Automated timeline completion using event progression knowledge base

A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program comprises instructions, which when executed on a processor of a computing device causes the computing device to implement a timeline generation system for automated timeline completion, wherein the computer readable program causes the computing device to:

• identify, by a timeline generation component executing within a natural language processing pipeline of the natural language processing system, a plurality of events having time information in documents in a corpus of information;
• place, by the timeline generation component, the plurality of events in a partial timeline data structure;
• select, by the timeline generation component, an event progression from an event progression knowledge base, wherein the event progression knowledge base is a knowledge resource consisting of multiple event progressions, wherein each event progression in the event progression knowledge base is a natural ordering of a sequence of events as they typically occur in the real world, and wherein each event in a given event progression has the following time information:
• rank information implicit in the ordering, approximate distance from the next event, and confidence or fuzziness factor for the distance, wherein selecting the event progression from the event progression knowledge base comprises:
• aligning each event progression in the event progression knowledge base to the partial timeline data structure;
• determining a matching score of each event progression based on a mapping function;
• and
• selecting an event progression having a highest matching score;
• align, by the timeline generation component, the selected event progression to the partial timeline data structure;
• identify, by the timeline generation component, a set of events present in the selected event progression and missing from the partial timeline data structure;
• identify, by the timeline generation component, a set of unassigned events in the documents in the corpus that lack associated time information and are not placed the partial timeline data structure;
• match, by the timeline generation component, the set of unassigned events to the selected event progression based on semantic similarity or synonymity between event pairs to form a set of matched unassigned events;
• map, by the timeline generation component, the set of matched events to the partial timeline based on the selected event progression to form a completed timeline data structure including the plurality of events having time information and the set of matched events;
• store, by the timeline generation component, the completed timeline data structure in the corpus of information;
• and
• perform, by the natural language processing system, a time-based natural language processing operation using the completed timeline data structure and the corpus of information.

A computing device comprising:

• a processor;
• and
• a memory coupled to the processor, wherein the memory comprises instructions, which when executed on a processor of a computing device causes the computing device to implement a timeline generation system for automated timeline completion, wherein the instructions cause the processor to:
• identify, by a timeline generation component executing within a natural language processing pipeline of the natural language processing system, a plurality of events having time information in documents in a corpus of information;
• place, by the timeline generation component, the plurality of events in a partial timeline data structure;
• select, by the timeline generation component, an event progression from an event progression knowledge base, wherein the event progression knowledge base is a knowledge resource consisting of multiple event progressions, wherein each event progression in the event progression knowledge base is a natural ordering of a sequence of events as they typically occur in the real world, and wherein each event in a given event progression has the following time information:
• rank information implicit in the ordering, approximate distance from the next event, and confidence or fuzziness factor for the distance, wherein selecting the event progression from the event progression knowledge base comprises:
• aligning each event progression in the event progression knowledge base to the partial timeline data structure;
• determining a matching score of each event progression based on a mapping function;
• and
• selecting an event progression having a highest matching score;
• align, by the timeline generation component, the selected event progression to the partial timeline data structure;
• identify, by the timeline generation component, a set of events present in the selected event progression and missing from the partial timeline data structure;
• identify, by the timeline generation component, a set of unassigned events in the documents in the corpus that lack associated time information and are not placed the partial timeline data structure;
• match, by the timeline generation component, the set of unassigned events to the selected event progression based on semantic similarity or synonymity between event pairs to form a set of matched unassigned events;
• map, by the timeline generation component, the set of matched events to the partial timeline based on the selected event progression to form a completed timeline data structure including the plurality of events having time information and the set of matched events;
• store, by the timeline generation component, the completed timeline data structure in the corpus of information;
• and
• perform, by the natural language processing system, a time-based natural language processing operation using the completed timeline data structure and the corpus of information.