Taesung Lee

Attribute extraction and scoring: A probabilistic approach

Knowledge bases, which consist of concepts, entities, attributes and relations, are increasingly important in a wide range of applications. We argue that knowledge about attributes (of concepts or entities) plays a critical role in inferencing. In this paper, we propose methods to derive attributes for millions of concepts and we quantify the typicality of the attributes with regard to their corresponding concepts. We employ multiple data sources such as web documents, search logs, and existing knowledge bases, and we derive typicality scores for attributes by aggregating different distributions derived from different sources using different methods. To the best of our knowledge, ours is the first approach to integrate concept- and instance-based patterns into probabilistic typicality scores that scale to broad concept space. We have conducted extensive experiments to show the effectiveness of our approach.

Processing and optimizing main memory spatial-keyword queries

Important cloud services rely on spatial-keyword queries, containing a spatial predicate and arbitrary boolean keyword queries. In particular, we study the processing of such queries in main memory to support short response times. In contrast, current state-of-the-art spatial-keyword indexes and relational engines are designed for different assumptions. Rather than building a new spatial-keyword index, we employ a cost-based optimizer to process these queries using a spatial index and a keyword index. We address several technical challenges to achieve this goal. We introduce three operators as the building blocks to construct plans for main memory query processing. We then develop a cost model for the operators and query plans. We introduce five optimization techniques that efficiently reduce the search space and produce a query plan with low cost. The optimization techniques are computationally efficient, and they identify a query plan with a formal approximation guarantee under the common independence assumption. Furthermore, we extend the framework to exploit interesting orders. We implement the query optimizer to empirically validate our proposed approach using real-life datasets. The evaluation shows that the optimizations provide significant reduction in the average and tail latency of query processing: 7- to 11-fold reduction over using a single index in terms of 99th percentile response time. In addition, this approach outperforms existing spatial-keyword indexes, and DBMS query optimizers for both average and high-percentile response times.

Trivia quiz mining using probabilistic knowledge

Recent work suggests that providing unexpected information is an important factor for drawing user traffic. Such examples can be easily found in the “Did you know” section of the Wikipedia main page, the ESPN quiz, the Google Doodles, and the Bing main page. Inspired by these applications, we propose a novel trivia quiz mining asking unexpected questions for a given entity. We solve this problem by linking different types of social media as input and output, and mine unexpected properties based on prototype theory to mediate the input and the output media.

Linking, integrating, and translating entities via iterative graph matching

Entity tasks, such as linking, integration, and translation, are crucial for many search and NLP applications. For this purposed entity graphs have been manually built or automatically harvested. In this paper, we survey existing approaches abstracting these problems into a graph-based iterative matching on a pair of entity graphs.

Understanding Relation Temporality of Entities

This paper demonstrates the importance of relation equivalence for entity translation pair discovery. Existing approach of understanding relation equivalence has focused on using explicit features of cooccurring entities. In this paper, we explore latent features of temporality for understanding relation equivalence, and empirically show that the explicit and latent features complement each other. Our proposed hybrid approach of using both explicit and latent features improves relation translation by 0.16 F1-score, and in turn improves entity translation by 0.02.

Map Translation Using Geo-tagged Social Media

This paper discusses the problem of map translation, of servicing spatial entities in multiple languages. Existing work on entity translation harvests translation evidence from text resources, not considering spatial locality in translation. In contrast, we mine geo-tagged sources for multilingual tags to improve recall, and consider spatial properties of tags for translation to improve precision. Our approach empirically improves accuracy from 0.562 to 0.746 using Taiwanese spatial entities.