Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making

Yao, Zijun; Li, Chengjiang; Dong, Tiansi; Lv, Xin; Yu, Jifan; Hou, Lei; Li, Juanzi; Zhang, Yichi; Dai, Zelin

doi:10.18653/v1/2021.acl-long.215

Cited by 9 publications

(6 citation statements)

References 30 publications

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“…Mojito [22] Model-specific LightEA [59], HIF+KAT [130], GMKSLEM [21] XTransE [137], CPM [99], Kelpie [81], CrossE [138], KGInfluence [143], GINN [40], SNS [39], approxSemanticCrossE [18] Local self-explaining TMN [52], BTPK [15], AutoTriggER [49], Instance-based [68] [91], D-REX [2], SIRE [134], SAIS [125], NERO [141], DISCO-RE [111],…”

Section: Entity Extraction Relation Extraction Entity Resolution Link...mentioning

confidence: 99%

Towards Explainable Automatic Knowledge Graph Construction with Human-in-the-Loop

Zhang

Meroño‐Peñuela

Simperl

2023

Frontiers in Artificial Intelligence and Applications

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Knowledge graphs are important in human-centered AI because of their ability to reduce the need for large labelled machine-learning datasets, facilitate transfer learning, and generate explanations. However, knowledge-graph construction has evolved into a complex, semi-automatic process that increasingly relies on opaque deep-learning models and vast collections of heterogeneous data sources to scale. The knowledge-graph lifecycle is not transparent, accountability is limited, and there are no accounts of, or indeed methods to determine, how fair a knowledge graph is in the downstream applications that use it. Knowledge graphs are thus at odds with AI regulation, for instance the EU’s upcoming AI Act, and with ongoing efforts elsewhere in AI to audit and debias data and algorithms. This paper reports on work in progress towards designing explainable (XAI) knowledge-graph construction pipelines with human-in-the-loop and discusses research topics in this space. These were grounded in a systematic literature review, in which we studied tasks in knowledge-graph construction that are often automated, as well as common methods to explain how they work and their outcomes. We identified three directions for future research: (i) tasks in knowledge-graph construction where manual input remains essential and where there may be opportunities for AI assistance; (ii) integrating XAI methods into established knowledge-engineering practices to improve stakeholder experience; as well as (iii) evaluating how effective explanations genuinely are in making knowledge-graph construction more trustworthy.

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Section: Entity Extraction Relation Extraction Entity Resolution Link...mentioning

confidence: 99%

Towards Explainable Automatic Knowledge Graph Construction with Human-in-the-Loop

Zhang

Meroño‐Peñuela

Simperl

2023

Frontiers in Artificial Intelligence and Applications

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“…Early works focus on engineering matching rules along with sophisticated comparison features. Recent study suggest that the attention architecture [124,125] and the pre-training scheme [126,127] may serve as an important role in entity matching.…”

Section: Knowledge Graph Completion and Integrationmentioning

confidence: 99%

A Roadmap for Big Model

Yuan¹,

Zhao²,

Jiahong³

et al. 2022

Preprint

Self Cite

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domains indexed by Google News. It contains 31 million documents with an average length of 793 BPE tokens. Like C4, it excludes examples with duplicate URLs. News dumps from December 2016 through March 2019 were used as training data, articles published in April 2019 from the April 2019 dump were used for evaluation. OpenWebText2(OWT2). OWT2 is an enhanced version of the original OpenWebTextCorpus, including content from multiple languages, document metadata, multiple dataset versions, and open source replication code, covering all Reddit submissions from 2005 up until April 2020. PubMed Central(PMC). PMC is a free full-text archive of biomedical and life sciences journal literature from the U.S. National Institutes of Health's National Library of Medicine (NIH/NLM). The dataset is updated daily. In addition to full-text articles, they contain corrections, retractions, and expressions of concern, as well as file lists that include metadata for articles in each dataset.PMC obtained by open registration in Amazon Web Services (AWS) includes The PMC Open Access Subset and The Author Manuscript Dataset. The PMC Open Access Subset includes all articles and preprints in PMC with a machine-readable Creative Commons license that allows reuse. The Author Manuscript Dataset includes accepted author manuscripts collected under a funder policy in PMC and made available in machine-readable formats for text mining. ArXiv. ArXiv is a repository of 1.7 million articles, with relevant features such as article titles, authors, categories, abstracts, full text PDFs, and more. It provides open access to academic articles, covering many subdisciplines from vast branches of physics to computer science to everything in between, including math, statistics, electrical engineering, quantitative biology, and economics, which is helpful to the potential downstream applications of the research field. In addition, the writing language of LaTeX also contributes to the study of language models. Colossal Clean Crawled Corpus(C4). C4 is a colossal, cleaned version of Common Crawl's web crawl corpus. It is based on Common Crawl dataset and was used to train the T5 text-to-text Transformer models. The cleaned English version of C4 has 364,868,901 training examples and 364,608 validation examples, while the uncleaned English version has 1,063,805,324 training examples and 1,065,029 validation examples; the realnewslike version has 13,799,838 training examples and 13,863 validation examples, while the webtextlike version has 4,500,788 training examples and 4,493 validation examples. Wiki-40B. Wikipedia (Wiki-40B) is a clean-up text collection containing more than 40 Wikipedia language editions of pages corresponding to entities. The dataset is split into train/validation/test sets for each language. The training set has 2,926,536 examples, the validation set has 163,597 examples, and the test set has 162,274 examples. Wiki-40B is cleaned by a page filter to remove ambiguous, redirected, deleted, and non-physical pages. CLUECorpus2020. CLUECorpus2020 ...

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“…(2) The semantic representations consider the embedding vectors that encapsulate a textual value, leveraging word-, characteror transformer-based models. In this work, we exclusively consider the unsupervised, pre-trained embeddings of fast-Text [8] that have been experimentally verified to effectively address the out-of-vocabulary cases in ER tasks, which abound in domain-specific terminology [25,46,59,64,66].…”

Section: Qualitative Analysismentioning

confidence: 99%

How to reduce the search space of Entity Resolution: with Blocking or Nearest Neighbor search?

Papadakis

Fisichella

Schoger

et al. 2022

Preprint

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Entity Resolution suffers from quadratic time complexity. To increase its time efficiency, three kinds of filtering techniques are typically used for restricting its search space: (i) blocking workflows, which group together entity profiles with identical or similar signatures, (ii) string similarity join algorithms, which quickly detect entities more similar than a threshold, and (iii) nearest-neighbor methods, which convert every entity profile into a vector and quickly detect the closest entities according to the specified distance function. Numerous methods have been proposed for each type, but the literature lacks a comparative analysis of their relative performance. As we show in this work, this is a non-trivial task, due to the significant impact of configuration parameters on the performance of each filtering technique. We perform the first systematic experimental study that investigates the relative performance of the main methods per type over 10 real-world datasets. For each method, we consider a plethora of parameter configurations, optimizing it with respect to recall and precision. For each dataset, we consider both schema-agnostic and schema-based settings. The experimental results provide novel insights into the effectiveness and time efficiency of the considered techniques, demonstrating the superiority of blocking workflows and string similarity joins.

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Interpretable and Low-Resource Entity Matching via Decoupling Feature Learning from Decision Making

Cited by 9 publications

References 30 publications

Towards Explainable Automatic Knowledge Graph Construction with Human-in-the-Loop

Towards Explainable Automatic Knowledge Graph Construction with Human-in-the-Loop

A Roadmap for Big Model

How to reduce the search space of Entity Resolution: with Blocking or Nearest Neighbor search?

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