Conference on Automated Knowledge Base Construction最新文献

Scalable Rule Learning in Probabilistic Knowledge Bases 概率知识库中的可扩展规则学习

Conference on Automated Knowledge Base Construction Pub Date : 2019-05-02 DOI: 10.24432/C5MW26

Arcchit Jain, Tal Friedman, Ondřej Kuželka, Guy Van den Broeck, L. D. Raedt

{"title":"Scalable Rule Learning in Probabilistic Knowledge Bases","authors":"Arcchit Jain, Tal Friedman, Ondřej Kuželka, Guy Van den Broeck, L. D. Raedt","doi":"10.24432/C5MW26","DOIUrl":"https://doi.org/10.24432/C5MW26","url":null,"abstract":"Knowledge Bases (KBs) are becoming increasingly large, sparse and probabilistic. These KBs are typically used to perform query inferences and rule mining. But their efficacy is only as high as their completeness. Efficiently utilizing incomplete KBs remains a major challenge as the current KB completion techniques either do not take into account the inherent uncertainty associated with each KB tuple or do not scale to large KBs. Probabilistic rule learning not only considers the probability of every KB tuple but also tackles the problem of KB completion in an explainable way. For any given probabilistic KB, it learns probabilistic first-order rules from its relations to identify interesting patterns. But, the current probabilistic rule learning techniques perform grounding to do probabilistic inference for evaluation of candidate rules. It does not scale well to large KBs as the time complexity of inference using grounding is exponential over the size of the KB. In this paper, we present SafeLearner -- a scalable solution to probabilistic KB completion that performs probabilistic rule learning using lifted probabilistic inference -- as faster approach instead of grounding. We compared SafeLearner to the state-of-the-art probabilistic rule learner ProbFOIL+ and to its deterministic contemporary AMIE+ on standard probabilistic KBs of NELL (Never-Ending Language Learner) and Yago. Our results demonstrate that SafeLearner scales as good as AMIE+ when learning simple rules and is also significantly faster than ProbFOIL+.","PeriodicalId":371465,"journal":{"name":"Conference on Automated Knowledge Base Construction","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133203143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Classifying entities into an incomplete ontology 将实体分类为一个不完整的本体

Conference on Automated Knowledge Base Construction Pub Date : 2013-10-27 DOI: 10.1145/2509558.2509564

Bhavana Dalvi, William W. Cohen, Jamie Callan

引用次数: 8

A joint model for discovering and linking entities 用于发现和链接实体的联合模型

Conference on Automated Knowledge Base Construction Pub Date : 2013-10-27 DOI: 10.1145/2509558.2509570

Michael L. Wick, Sameer Singh, Harshal Pandya, A. McCallum

{"title":"A joint model for discovering and linking entities","authors":"Michael L. Wick, Sameer Singh, Harshal Pandya, A. McCallum","doi":"10.1145/2509558.2509570","DOIUrl":"https://doi.org/10.1145/2509558.2509570","url":null,"abstract":"Entity resolution, the task of automatically determining which mentions refer to the same real-world entity, is a crucial aspect of knowledge base construction and management. However, performing entity resolution at large scales is challenging because (1) the inference algorithms must cope with unavoidable system scalability issues and (2) the search space grows exponentially in the number of mentions. Current conventional wisdom has been that performing coreference at these scales requires decomposing the problem by first solving the simpler task of entity-linking (matching a set of mentions to a known set of KB entities), and then performing entity discovery as a post-processing step (to identify new entities not present in the KB). However, we argue that this traditional approach is harmful to both entity-linking and overall coreference accuracy. Therefore, we embrace the challenge of jointly modeling entity-linking and entity-discovery as a single entity resolution problem. In order to make progress towards scalability we (1) present a model that reasons over compact hierarchical entity representations, and (2) propose a novel distributed inference architecture that does not suffer from the synchronicity bottleneck which is inherent in map-reduce architectures. We demonstrate that more test-time data actually improves the accuracy of coreference, and show that joint coreference is substantially more accurate than traditional entity-linking, reducing error by 75%.","PeriodicalId":371465,"journal":{"name":"Conference on Automated Knowledge Base Construction","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133609580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 20

Ontology-aware partitioning for knowledge graph identification 知识图识别的本体感知划分

Conference on Automated Knowledge Base Construction Pub Date : 2013-10-27 DOI: 10.1145/2509558.2509562

