; or
periodic temporal coding.
21 . The computer-implemented method of claim 18 , wherein the spatial parameter is to represent the physical locations based on linear spatial coding, log scale spatial coding, variable compressive spatial coding, or periodic spatial coding.
22 . The computer-implemented method of claim 21 , wherein the spatial parameter comprises a plurality of spatial parameters to represent the physical locations based on a plurality of spatial coding formats, wherein the plurality of spatial coding formats comprises two or more of:
linear spatial coding;
log scale spatial coding;
variable compressive spatial coding; or
periodic spatial coding.
23 . A device to derive inferences associated with semantic concepts using machine learning, the device including:
means for accessing a distributed knowledge graph (DKG) data structure stored in memory circuitry, wherein the DKG data structure represents a plurality of semantic concepts associated with a training dataset as a set of vectors in a vector space, wherein elements of the set of vectors correspond to a set of meta-semantic parameters associated with the plurality of semantic concepts, wherein the set of meta-semantic parameters includes:
a temporal parameter to represent timestamps associated with the plurality of semantic concepts; and
a spatial parameter to represent physical locations associated with the plurality of semantic concepts;
means for training a machine learning model to derive inferences associated with the plurality of semantic concepts based on the DKG data structure;
means for obtaining an input vector corresponding to data captured by one or more sensors, wherein elements of the input vector correspond to the set of meta-semantic parameters; and
means for deriving an inference associated with one or more semantic concepts corresponding to the input vector, wherein the inference is derived based on processing the input vector using the machine learning model.
24 . The device of claim 23 , wherein the temporal parameter is to represent the timestamps based on linear temporal coding, log scale temporal coding, variable compressive temporal coding, or periodic temporal coding.
25 . The device of claim 23 , wherein the spatial parameter is to represent the physical locations based on linear spatial coding, log scale spatial coding, variable compressive spatial coding, or periodic spatial coding.