6.8 Explicit Semantic Analysis
The ore.odmESA function creates a model that uses the in-database Explicit Semantic Analysis (ESA) algorithm.
ESA is an in-database unsupervised algorithm that supports feature extraction. ESA does not discover latent features but instead uses explicit features based on an existing knowledge base.
Explicit knowledge often exists in text form. Multiple knowledge bases are available as collections of text documents. These knowledge bases can be generic, for example, Wikipedia, or domain-specific. Data preparation transforms the text into vectors that capture attribute-concept associations.
While projecting a document to the ESA topic space produces a high-dimensional sparse vector, it is unsuitable as an input to other machine learning algorithms. Starting from Oracle Database 23ai, embeddings are added to address this issue. For more information about the embeddings, see Oracle Machine Learning for SQL Concepts Guide.
For information on the ore.odmESA function arguments, call help(ore.odmESA).
Settings for an Explicit Semantic Analysis Model
The following table lists settings that apply to Explicit Semantic Analysis models.
Table 6-7 Explicit Semantic Analysis Model Settings
| Setting Name | Setting Value | Description |
|---|---|---|
|
|
|
This setting thresholds a small value for attribute weights in the transformed build data. The default is |
|
|
|
This setting determines the minimum number of non-zero entries that need to be present in an input row. The default is 100 for text input and 0 for non-text input. |
|
|
|
This setting controls the maximum number of features per attribute. The default is 1000.
|
|
Note: Available only in Oracle Database 23ai. |
|
This setting applies to feature extraction models. The default value is
|
|
Note: Available only in Oracle Database 23ai. |
|
This setting applies to feature extraction models. This setting specifies the size of the vectors representing embeddings. You can set this parameter only if you have enabled |
Example 6-7 Using the ore.odmESA Function
title <- c('Aids in Africa: Planning for a long war',
'Mars rover maneuvers for rim shot',
'Mars express confirms presence of water at Mars south pole',
'NASA announces major Mars rover finding',
'Drug access, Asia threat in focus at AIDS summit',
'NASA Mars Odyssey THEMIS image: typical crater',
'Road blocks for Aids')
# TEXT contents in character column
df <- data.frame(CUST_ID = seq(length(title)), TITLE = title)
ESA_TEXT <- ore.push(df)
# TEXT contents in clob column
attr(df$TITLE, "ora.type") <- "clob"
ESA_TEXT_CLOB <- ore.push(df)
# Create text policy (CTXSYS.CTX_DDL privilege is required)
ore.exec("Begin ctx_ddl.create_policy('ESA_TXTPOL'); End;")
# Specify TEXT POLICY_NAME, MIN_DOCUMENTS, MAX_FEATURES and
# ESA algorithm settings in odm.settings
esa.mod <- ore.odmESA(~ TITLE, data = ESA_TEXT_CLOB,
odm.settings = list(case_id_column_name = "CUST_ID",
ODMS_TEXT_POLICY_NAME = "ESA_TXTPOL",
ODMS_TEXT_MIN_DOCUMENTS = 1,
ODMS_TEXT_MAX_FEATURES = 3,
ESAS_MIN_ITEMS = 1,
ESAS_VALUE_THRESHOLD = 0.0001,
ESAS_TOPN_FEATURES = 3))
class(esa.mod)
summary(esa.mod)
settings(esa.mod)
features(esa.mod)
predict(esa.mod, ESA_TEXT, type = "class", supplemental.cols = "TITLE")
