Source code for indra.preassembler.grounding_mapper.adeft

import logging
from indra.ontology.standardize \
    import standardize_agent_name

logger = logging.getLogger(__name__)

# If the adeft disambiguator is installed, load adeft models to
# disambiguate acronyms and shortforms
    from adeft import available_shortforms as available_adeft_models
    from adeft.disambiguate import load_disambiguator
    adeft_disambiguators = {}
    for shortform in available_adeft_models:
        adeft_disambiguators[shortform] = load_disambiguator(shortform)
except Exception:'Adeft will not be available for grounding disambiguation.')
    adeft_disambiguators = {}

[docs]def run_adeft_disambiguation(stmt, agent, idx, agent_txt): """Run Adeft disambiguation on an Agent in a given Statement. This function looks at the evidence of the given Statement and attempts to look up the full paper or the abstract for the evidence. If both of those fail, the evidence sentence itself is used for disambiguation. The disambiguation model corresponding to the Agent text is then called, and the highest scoring returned grounding is set as the Agent's new grounding. The Statement's annotations as well as the Agent are modified in place and no value is returned. Parameters ---------- stmt : indra.statements.Statement An INDRA Statement in which the Agent to be disambiguated appears. agent : indra.statements.Agent The Agent (potentially grounding mapped) which we want to disambiguate in the context of the evidence of the given Statement. idx : int The index of the new Agent's position in the Statement's agent list (needed to set annotations correctly). Returns ------- bool True if disambiguation was successfully applied, and False otherwise. Reasons for a False response can be the lack of evidence as well as failure to obtain text for grounding disambiguation. """ success = False # If the Statement doesn't have evidence for some reason, then there is # no text to disambiguate by # NOTE: we might want to try disambiguating by other agents in the # Statement if not stmt.evidence: return False # Initialize annotations if needed so Adeft predicted # probabilities can be added to Agent annotations annots = stmt.evidence[0].annotations if 'agents' in annots: if 'adeft' not in annots['agents']: annots['agents']['adeft'] = \ {'adeft': [None for _ in stmt.agent_list()]} else: annots['agents'] = {'adeft': [None for _ in stmt.agent_list()]} grounding_text = _get_text_for_grounding(stmt, agent_txt) def apply_grounding(agent, agent_txt, ns_and_id): db_ns, db_id = ns_and_id.split(':', maxsplit=1) if db_ns == 'CHEBI' and not db_id.startswith('CHEBI:'): db_id = 'CHEBI:%s' % db_id agent.db_refs = {'TEXT': agent_txt, db_ns: db_id} = standard_name logger.debug('Disambiguated %s to: %s, %s:%s' % (agent_txt, standard_name, db_ns, db_id)) standardize_agent_name(agent, standardize_refs=True) def remove_grounding(agent, agent_txt): = agent_txt agent.db_refs = {'TEXT': agent_txt} if grounding_text: da = adeft_disambiguators[agent_txt] res = da.disambiguate([grounding_text]) ns_and_id, standard_name, disamb_scores = res[0] # If the highest score is ungrounded we explicitly remove grounding # and reset the (potentially incorrectly standardized) name to the # original text value. if ns_and_id == 'ungrounded': remove_grounding(agent, agent_txt) # Otherwise we update the db_refs with what we got from DEFT # and set the standard name elif ns_and_id in da.pos_labels: apply_grounding(agent, agent_txt, ns_and_id) annots['agents']['adeft'][idx] = disamb_scores else: if disamb_scores[ns_and_id] == 1 and ':' in ns_and_id: apply_grounding(agent, agent_txt, ns_and_id) annots['agents']['adeft'][idx] = disamb_scores else: remove_grounding(agent, agent_txt) success = True return success
def _get_text_for_grounding(stmt, agent_text): """Get text context for Adeft disambiguation If the INDRA database is available, attempts to get the fulltext from which the statement was extracted. If the fulltext is not available, the abstract is returned. If the indra database is not available, uses the pubmed client to get the abstract. If no abstract can be found, falls back on returning the evidence text for the statement. Parameters ---------- stmt : py:class:`indra.statements.Statement` Statement with agent we seek to disambiguate. agent_text : str Agent text that needs to be disambiguated Returns ------- text : str Text for Adeft disambiguation """ text = None # First we will try to get content from the DB try: from indra_db.util.content_scripts \ import get_text_content_from_text_refs from indra.literature.adeft_tools import universal_extract_text refs = stmt.evidence[0].text_refs # Prioritize the pmid attribute if given if stmt.evidence[0].pmid: refs['PMID'] = stmt.evidence[0].pmid logger.debug('Obtaining text for disambiguation with refs: %s' % refs) content = get_text_content_from_text_refs(refs) if not content: raise ValueError('Text obtained from DB is empty') text = universal_extract_text(content, contains=agent_text) if text: return text except Exception as e:'Could not get text for disambiguation from DB: %s' % e) # If that doesn't work, we try PubMed next if text is None: from indra.literature import pubmed_client pmid = stmt.evidence[0].pmid if pmid: logger.debug('Obtaining abstract for disambiguation for PMID%s' % pmid) text = pubmed_client.get_abstract(pmid) if text: return text # Finally, falling back on the evidence sentence if text is None:'Falling back on sentence-based disambiguation') text = stmt.evidence[0].text return text return None