Enabling Quality Control for Entity Resolution: A Human and Machine Cooperation Framework

Even though many machine algorithms have been proposed for entity resolution, it remains very challenging to find a solution with quality guarantees. In this paper, we propose a novel HUman and Machine cOoperation (HUMO) framework for entity resolution (ER), which divides an ER workload between the machine and the human. HUMO enables a mechanism for quality control that can flexibly enforce both precision and recall levels. We introduce the optimization problem of HUMO, minimizing human cost given a quality requirement, and then present three optimization approaches: a conservative baseline one purely based on the monotonicity assumption of precision, a more aggressive one based on sampling and a hybrid one that can take advantage of the strengths of both previous approaches. Finally, we demonstrate by extensive experiments on real and synthetic datasets that HUMO can achieve high-quality results with reasonable return on investment (ROI) in terms of human cost, and it performs considerably better than the state-of-the-art alternatives in quality control.Comment: 12 pages, 11 figures. Camera-ready version of the paper submitted to ICDE 2018, In Proceedings of the 34th IEEE International Conference on Data Engineering (ICDE 2018

ANALISIS ENTITY MATCHING PADA DATASET SMARTPHONE MENGGUNAKAN METODE SIF, RNN, ATTENTION, DAN HYBRID

Penerapan teknologi informasi saat ini berdampak pada kecepatan dan efektivitas suatu perusahaan atau masyarakat. Perkembangan dan kecepatan data saat ini sangat krusial, perusahaan terus berupaya untuk mempercepat analisis data perusahaan mereka. Dalam pelaksanaan pengolahan data, entity matching berperan mencocokkan dua entitas. Masalah dengan entity matching adalah bahwa kecocokan dalam pengenal unik terdistribusi jarang terjadi dan sering kali terkait dengan masalah privasi. Oleh karena itu diperlukan langkah untuk mencocokkan dua entitas yang sama yaitu dengan menggunakan deep learning. Deepmatcher adalah paket Python yang didasarkan pada arsitektur model Deep learning yang dianggap berfungsi baik dengan pencocokan entitas. Tujuan dari penelitian ini adalah untuk mengimplementasikan deepmatcher pada entity matching melalui pencocokan antara dua dataset smartphone dengan menggunakan model SIF, RNN, attention, dan hybrid. Hasil pengujian dengan semua model rata-rata akurat. Model attention dan hybrid cocok untuk proses pelatihan model pada dataset smartphone karena masing-masing memiliki nilai F1 terbesar yaitu 82,93.

Generating Concise Entity Matching Rules

© 2017 ACM. Entity matching (EM) is a critical part of data integration and cleaning. In many applications, the users need to understand why two entities are considered a match, which reveals the need for interpretable and concise EM rules. We model EM rules in the form of General Boolean Formulas (GBFs) that allows arbitrary attribute matching combined by conjunctions (Vee), disjunctions (Wedge), and negations (not). GBFs can generate more concise rules than traditional EM rules represented in disjunctive normal forms (DNFs). We use program synthesis, a powerful tool to automatically generate rules (or programs) that provably satisfy a high-level specification, to automatically synthesize EM rules in GBF format, given only positive and negative matching examples. In this demo, attendees will experience the following features: (1) Interpretability. they can see and measure the conciseness of EM rules defined using GBFs; (2) Easy customization. they can provide custom experiment parameters for various datasets, and, easily modify a rich predefined (default) synthesis grammar, using a Web interface; and (3) High performance. they will be able to compare the generated concise rules, in terms of accuracy, with probabilistic models (e.g., machine learning methods), and hand-written EM rules provided by experts. Moreover, this system will serve as a general platform for evaluating di.erent methods that discover EM rules, which will be released as an opensource tool on GitHub