Knowledge bases (KBs), which store high-quality information, are crucial for many applications, such as enhancing search results and serving as external sources for data cleaning. Not surprisingly, there exist outdated facts in most KBs due to the rapid change of information. Naturally, it is important to keep KBs up-to-date. Traditional wisdom has investigated the problem of using reference data (such as new facts extracted from the news) to detect outdated facts in KBs. However, existing approaches can only cover a small percentage of facts in KBs. In this paper, we proposeHOFD, a novel human-in-the-loop approach for outdated fact detection in KBs.HOFDtrains a binary classifier using features such as historical update frequency and update time of a fact to compute the likelihood of a fact in a KB to be outdated. Then,HOFDinteracts with humans to verify whether a fact with high likelihood is indeed outdated. In addition,HOFDalso uses logical rules to detect more outdated facts based on human feedback. The outdated facts detected by the logical rules will also be fed back to train the ML model further fordata augmentation. Extensive experiments on real-world KBs, such as Yago and DBpedia, show the effectiveness of our solution.

As an important part of database optimization, index selection problem remains a hot topic. Existing methods tend to use the cost estimated by the DBMS optimizer to measure the benefit of an index. However, due to the limitations of the cost estimation model in database management system (DBMS), these methods may not find the optimal index configuration. To address this problem, we present SmartIndex, an index advisor for relational database with learned cost estimator. We first design a graph convolutional network (GCN) based cost estimation model to predict a query's execution time on certain indexes. After that, we use a greedy method for index selection under certain constraints including number of indexes and storage cost of indexes, which can find better solutions for a given workload.

Many tables on the web suffer from multi-level and multi-type quality problems, but existing cleaning systems cannot provide a comprehensive quality improvement for them. Most of these systems are designed for solving a specific type of error, so that we need to resort to a number of different cleaning tools (one per error type) to get a high quality table. In this demonstration, we propose a human-in-the-loop cleaning platform EasyDR for detecting and repairing multi-level&multi-type errors in tables. The attendees will experience the following features of EasyDR: 1) Holistic error detection&repair. Users are able to perform a holistic table cleaning in EasyDR where machine algorithms are responsible for error detection while human intelligence is leveraged for error repairing. 2) Human-in-the-loop table cleaning. EasyDR performs an all-round quality diagnosis for the table, and automatically generates crowdsourcing cleaning tasks for the detected errors. To simplify cleaning tasks for crowdsourcing workers, EasyDR provides two task optimization techniques including domain-aware table summarization and difficulty-aware task order optimization. 3) Customizable cleaning mode. EasyDR provides a declarative language for users to customize cleaning tasks flexibly, e.g., selecting target errors, restricting the cleaning scope, defining the cooperation mode for machine and crowd.

Knowledge bases (KBs), which store high-quality information, are crucial for many applications, such as enhancing search results and serving as external sources for data cleaning. Not surprisingly, there exist outdated facts in most KBs due to the rapid change of information. Naturally, it is important to keep KBs up-to-date. Traditional wisdom has investigated the problem of using reference data (such as new facts extracted from the news) to detect outdated facts in KBs. However, existing approaches can only cover a small percentage of facts in KBs. In this paper, we propose a novel human-in-the-loop approach for outdated fact detection in KBs. It trains a binary classifier using features such as historical update frequency and existence time of a fact to compute the likelihood of a fact in a KB to be outdated. Then, it interacts with humans to verify whether a fact with high likelihood is indeed outdated. In addition, it also uses logical rules to detect more outdated facts based on human feedback. The outdated facts detected by the logical rules will also be fed back to train the ML model further for data augmentation. Extensive experiments on real-world KBs, such as Yago and DBpedia, show the effectiveness of our solution.

A data summarization for the large table can be of great help, which provides a concise and informative overview and assists the user to quickly figure out the subject of the data. However, a high quality summarization needs to have two desirable properties: presenting notable entities and achieving broad domain coverage. In this demonstration, we propose a summarizer system called PocketView that is able to create a data summarization through a pocket view of the table. The attendees will experience the following features of our system:(1) time-sensitive notability evaluation - PocketView can automatically identify notable entities according to their significance and popularity in user-defined time period; (2) broad-coverage pocket view - Our system will provide a pocket view for the table without losing any domain, which is much simpler and clearer for attendees to figure out the subject compared with the original table.

Web table augmentation enables users to augment attributes based on key column and other known information. For table augmentation, most of systems return a single result which could not meet the users' needs of selection and validation. Furthermore, previous works only consider the entity-attribute binary tables with the first column corresponding to the entity name and the second to an attribute to be extended. When a table has multiple columns to be extended, the result table consolidated by binary tables will suffer from entity inconsistency. In this paper, we present a framework called TAT to build Top-k consistent results for web table augmentation. While ensuring the consistency of entities, TAT provides as diverse results as possible. We design two algorithms, exclusive and iterative algorithm, for web table augmentation that return Top-k results based on different requirements from users. The experiments show that TAT could return Top-k consistent results without loss of precision or coverage.