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Jiyu Komiya

dblp:205/2529 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Knowledge representation and reasoning · 100%
Databases, data mining, and information retrieval
1 paper
Query processing and optimization · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning › knowledge acquisition › knowledge extraction
declarative information extraction
0.312018
Koko: A System for Scalable Semantic Querying of Text · Proc. VLDB Endow. 2018

Methods — techniques the papers use, named apart from their topics

surface syntax conditions · 0.7dependency parse tree · 0.7
YearPublicationVenuePosition
2018 Koko: A System for Scalable Semantic Querying of Text
abstract
K oko is a declarative information extraction system that incorporates advances in natural language processing techniques in its extraction language. K oko 's extraction language supports simultaneous specification of conditions over the surface syntax and on the structure of the dependency parse tree of sentences, thereby allowing for more refined extractions. Furthermore, the K oko extraction language allows for aggregating evidence from an input document and supports conditions that are tolerant of linguistic variation of expressing concepts. In this demo, we outline the design of K oko , a system for extracting information and understanding the results of the extraction. K oko provides an interactive interface that allows participants to write queries, understand the input and results of the queries. In particular, the user can customize the input text, visualize the input text's dependency parse trees, and understand the correspondences between query components, dependency tree nodes, text tokens, and the computation and associated scores that led to an extraction.
Xiaolan Wang 0001, Jiyu Komiya, Yoshihiko Suhara, Aaron Feng, Behzad Golshan, Alon Y. Halevy, Wang Chiew Tan
Proc. VLDB Endow.2