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# Whether the Jaccard common-word heuristic should be used.

jaccard_common_word=true

# Used with LENGTH above to determine the length range (must be within [0, 1]

length_difference_ratio=.2

# default storage berkeley folder

dbberkeley_base_dir=.dbberkeley

# IMPLICIT NODES AND EDGES

# - true: nodes and edges deemed implicit are stored in memory (requires reloading upon each run)

# - false: no node or edge is considered implicit and all are stored in the underlying database

keep_implicit_in_memory=false

# SEARCH

#

# Timeout (ms) after which to stop the search

#search_stopper_timeout=20000

#search_stopper_timeout=10000

search_stopper_timeout=15000

# Number of results after which to stop the search

search_stopper_topk=-1

#

# The default search algorithm to use:

# Possible values:

# - GAM: Grow and Aggressive Merge as described in CAiSE 2020 submission

# - GAM_COARSE: like GAM, but operating only on entities and datasets.

# - GAM_COARSE_LOCAL: coarse GAM with local search using short path algorithm within island.

# - GAM_COARSE_LOCAL_ASYNC: same as above, running local searches asynchronously.

# - SQL_PATH: supports only 2-keyword queries and only finds results within a single dataset; computes paths up to some fixed length using SQL directly

global_search_algorithm=GAM

#

# Parameters for the GAM algorithm(s):

# Number of matches to consider for a keyword:

max_matches_per_kwd=-1

# Metric on a (tree, root-adjacent edge) pair that determines the priority of the pair in the queue:

# - NUMBER_OF_NODES: The number of nodes of the tree.

# - ROOT_ADJACENT_EDGES: The number of edges adjacent to the tree root.

# - NUMBER_OF_NODES_ROOT_ADJACENT_EDGES: The number of nodes in the tree, then the number of edges adjacent to the tree root.

# - NODES_MINUS_MATCHES: The number of tree nodes minus the number of tree matches.

# - MATCHES_MINUS_NODES: The number of tree matches minus the number of tree nodes.

# - NODES_MINUS_MATCHES_THEN_FANOUT: The number of tree nodes minus the number of tree matches, then (if tie) the number of root-adjacent edges.

# - MATCHES_MINUS_NODES_THEN_FANOUT: The number of tree matches minus the number of tree nodes, then (if tie) the number of root-adjacent edges.

# - NODES_MINUS_MATCHES_THEN_SPECIFICITY: The number of tree nodes minus the number of tree matches, then (if tie) the specificity of the edge.

# - MATCHES_MINUS_NODES_THEN_SPECIFICITY: The number of tree matches minus the number of tree nodes, then (if tie) specificity of the edge.

# - MATCHES_THEN_NODES_THEN_SPECIFICITY: (-1)* number of tree matches, then the number of nodes, then (if tie) the specificity of the edge.

# Whether or not to remove obsolete tuples from the specificity table

cleanup_specificity=false

# ABSTRACT GRAPH

#

# Whether to create the abstract graph from the basic ConnectionLens graph. Possible values : true, false

create_abstract_graph=false

#

# Which graph to read when accessing the database. Possible values :

# - false : The graph read is the basic ConnectionLens graph which is stored in the default tables (nodes, edges, weak_same_as, specificity, ...)

# - true : The graph read is the abstract graph stored in a distinct set of tables (abs_nodes, abs_edges, ...). Should be set to true only for abstract graphs (i.e. after R&C extraction)

read_abstract_graph=false

query_only_specific_edge=false

# To run (or not) the classification method on the abstract graph. Possibles values are: NONE (classification is not run), TUFFY (to run Tuffy classification) or SUMMARIES (to run classification based on summaries). Default is NONE.

classification_method=NONE

tuffy_dbname = tuffydb

tuffy_username = tuffer

tuffy_password = strongPasswoRd

tuffy_directory = /var/connectionlens/tuffy

# the path where is store the models used by WordNet