SLQ: A User-friendly Graph Querying System Shengqi Yang 1 Yanan Xie 2 Yinghui Wu 1 Tianyu Wu 2 Huan Sun 1 Jian Wu 2 Xifeng Yan 1 1 University of California Santa Barbara 2 Zhejiang University {sqyang, yinghui, huansun, xyan}@cs.ucsb.edu {xyn, tywu, wujian2000}@zju.edu.cn ABSTRACT Querying complex graph databases such as knowledge graphs is a challenging task for non-professional users. In this demo, we present SLQ, a user-friendly graph query- ing system enabling s chemal ess and s tructurel ess graph q uerying, where a user need not describe queries precisely as required by most databases. SLQ system combines search- ing and ranking: it leverages a set of transformation func- tions, including abbreviation, ontology, synonym, etc., that map keywords and linkages from a query to their matches in a data graph, based on an automatically learned rank- ing model. To help users better understand search results at different levels of granularity, it supports effective result summarization with “drill-down” and “roll-up” operations. Better still, the architecture of SLQ is elastic for new trans- formation functions, query logs and user feedback, to itera- tively refine the ranking model. SLQ significantly improves the usability of graph querying. This demonstration high- lights (1) SLQ can automatically learn an effective ranking model, without assuming manually labeled training exam- ples, (2) it can efficiently return top ranked matches over noisy, large data graphs, (3) it can summarize the query matches to help users easily access, explore and understand query results, and (4) its GUI can interact with users to help them construct queries, explore data graphs and in- spect matches in a user-friendly manner. Categories and Subject Descriptors H.2.4 [Database Management]: Systems—Query pro- cessing Keywords schemaless graph querying; keyword query; graph databases 1. INTRODUCTION Graph querying is widely adopted to retrieve information from emerging graph databases, e.g., knowledge graphs, in- Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full cita- tion on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or re- publish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. SIGMOD’14, June 22–27, 2014, Snowbird, UT, USA. Copyright 2014 ACM 978-1-4503-2376-5/14/06 ...$15.00. http://dx.doi.org/10.1145/2588555.2594516. formation and social networks. Given a query, it is to find reasonable top answers from a data graph. Searching real- life graphs, nevertheless, is not an easy task especially for non-professional users. (1) Either no standard schema is available, or schemas become too complicated for users to completely possess. (2) Graph queries are hard to write and interpret. Structured queries (e.g., XQuery [4] and SPARQL [5,7]) require the expertise in complex grammars, while keyword queries [8, 10] can be too ambiguous to re- flect user search intent. Moreover, most of these methods adopt predefined ranking models [5, 8], which is barely able to bridge the gap between queries and their desired matches. (3) Moreover, it is a daunting task for users to inspect a large number of matches produced from querying large-scale het- erogeneous graph data. "Jaguar" "history" "America" "Jaguar" ... "Panthera On ca" Panthera "Black Panther" Melanism ontology Query result summarization G history habitat "America" (continent) offer company city history Black Panther (animal) history habitat south American (continent) Panthera On ca (animal) history habitat north American (continent) result 1 result 2 result 3 USA (country) Jaguar XK (car) result Dearborn (country) United States history (animal) Ford (company) (city) "America" (country) Chicago(city) offer 1 ... offer n (city) New York k ... (car) XJ Line schemaless & structureless search ontology-based search Figure 1: Searching a knowledge graph Example 1: Consider a query asking “tell me about the his- tory of Jaguar in America”. The query can be presented as either a keyword query“Jaguar history America”, or a small graph in Fig. 1. To find answers for such a simple query is, nevertheless, not easy. (1) A keyword e.g., “Jaguar” may not have identical matches, but instead can be matched with entities that are semantically close, i.e., luxury cars or ani- mals. How to find matches that are semantically related to the query? (2) A large number of possible answers can be identified by various matching mechanisms. For example, “Panthera Onca” is closely related with “Jaguar” as its sci- entific name, while “Jaguar XK” is another match obtained simply by string transformations. Which one is better? A ranking model should be employed and tuned with or with- out manual tuning effort. (3) There are a large number of good results, e.g., different species related to Jaguar (result 893