SUITS: Faceted User Interface for Constructing Structured Queries from Keywords

Demidova, Elena; Zhou, Xuan; Zenz, Gideon; Nejdl, Wolfgang

doi:10.1007/978-3-642-00887-0_70

Cited by 9 publications

(8 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [11] and [38], we presented SUITS, a faceted interface that enables users to interactively disambiguate keyword queries. However, SUITS lacks a theoretical foundation for verifying its effectiveness.…”

Section: Related Workmentioning

confidence: 99%

“…However, as the framework of [35] does not take the probability of query interpretations into account, the resulting query construction process may be not optimal. Compared with [11], [35], [38], the contributions of IQ P include: 1) a probabilistic framework to define the process of incremental query construction; 2) a probabilistic model to estimate the probabilities of structural query interpretations; and 3) an algorithm for generating an optimal QCP based on Information Gain.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Probabilistic Scheme for Keyword-Based Incremental Query Construction

Demidova

Zhou

Nejdl

2012

IEEE Trans. Knowl. Data Eng.

Self Cite

View full text Add to dashboard Cite

Databases enable users to precisely express their informational needs using structured queries. However, database query construction is a laborious and error-prone process, which cannot be performed well by most end users. Keyword search alleviates the usability problem at the price of query expressiveness. As keyword search algorithms do not differentiate between the possible informational needs represented by a keyword query, users may not receive adequate results. This paper presents IQ P -a novel approach to bridge the gap between usability of keyword search and expressiveness of database queries. IQ P enables a user to start with an arbitrary keyword query and incrementally refine it into a structured query through an interactive interface. The enabling techniques of IQ P include: 1) a probabilistic framework for incremental query construction; 2) a probabilistic model to assess the possible informational needs represented by a keyword query; 3) an algorithm to obtain the optimal query construction process. This paper presents the detailed design of IQ P , and demonstrates its effectiveness and scalability through experiments over real-world data and a user study.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Probabilistic Scheme for Keyword-Based Incremental Query Construction

Demidova

Zhou

Nejdl

2012

IEEE Trans. Knowl. Data Eng.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The existing approaches to interactive query construction [7], [8], as well as the state-of-the-art approaches to schema-based database keyword search [2], [12], [16], [17] rely on an entirely materialized query interpretation space. These approaches can hardly work with a big schema, as it is infeasible to generate all the query interpretations at the query time.…”

Section: Problem 2 Very Large Query Interpretation Spacementioning

confidence: 99%

Efficient query construction for large scale data

Demidova

Zhou

Nejdl

2013

Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval

Self Cite

View full text Add to dashboard Cite

In recent years, a number of open databases have emerged on the Web, providing Web users with platforms to collaboratively create structured information. As these databases are intended to accommodate heterogeneous information and knowledge, they usually comprise a very large schema and billions of instances. Browsing and searching data on such a scale is not an easy task for a Web user. In this context, interactive query construction offers an intuitive interface for novice users to retrieve information from databases neither requiring any knowledge of structured query languages, nor any prior knowledge of the database schema. However, the existing mechanisms do not scale well on large scale datasets. This paper presents a set of techniques to boost the scalability of interactive query construction, from the perspective of both, user interaction cost and performance. We connect an abstract ontology layer to the database schema to shorten the process of user-computer interaction. We also introduce a search mechanism to enable efficient exploration of query interpretation spaces over large scale data. Extensive experiments show that our approach scales well on Freebase -an open database containing more than 7,000 relational tables in more than 100 domains.

show abstract

“…Some recent efforts such as Keymantic [5] QUICK [13], SUITS [6], Q2Semantics [12] and [11] have focused on introducing an explicit keyword interpretation phase prior to answering the query. The general approach used is to find the "best " sub-graphs (of the schema plus a data graph) connecting the given keywords and represent the intended query meaning.…”

Section: Related Workmentioning

confidence: 99%

“…However, these techniques are based on fixed data-driven heuristics and do not adapt to varying user needs. Alternative approaches [6] [13] use techniques that incorporate user input to incrementally construct queries by providing them with query templates. The limitation of these techniques is the extra burden they place on users.…”

Section: Related Workmentioning

confidence: 99%

Effectively Interpreting Keyword Queries on RDF Databases with a Rear View

Anyanwu

2011

The Semantic Web – ISWC 2011

View full text Add to dashboard Cite

Abstract. Effective techniques for keyword search over RDF databases incorporate an explicit interpretation phase that maps keywords in a keyword query to structured query constructs. Because of the ambiguity of keyword queries, it is often not possible to generate a unique interpretation for a keyword query. Consequently, heuristics geared toward generating the top-K likeliest user-intended interpretations have been proposed. However, heuristics currently proposed fail to capture any user-dependent characteristics, but rather depend on database-dependent properties such as occurrence frequency of subgraph pattern connecting keywords. This leads to the problem of generating top-K interpretations that are not aligned with user intentions. In this paper, we propose a context-aware approach for keyword query interpretation that personalizes the interpretation process based on a user's query context. Our approach addresses the novel problem of using a sequence of structured queries corresponding to interpretations of keyword queries in the query history as contextual information for biasing the interpretation of a new query. Experimental results presented over DBPedia dataset show that our approach outperforms the state-of-the-art technique on both efficiency and effectiveness, particularly for ambiguous queries.

show abstract

SUITS: Faceted User Interface for Constructing Structured Queries from Keywords

Cited by 9 publications

References 2 publications

A Probabilistic Scheme for Keyword-Based Incremental Query Construction

A Probabilistic Scheme for Keyword-Based Incremental Query Construction

Efficient query construction for large scale data

Effectively Interpreting Keyword Queries on RDF Databases with a Rear View

Contact Info

Product

Resources

About