Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
WordNet: a lexical database for English
Communications of the ACM
Conceptual modeling for data and knowledge management
Data & Knowledge Engineering
Information Retrieval with Conceptual Graph Matching
DEXA '00 Proceedings of the 11th International Conference on Database and Expert Systems Applications
Using the patent co-citation approach to establish a new patent classification system
Information Processing and Management: an International Journal
FASTUS: a system for extracting information from text
HLT '93 Proceedings of the workshop on Human Language Technology
A patent document retrieval system addressing both semantic and syntactic properties
PATENT '03 Proceedings of the ACL-2003 workshop on Patent corpus processing - Volume 20
Patent claim processing for readability: structure analysis and term explanation
PATENT '03 Proceedings of the ACL-2003 workshop on Patent corpus processing - Volume 20
Natural language analysis of patent claims
PATENT '03 Proceedings of the ACL-2003 workshop on Patent corpus processing - Volume 20
Evaluating patent retrieval in the third NTCIR workshop
Information Processing and Management: an International Journal - Special issue: Formal methods for information retrieval
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This paper develops techniques to extract conceptual graphs from a patent claim using syntactic information (POS, and dependency tree) and semantic information (background ontology). Due to plenteous technical domain terms and lengthy sentences prevailing in patent claims, it is difficult to apply a NLP Parser directly to parse the plain texts in the patent claim. This paper combines techniques such as finite state machines, Part-Of-Speech tags, conceptual graphs, domain ontology and dependency tree to convert a patent claim into a formally defined conceptual graph. The method of a finite state machine splits a lengthy patent claim sentence into a set of shortened sub-sentences so that the NLP Parser can parse them one by one effectively. The Part-Of-Speech and dependency tree of a patent claim are used to build the conceptual graph based on the pre-established domain ontology. The result shows that 99% sub-sentences split from 1700 patent claims can be efficiently parsed by the NLP Parser. There are two types of nodes in a conceptual graph, the concept and the relation nodes. Each concept or relation can be extracted directly from a patent claim and each relation can link with a fixed number of concepts in a conceptual graph. From 100 patent claims, the average precision and recall of a concept class mapping from the patent claim to domain ontology are 96% and 89%, respectively, and the average precision and recall for Real relation class mapping are 97% and 98%, respectively. For the concept linking of a relation, the average precision is 79%. Based on the extracted conceptual graphs from patents, it would facilitate automated comparison and summarization among patents for judgment of patent infringement.