Interpreting keyword queries over web knowledge bases

Pound, Jeffrey; Hudek, Alexander K.; Ilyas, Ihab F.; Weddell, Grant E.

doi:10.1145/2396761.2396803

Cited by 53 publications

(49 citation statements)

References 26 publications

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“…For the telegraphic query workload of Pound et al [34], SPOX+Relax again turned out to be the best method in our experiments. For factoid questions we achieved an MRR of 0.83, and for list questions a precision@10 of 0.73.…”

Section: Resultsmentioning

confidence: 74%

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Robust question answering over the web of linked data

Yahya

Berberich

Elbassuoni

et al. 2013

Proceedings of the 22nd ACM International Conference on Information &Amp; Knowledge Management

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Knowledge bases and the Web of Linked Data have become important assets for search, recommendation, and analytics. Natural-language questions are a user-friendly mode of tapping this wealth of knowledge and data. However, question answering technology does not work robustly in this setting as questions have to be translated into structured queries and users have to be careful in phrasing their questions. This paper advocates a new approach that allows questions to be partially translated into relaxed queries, covering the essential but not necessarily all aspects of the user's input. To compensate for the omissions, we exploit textual sources associated with entities and relational facts. Our system translates user questions into an extended form of structured SPARQL queries, with text predicates attached to triple patterns. Our solution is based on a novel optimization model, cast into an integer linear program, for joint decomposition and disambiguation of the user question. We demonstrate the quality of our methods through experiments with the QALD benchmark.

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Section: Resultsmentioning

confidence: 74%

“…In addition, we experimented with the 48 telegraphic queries used by Pound et al [34]. 19 of these queries are not real questions, but are merely entity lookups.…”

Section: Setupmentioning

confidence: 99%

Robust question answering over the web of linked data

Yahya

Berberich

Elbassuoni

et al. 2013

Proceedings of the 22nd ACM International Conference on Information &Amp; Knowledge Management

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“…Examples include, {Toyota, Honda, Sales}. However, keyword-based queries can have several acceptable inferences, and various interpretations, generating a large set indicating what that particular query could mean [34] [35].…”

Section: Ranking Factorsmentioning

confidence: 99%

Web Search Query Privacy, an End-User Perspective

Mivule¹

2017

JIS

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While search engines have become vital tools for searching information on the Internet, privacy issues remain a growing concern due to the technological abilities of search engines to retain user search logs. Although such capabilities might provide enhanced personalized search results, the confidentiality of user intent remains uncertain. Even with web search query obfuscation techniques, another challenge remains, namely, reusing the same obfuscation methods is problematic, given that search engines have enormous computation and storage resources for query disambiguation. A number of web search query privacy procedures involve the cooperation of the search engine, a nontrusted entity in such cases, making query obfuscation even more challenging. In this study, we provide a review on how search engines work in regards to web search queries and user intent. Secondly, this study reviews material in a manner accessible to those outside computer science with the intent to introduce knowledge of web search engines to enable non-computer scientists to approach web search query privacy innovatively. As a contribution, we identify and highlight areas open for further investigative and innovative research in regards to end-user personalized web search privacy-that is methods that can be executed on the user side without third party involvement such as, search engines. The goal is to motivate future web search obfuscation heuristics that give users control over their personal search privacy.

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“…Later editions of the track anchored these annotations in a knowledge base (DBpedia) [7]. Obtaining such semantic annotations for keyword queries automatically is a topic of active research, often involving methods at the intersection of information retrieval and databases [37,40].…”

Section: Further Readingmentioning

confidence: 99%

Semistructured Data Search

Balog

2014

Lecture Notes in Computer Science

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Abstract. This paper presents a selection of methods for searching in heterogeneous data collections where some amount of structure is available. We start with a general retrieval framework, based on generative probabilistic modeling, for ranking unstructured document representations. Then, we consider structure at two different levels: documents and queries. For documents, the internal structure is captured through the use of multiple document fields, and various approaches to setting field weights are discussed. For queries, the focus is on effectively utilizing additional input data that the user might provide along with the keyword query, such as target categories or example documents. We place a particular emphasis on methods that are robust with respect to the availability of structured data and are able to deal with inconsistent or incomplete information.

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Interpreting keyword queries over web knowledge bases

Cited by 53 publications

References 26 publications

Robust question answering over the web of linked data

Robust question answering over the web of linked data

Web Search Query Privacy, an End-User Perspective

Semistructured Data Search

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