LODチャレンジ実行委員会 関西支部長 古崎晃司
LODチャレンジ実行委員会/Linked Open Data Initiative理事 松村冬子
Linked Open Dataの基本的な技術の解説,利用事例の紹介に加え,簡単なサンプルプログラムの紹介など,ハッカソンに活用できるLOD技術や情報ソースについて解説します.
第3回Linked Open Dataハッカソン関西(1日目)アイデアソン
開催日:2014年2月11日(火)
The document discusses graph databases and their properties. Graph databases are structured to store graph-based data by using nodes and edges to represent entities and their relationships. They are well-suited for applications with complex relationships between entities that can be modeled as graphs, such as social networks. Key graph database technologies mentioned include Neo4j, OrientDB, and TinkerPop which provides graph traversal capabilities.
LODチャレンジ実行委員会 関西支部長 古崎晃司
LODチャレンジ実行委員会/Linked Open Data Initiative理事 松村冬子
Linked Open Dataの基本的な技術の解説,利用事例の紹介に加え,簡単なサンプルプログラムの紹介など,ハッカソンに活用できるLOD技術や情報ソースについて解説します.
第3回Linked Open Dataハッカソン関西(1日目)アイデアソン
開催日:2014年2月11日(火)
The document discusses graph databases and their properties. Graph databases are structured to store graph-based data by using nodes and edges to represent entities and their relationships. They are well-suited for applications with complex relationships between entities that can be modeled as graphs, such as social networks. Key graph database technologies mentioned include Neo4j, OrientDB, and TinkerPop which provides graph traversal capabilities.
Neural Models for Information Retrieval
Bhaskar Mitra, Nick Craswell
(Submitted on 3 May 2017)
Neural ranking models for information retrieval (IR) use shallow or deep neural networks to rank search results in response to a query. Traditional learning to rank models employ machine learning techniques over hand-crafted IR features. By contrast, neural models learn representations of language from raw text that can bridge the gap between query and document vocabulary. Unlike classical IR models, these new machine learning based approaches are data-hungry, requiring large scale training data before they can be deployed. This tutorial introduces basic concepts and intuitions behind neural IR models, and places them in the context of traditional retrieval models. We begin by introducing fundamental concepts of IR and different neural and non-neural approaches to learning vector representations of text. We then review shallow neural IR methods that employ pre-trained neural term embeddings without learning the IR task end-to-end. We introduce deep neural networks next, discussing popular deep architectures. Finally, we review the current DNN models for information retrieval. We conclude with a discussion on potential future directions for neural IR.
LODチャレンジ2022授賞式シンポジウムでの紹介スライドです。
受賞作品:https://github.com/KnowledgeGraphJapan/KGRC-RDF/blob/kgrc4si/extended_readme.md
受賞情報:https://2022.lodc.jp/awardPressRelease2022.html
引用:
江上周作,鵜飼孝典,窪田文也,大野美喜子,北村光司,福田賢一郎: 家庭内の事故予防に向けた合成ナレッジグラフの構築と推論,第56回人工知能学会セマンティックウェブとオントロジー研究会, SIG-SWO-056-14 (2022) DOI: https://doi.org/10.11517/jsaisigtwo.2022.SWO-056_14
Egami, S., Nishimura, S., Fukuda, K.: A Framework for Constructing and Augmenting Knowledge Graphs using Virtual Space: Towards Analysis of Daily Activities. Proceedings of the 33rd IEEE International Conference on Tools with Artificial Intelligence. pp.1226-1230 (2021) DOI: https://doi.org/10.1109/ICTAI52525.2021.00194
Egami, S., Nishimura, S., Fukuda, K.: VirtualHome2KG: Constructing and Augmenting Knowledge Graphs of Daily Activities Using Virtual Space. Proceedings of the ISWC 2021 Posters, Demos and Industry Tracks: From Novel Ideas to Industrial Practice, co-located with 20th International Semantic Web Conference. CEUR, Vol.2980 (2021) https://ceur-ws.org/Vol-2980/paper381.pdf
Knowledge Graph Reasoning Techniques through Studies on Mystery Stories - Rep...KnowledgeGraph
1) The document summarizes the Knowledge Graph Reasoning Challenge (KGRC) held from 2018 to 2020.
2) The challenge task involved developing AI systems that can reason about and solve mysteries presented as open knowledge graphs based on Sherlock Holmes stories, providing reasonable explanations.
3) Over the three years of the challenge, 24 systems were submitted using various approaches like knowledge processing, machine learning, or combinations, and making use of different external knowledge resources. The challenge aims to promote techniques for explainable AI using knowledge graph reasoning.
Report on the First Knowledge Graph Reasoning Challenge 2018 -Toward the eXp...KnowledgeGraph
JIST2019: The 9th Joint International Semantic Technology Conference
The premium Asian forum on Semantic Web, Knowledge Graph, Linked Data and AI on the Web. Nov. 25-27, 2019, Hangzhou, China.
http://jist2019.openkg.cn/