研究論文

Entity population, a task of collecting entities that belong to a particular category, has attracted attention from vertical domains. There is still a high demand for creating entity dictionaries in vertical domains, which are not covered by existing knowledge bases. We develop a lightweight front-end tool for facilitating interactive entity population. We implement key components necessary for effective interactive entity population: 1) GUI-based dashboards to quickly modify an entity dictionary, and 2) entity highlighting on documents for quickly viewing the current progress. We aim to reduce user cost from beginning to end, including package installation and maintenance. The implementation enables users to use this tool on their web browsers without any additional packages — users can focus on their missions to create entity dictionaries. Moreover, an entity expansion module is implemented as external APIs. This design makes it easy to continuously improve interactive entity population pipelines. We are making our demo publicly available (http://bit.ly/luwak-demo).

The field of data integration has expanded significantly over the years, from providing a uniform query and update interface to structured databases within an enterprise to the ability to search, ex- change, and even update, structured or unstructured data that are within or external to the enterprise. This paper describes the evolution in the landscape of data integration since the work on rewriting queries using views in the mid-1990’s. In addition, we describe two important challenges for the field going forward. The first challenge is to develop good open-source tools for different components of data integration pipelines. The second challenge is to provide practitioners with viable solutions for the long-standing problem of systematically combining structured and unstructured data.

Depression is a prevailing issue and is an increasing problem in many people’s lives. Without observable diagnostic criteria, the signs of depression may go unnoticed, resulting in high demand for detecting depression in advance automatically. This paper tackles the challenging problem of forecasting severely depressed moods based on self-reported histories. Despite the large amount of research on understanding individual moods including depression, anxiety, and stress based on behavioral logs collected by pervasive computing devices such as smartphones, forecasting depressed moods is still an open question. This paper develops a recurrent neural network algorithm that incorporates categorical embedding layers for forecasting depression. We collected large-scale records from 2,382 self-declared depressed people to conduct the experiment. Experimental results show that our method forecast the severely depressed mood of a user based on self-reported histories, with higher accuracy than SVM. The results also showed that the long-term historical information of a user improves the accuracy of forecasting depressed mood.

Recently, search engines have invested significant effort to answering entity–attribute queries from structured data, but have focused mostly on queries for frequent attributes. In parallel, several research efforts have demonstrated that there is a long tail of attributes, often thousands per class of entities, that are of interest to users. Researchers are beginning to leverage these new collections of attributes to expand the ontologies that power search engines and to recognize entity– attribute queries. Because of the sheer number of potential attributes, such tasks require us to impose some structure on this long and heavy tail of attributes. This paper introduces the problem of organizing the attributes by expressing the compositional structure of their names as a rule-based grammar. These rules offer a compact and rich semantic interpretation of multi-word attributes, while generalizing from the observed attributes to new unseen ones. The paper describes an unsupervised learning method to generate such a grammar automatically from a large set of attribute names. Experiments show that our method can discover a precise grammar over 100,000 attributes of Countries while providing a 40-fold compaction over the attribute names. Furthermore, our grammar enables us to increase the precision of attributes from 47% to more than 90% with only a minimal curation effort. Thus, our approach provides an efficient and scalable way to expand ontologies with attributes of user interest.

Enterprises increasingly rely on structured datasets to run their businesses. These datasets take a variety of forms, such as structured files, databases, spreadsheets, or even services that provide access to the data. The datasets often reside in different storage systems, may vary in their formats, may change every day. In this paper, we present Goods, a project to rethink how we organize structured datasets at scale, in a setting where teams use diverse and often idiosyncratic ways to produce the datasets and where there is no centralized system for storing and querying them. Goods extracts metadata ranging from salient information about each dataset (owners, timestamps, schema) to relationships among datasets, such as similarity and provenance. It then exposes this metadata through services that allow engineers to find datasets within the company, to monitor datasets, to annotate them in order to enable others to use their datasets, and to analyze relationships between them. We discuss the technical challenges that we had to overcome in order to crawl and infer the metadata for billions of datasets, to maintain the consistency of our metadata catalog at scale, and to expose the metadata to users. We believe that many of the lessons that we learned are applicable to building large-scale enterprise-level datamanagement systems in general.

For most large enterprises today, data constitutes their core asset, along with code and infrastructure. For most enterprises, the amount of data that they produce internally has exploded in recent years. At the same time, in many cases, engineers and data scientists do not use centralized data-management systems and end up creating what became known as a data lake—a collection of datasets that often are not well organized or not organized at all and where one needs to “fish” for useful datasets. In this paper, we describe our experience building and deploying GOODS, a system to manage Google’s internal data lake. GOODS crawls Google’s infrastructure and builds a catalog of discovered datasets, including structured files, databases, spreadsheets, and even services that provide access to the data. GOODS extracts metadata about datasets in a post-hoc way: engineers continue to generate and organize datasets in the same way that they have before, and GOODS provides value without disrupting teams’ practices. The technical challenges that we had to address resulted both from the scale and heterogeneity of Google’s data lake and from our decision to extract metadata in a post-hoc manner. We believe that many of the lessons that we learned are applicable to building large-scale enterprise-level data-management systems in general.