Authors:
Youngho Kim
;
Jihee Ryu
and
Sung-Hyon Myaeng
Affiliation:
KAIST, Korea, Republic of
Keyword(s):
Patent retrieval, Invalidity search, Semantic annotation, Cluster-based retrieval.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Clustering and Classification Methods
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Mining Text and Semi-Structured Data
;
Symbolic Systems
Abstract:
Automatic annotation of key phrases for their semantic categories can help improving effectiveness of a variety of text-based systems including information retrieval, summarization, question answering, etc. In this paper, we exploit semantic annotations for patent retrieval (i.e., patent invalidity search). We first annotated key phrases for two semantic categories, PROBLEM (e.g. “pattern matching”) and SOLUTION (e.g. “dynamic programming”) in a patent document, which constitute a particular technology. Semantic clusters are formed by grouping patent documents with the same PROBLEM or SOLUTION tag. A language modelling approach to information retrieval is extended to consider the semantically oriented clusters as well as document models. Our retrieval evaluation of the proposed approach using a collection of United States patent documents shows a 22% improvement over the baseline, a smoothed language modelling approach without using the semantic annotations.