# Use ctx.settings to specify a character column as TEXT and
# the same settings as above as well as TOKEN_TYPE
esa.mod2 <- ore.odmESA(~ TITLE, data = ESA_TEXT,
odm.settings = list(case_id_column_name = "CUST_ID", ESAS_MIN_ITEMS = 1),
ctx.settings = list(TITLE =
"TEXT(POLICY_NAME:ESA_TXTPOL)(TOKEN_TYPE:STEM)(MIN_DOCUMENTS:1)(MAX_FEATURES:3)"))
summary(esa.mod2)
settings(esa.mod2)
features(esa.mod2)
predict(esa.mod2, ESA_TEXT_CLOB, type = "class", supplemental.cols = "TITLE")
ore.exec("Begin ctx_ddl.drop_policy('ESA_TXTPOL'); End;")Listing for This Example
R> title <- c('Aids in Africa: Planning for a long war',
+ 'Mars rover maneuvers for rim shot',
+ 'Mars express confirms presence of water at Mars south pole',
+ 'NASA announces major Mars rover finding',
+ 'Drug access, Asia threat in focus at AIDS summit',
+ 'NASA Mars Odyssey THEMIS image: typical crater',
+ 'Road blocks for Aids')
R>
R> # TEXT contents in character column
R> df <- data.frame(CUST_ID = seq(length(title)), TITLE = title)
R> ESA_TEXT <- ore.push(df)
R>
R> # TEXT contents in clob column
R> attr(df$TITLE, "ora.type") <- "clob"
R> ESA_TEXT_CLOB <- ore.push(df)
R>
R> # Create a text policy (CTXSYS.CTX_DDL privilege is required)
R> ore.exec("Begin ctx_ddl.create_policy('ESA_TXTPOL'); End;")
R>
R> # Specify TEXT POLICY_NAME, MIN_DOCUMENTS, MAX_FEATURES and
R> # ESA algorithm settings in odm.settings
R> esa.mod <- ore.odmESA(~ TITLE, data = ESA_TEXT_CLOB,
+ odm.settings = list(case_id_column_name = "CUST_ID",
+ ODMS_TEXT_POLICY_NAME = "ESA_TXTPOL",
+ ODMS_TEXT_MIN_DOCUMENTS = 1,
+ ODMS_TEXT_MAX_FEATURES = 3,
+ ESAS_MIN_ITEMS = 1,
+ ESAS_VALUE_THRESHOLD = 0.0001,
+ ESAS_TOPN_FEATURES = 3))
R> class(esa.mod)
[1] "ore.odmESA" "ore.model"
R> summary(esa.mod)
Call:
ore.odmESA(formula = ~TITLE, data = ESA_TEXT_CLOB, odm.settings = list(case_id_column_name = "CUST_ID",
ODMS_TEXT_POLICY_NAME = "ESA_TXTPOL", ODMS_TEXT_MIN_DOCUMENTS = 1,
ODMS_TEXT_MAX_FEATURES = 3, ESAS_MIN_ITEMS = 1, ESAS_VALUE_THRESHOLD = 1e-04,
ESAS_TOPN_FEATURES = 3))
Settings:
value
min.items 1
topn.features 3
value.threshold 1e-04
odms.missing.value.treatment odms.missing.value.auto
odms.sampling odms.sampling.disable
odms.text.max.features 3
odms.text.min.documents 1
odms.text.policy.name ESA_TXTPOL
prep.auto ON
Features:
FEATURE_ID ATTRIBUTE_NAME ATTRIBUTE_VALUE COEFFICIENT
1 1 TITLE.AIDS <NA> 1.0000000
2 2 TITLE.MARS <NA> 0.4078615
3 2 TITLE.ROVER <NA> 0.9130438
4 3 TITLE.MARS <NA> 1.0000000
5 4 TITLE.NASA <NA> 0.6742695
6 4 TITLE.ROVER <NA> 0.6742695
7 5 TITLE.AIDS <NA> 1.0000000
8 6 TITLE.MARS <NA> 0.4078615
9 6 TITLE.NASA <NA> 0.9130438
10 7 TITLE.AIDS <NA> 1.0000000
R> settings(esa.mod)
SETTING_NAME SETTING_VALUE SETTING_TYPE
1 ALGO_NAME ALGO_EXPLICIT_SEMANTIC_ANALYS INPUT
2 ESAS_MIN_ITEMS 1 INPUT
3 ESAS_TOPN_FEATURES 3 INPUT
4 ESAS_VALUE_THRESHOLD 1e-04 INPUT
5 ODMS_MISSING_VALUE_TREATMENT ODMS_MISSING_VALUE_AUTO DEFAULT
6 ODMS_SAMPLING ODMS_SAMPLING_DISABLE DEFAULT
7 ODMS_TEXT_MAX_FEATURES 3 INPUT
8 ODMS_TEXT_MIN_DOCUMENTS 1 INPUT
9 ODMS_TEXT_POLICY_NAME ESA_TXTPOL INPUT
10 PREP_AUTO ON INPUT
R> features(esa.mod)
FEATURE_ID ATTRIBUTE_NAME ATTRIBUTE_VALUE COEFFICIENT
1 1 TITLE.AIDS <NA> 1.0000000