J. Pujara, Hui Miao, L. Getoor, William W. Cohen

{"title":"Ontology-aware partitioning for knowledge graph identification","authors":"J. Pujara, Hui Miao, L. Getoor, William W. Cohen","doi":"10.1145/2509558.2509562","DOIUrl":"https://doi.org/10.1145/2509558.2509562","url":null,"abstract":"Knowledge graphs provide a powerful representation of entities and the relationships between them, but automatically constructing such graphs from noisy extractions presents numerous challenges. Knowledge graph identification (KGI) is a technique for knowledge graph construction that jointly reasons about entities, attributes and relations in the presence of uncertain inputs and ontological constraints. Although knowledge graph identification shows promise scaling to knowledge graphs built from millions of extractions, increasingly powerful extraction engines may soon require knowledge graphs built from billions of extractions. One tool for scaling is partitioning extractions to allow reasoning to occur in parallel. We explore approaches which leverage ontological information and distributional information in partitioning. We compare these techniques with hash-based approaches, and show that using a richer partitioning model that incorporates the ontology graph and distribution of extractions provides superior results. Our results demonstrate that partitioning can result in order-of-magnitude speedups without reducing model performance.","PeriodicalId":371465,"journal":{"name":"Conference on Automated Knowledge Base Construction","volume":"232 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131889658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18

Universal schema for entity type prediction 用于实体类型预测的通用模式

Conference on Automated Knowledge Base Construction Pub Date : 2013-10-27 DOI: 10.1145/2509558.2509572

Limin Yao, S. Riedel, A. McCallum

{"title":"Universal schema for entity type prediction","authors":"Limin Yao, S. Riedel, A. McCallum","doi":"10.1145/2509558.2509572","DOIUrl":"https://doi.org/10.1145/2509558.2509572","url":null,"abstract":"Categorizing entities by their types is useful in many applications, including knowledge base construction, relation extraction and query intent prediction. Fine-grained entity type ontologies are especially valuable, but typically difficult to design because of unavoidable quandaries about level of detail and boundary cases. Automatically classifying entities by type is challenging as well, usually involving hand-labeling data and training a supervised predictor.\u0000 This paper presents a universal schema approach to fine-grained entity type prediction. The set of types is taken as the union of textual surface patterns (e.g. appositives) and pre-defined types from available databases (e.g. Freebase)---yielding not tens or hundreds of types, but more than ten thousands of entity types, such as financier, criminologist, and musical trio. We robustly learn mutual implication among this large union by learning latent vector embeddings from probabilistic matrix factorization, thus avoiding the need for hand-labeled data. Experimental results demonstrate more than 30% reduction in error versus a traditional classification approach on predicting fine-grained entities types.","PeriodicalId":371465,"journal":{"name":"Conference on Automated Knowledge Base Construction","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129840840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 51

A survey of noise reduction methods for distant supervision 远程监理降噪方法综述

Conference on Automated Knowledge Base Construction Pub Date : 2013-10-27 DOI: 10.1145/2509558.2509571

Benjamin Roth, Tassilo Barth, Michael Wiegand, D. Klakow

引用次数: 57

Exploiting DBpedia for web search results clustering 利用DBpedia进行web搜索结果聚类

Conference on Automated Knowledge Base Construction Pub Date : 2013-10-27 DOI: 10.1145/2509558.2509574

M. Schuhmacher, Simone Paolo Ponzetto

引用次数: 15

Extracting meronyms for a biology knowledge base using distant supervision 利用远程监控提取生物知识库的别名

Conference on Automated Knowledge Base Construction Pub Date : 2013-10-27 DOI: 10.1145/2509558.2509560

Xiao Ling, Peter Clark, Daniel S. Weld

引用次数: 9

Knowledge base population and visualization using an ontology based on semantic roles 基于语义角色的本体的知识库填充和可视化

Conference on Automated Knowledge Base Construction Pub Date : 2013-10-27 DOI: 10.1145/2509558.2509573

Maryam Siahbani, Ravikiran Vadlapudi, M. Whitney, Anoop Sarkar

引用次数: 3

Using natural language to integrate, evaluate, and optimize extracted knowledge bases 使用自然语言来整合、评估和优化提取的知识库

Conference on Automated Knowledge Base Construction Pub Date : 2013-10-27 DOI: 10.1145/2509558.2509569

Doug Downey, Chandra Bhagavatula, A. Yates

引用次数: 3