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检索条件"任意字段=1st Workshop on Knowledge Graphs and Semantics for Text Retrieval and Analysis, KG4IR 2017"

共 13 条记录，以下是1-10 订阅

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WordNetContext: Information retrieval-friendly access to wordnet senses 1

WordNetContext: Information retrieval-friendly access to wor...

引用

Joint 1st International workshop on Professional Search, the 2nd workshop on knowledge graphs and semantics for text retrieval, analysis, and Understanding and the International workshop on Data Search, ProfS-kg4ir-Data:Search 2018

作者： Basu, Chumki Dietz, Laura Fellbaum, Christiane Perspecta Labs United States University of New Hampshire United States Princeton University United States

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Overcoming low-utility facets for complex answer retrieval 1

Overcoming low-utility facets for complex answer retrieval

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作者： MacAvaney, Sean Yates, Andrew Soldaini, Luca Hui, Kai Goharian, Nazli Friederl, Ophir IRLab Georgetown University United States Max Planck Institute for Informatics United States SAP SE United States

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Neural variational entity set expansion for automatically populated knowledge graphs 1

Neural variational entity set expansion for automatically po...

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作者： Rastogi, Pushpendre Poliak, Adam Lyzinski, Vince Durme, Benjamin Van Johns Hopkins University United States UMass Amherst United States

We propose Neural Variational Set Expansion to extract actionable information from a noisy knowledge graph (kg) and propose a general approach for increasing the interpretability of recommendation systems. We demonstrate the usefulness of applying a variational autoencoder to the Entity Set Expansion task based on a realistic automatically generated kg. © Copyright by the paper's authors.

关键词： knowledge graph

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Automated knowledge hierarchy assessment 1

Automated knowledge hierarchy assessment

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作者： Nayak, G. Dutta, S. Ajwani, D. Nicholson, P. Sala, A. Univ. of Minnesota United States Nokia Bell Labs Ireland United States

Automated construction of knowledge hierarchies is gaining increasing attention to tackle the infeasibility of manually extracting and semantically linking millions of concepts. With the evolution of knowledge hierarchies, there is a need for measures to assess its temporal evolution, quantifying the similarities between different versions and identifying the relative growth of different subgraphs in the knowledge hierarchy. This work proposes a principled and scalable similarity measure, based on Katz similarity between concept nodes, for comparing knowledge hierarchies, modeled as generic Directed Acyclic graphs (DAGs). © Copyright by the paper's authors.

关键词： Directed graphs

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CEUR workshop Proceedings

CEUR Workshop Proceedings

引用

The proceedings contain 12 papers. The topics discussed include: search and recommendation in the enterprise;research challenges in legal discovery and investigations;automatic shortlisting of candidates in recruitment;searching for relevant lessons learned using hybrid information retrieval classifiers: a case study in software engineering;refresh strategies in continuous active learning;validating the importance of work tasks for professional search;challenges in the development of effective systems for professional legal search;distributional representation of complex semantics;learning new type representations from knowledge graphs;neural variational entity set expansion for automatically populated knowledge graphs;and summarizing entities using distantly supervised information extractors.

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kg4ir: The second workshop on knowledge graphs and semantics for text retrieval, analysis, and understanding

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CEUR workshop Proceedings 2018年 2127卷 43-44页

作者： Dietz, Laura Xiong, Chenyan Dalton, Jeff Meij, Edgar University of New Hampshire United States Carnegie Mellon University United States University of Glasgow United Kingdom Bloomberg United States

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Searching for relevant lessons learned using hybrid information retrieval classifiers: A case study in software engineering 1

Searching for relevant lessons learned using hybrid informat...

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作者： Abdellatif, Tamer Mohamed Capretz, Luiz Fernando Ho, Danny Dept. of Electrical and Computer Engineering Western University United States

The lessons learned (LL) repository is one of the most valuable sources of knowledge for a software organization. It can provide distinctive guidance regarding previous working solutions for historical software management problems, or former success stories to be followed. However, the unstructured format of the LL repository makes it diffcult to search using general queries, which are manually inputted by project managers (PMs). For this reason, this repository may often be over- looked despite the valuable information it provides. Since the LL repository targets PMs, the search method should be domain specific rather than generic as in the case of general web searching. In previous work, we provided an automatic information retrieval based LL classifier solution. In our solution, we relied on existing project management artifacts in constructing the search query on-they. In this paper, we extend our previous work by examining the impact of the hybridization of multiple LL classifiers, from our previous study, on performance. We employ two of the hybridization techniques from the literature to construct the hybrid classifiers. An industrial dataset of 212 LL records is used for validation. The results show the superiority of the hybrid classifier over the top achieving individual classifier, which reached 24%. © Copyright by the paper's authors.

关键词： Information retrieval

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First international workshop on professional search preface

引用

CEUR workshop Proceedings 2018年 2127卷 1-2页

作者： Verberne, Suzan He, Jiyin Kruschwitz, Udo Larsen, Birger Russell-Rose, Tony De Vries, Arjen P. Leiden University Netherlands Signal Media Netherlands University of Essex United Kingdom Aalborg University Denmark 2dSearch Guildford United Kingdom Radboud University Netherlands

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CEUR workshop Proceedings

CEUR Workshop Proceedings

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1st workshop on knowledge graphs and semantics for text retrieval and analysis, kg4ir 2017

The proceedings contain 8 papers. The topics discussed include: predicting asymmetric transitive relations in knowledge bases;measuring demonstrated potential domain knowledge with knowledge graphs;entity attribute ranking using learning to rank;test collection for evaluating actionable knowledge graphs;enhancing categorization of computer science research papers using knowledge bases;natural language supported relation matching for question answering with knowledge graphs;and early fusion strategy for entity-relationship retrieval.

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Measuring demonstrated potential domain knowledge with knowledge graphs 1

Measuring demonstrated potential domain knowledge with knowl...

引用

1st workshop on knowledge graphs and semantics for text retrieval and analysis, kg4ir 2017

作者： He, Jiyin M., Bron CWI Amsterdam Netherlands Yahoo London United Kingdom

Current search and recommendation engines enable us to effectively retrieve a set of documents based on topical relevance. What is not taken into account is the knowledge a user may already have about a topic, e.g., whether information is redundant or whether he/she is able to understand the results. We propose a method to measure demonstrated potential domain knowledge (DPDK) as a proxy for knowledge and use this metric to analyse the query log of a user spanning over 10 years. © Copyright by the paper's authors.

关键词： Domain knowledge

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