2 2 TITLE.MARS <NA> 0.4078615
3 2 TITLE.ROVER <NA> 0.9130438
4 3 TITLE.MARS <NA> 1.0000000
5 4 TITLE.NASA <NA> 0.6742695
6 4 TITLE.ROVER <NA> 0.6742695
7 5 TITLE.AIDS <NA> 1.0000000
8 6 TITLE.MARS <NA> 0.4078615
9 6 TITLE.NASA <NA> 0.9130438
10 7 TITLE.AIDS <NA> 1.0000000
R> predict(esa.mod, ESA_TEXT, type = "class", supplemental.cols = "TITLE")
TITLE FEATURE_ID
1 Aids in Africa: Planning for a long war 1
2 Mars rover maneuvers for rim shot 2
3 Mars express confirms presence of water at Mars south pole 3
4 NASA announces major Mars rover finding 4
5 Drug access, Asia threat in focus at AIDS summit 1
6 NASA Mars Odyssey THEMIS image: typical crater 6
7 Road blocks for Aids 1
R>
R> # Use ctx.settings to specify a character column as TEXT and
R> # the same settings as above as well as TOKEN_TYPE
R> esa.mod2 <- ore.odmESA(~ TITLE, data = ESA_TEXT,
+ odm.settings = list(case_id_column_name = "CUST_ID", ESAS_MIN_ITEMS = 1),
+ ctx.settings = list(TITLE =
+ "TEXT(POLICY_NAME:ESA_TXTPOL)(TOKEN_TYPE:STEM)(MIN_DOCUMENTS:1)(MAX_FEATURES:3)"))
R> summary(esa.mod2)
Call:
ore.odmESA(formula = ~TITLE, data = ESA_TEXT, odm.settings = list(case_id_column_name = "CUST_ID",
ESAS_MIN_ITEMS = 1), ctx.settings = list(TITLE = "TEXT(POLICY_NAME:ESA_TXTPOL)(TOKEN_TYPE:STEM)(MIN_DOCUMENTS:1)(MAX_FEATURES:3)"))
Settings:
value
min.items 1
topn.features 1000
value.threshold .00000001
odms.missing.value.treatment odms.missing.value.auto
odms.sampling odms.sampling.disable
odms.text.max.features 300000
odms.text.min.documents 3
prep.auto ON
Features:
FEATURE_ID ATTRIBUTE_NAME ATTRIBUTE_VALUE COEFFICIENT
1 1 TITLE.AIDS <NA> 1.0000000
2 2 TITLE.MARS <NA> 0.4078615
3 2 TITLE.ROVER <NA> 0.9130438
4 3 TITLE.MARS <NA> 1.0000000
5 4 TITLE.MARS <NA> 0.3011997
6 4 TITLE.NASA <NA> 0.6742695
7 4 TITLE.ROVER <NA> 0.6742695
8 5 TITLE.AIDS <NA> 1.0000000
9 6 TITLE.MARS <NA> 0.4078615
10 6 TITLE.NASA <NA> 0.9130438
11 7 TITLE.AIDS <NA> 1.0000000
R> settings(esa.mod2)
SETTING_NAME SETTING_VALUE SETTING_TYPE
1 ALGO_NAME ALGO_EXPLICIT_SEMANTIC_ANALYS INPUT
2 ESAS_MIN_ITEMS 1 INPUT
3 ESAS_TOPN_FEATURES 1000 DEFAULT
4 ESAS_VALUE_THRESHOLD .00000001 DEFAULT
5 ODMS_MISSING_VALUE_TREATMENT ODMS_MISSING_VALUE_AUTO DEFAULT
6 ODMS_SAMPLING ODMS_SAMPLING_DISABLE DEFAULT
7 ODMS_TEXT_MAX_FEATURES 300000 DEFAULT
8 ODMS_TEXT_MIN_DOCUMENTS 3 DEFAULT
9 PREP_AUTO ON INPUT
R> features(esa.mod2)
FEATURE_ID ATTRIBUTE_NAME ATTRIBUTE_VALUE COEFFICIENT
1 1 TITLE.AIDS <NA> 1.0000000
2 2 TITLE.MARS <NA> 0.4078615
3 2 TITLE.ROVER <NA> 0.9130438
4 3 TITLE.MARS <NA> 1.0000000
5 4 TITLE.MARS <NA> 0.3011997
6 4 TITLE.NASA <NA> 0.6742695
7 4 TITLE.ROVER <NA> 0.6742695
8 5 TITLE.AIDS <NA> 1.0000000
9 6 TITLE.MARS <NA> 0.4078615
10 6 TITLE.NASA <NA> 0.9130438
11 7 TITLE.AIDS <NA> 1.0000000
R> predict(esa.mod2, ESA_TEXT_CLOB, type = "class", supplemental.cols = "TITLE")
TITLE FEATURE_ID
1 Aids in Africa: Planning for a long war 1
2 Mars rover maneuvers for rim shot 2
3 Mars express confirms presence of water at Mars south pole 3
4 NASA announces major Mars rover finding 4
5 Drug access, Asia threat in focus at AIDS summit 1
6 NASA Mars Odyssey THEMIS image: typical crater 6
7 Road blocks for Aids 1
R>
R> ore.exec("Begin ctx_ddl.drop_policy('ESA_TXTPOL'); End;")