From Keywords to Semantic Queries   Incremental Query Construction on the Semantic Web

Zenz, Gideon; Zhou, Xuan; Minack, Enrico; Siberski, Wolf; Nejdl, Wolfgang

doi:10.2139/ssrn.3199425

Cited by 25 publications

(35 citation statements)

References 22 publications

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“…Here, most of the work has focused on profiling the data itself, such as [1,4,8]. However, analyzing and profiling actual queries on Linked Data has recently also spawned a number of applications, such as Sparql benchmarking [3,12] or providing query suggestions [9,16].…”

Section: Sparql Query Profilingmentioning

confidence: 99%

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Detecting SPARQL Query Templates for Data Prefetching

Lorey

Naumann

2013

The Semantic Web: Semantics and Big Data

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Abstract. Publicly available Linked Data repositories provide a multitude of information. By utilizing Sparql, Web sites and services can consume this data and present it in a user-friendly form, e.g., in mashups. To gather RDF triples for this task, machine agents typically issue similarly structured queries with recurring patterns against the Sparql endpoint. These queries usually differ only in a small number of individual triple pattern parts, such as resource labels or literals in objects. We present an approach to detect such recurring patterns in queries and introduce the notion of query templates, which represent clusters of similar queries exhibiting these recurrences. We describe a matching algorithm to extract query templates and illustrate the benefits of prefetching data by utilizing these templates. Finally, we comment on the applicability of our approach using results from real-world Sparql query logs.

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Section: Sparql Query Profilingmentioning

confidence: 99%

“…The authors claim that their approach benefits users who have no knowledge of the underlying schema or the Sparql query language. A similar approach in [16] allows users to refine an initial query based on keywords.…”

Section: Sparql Query Profilingmentioning

confidence: 99%

Detecting SPARQL Query Templates for Data Prefetching

Lorey

Naumann

2013

The Semantic Web: Semantics and Big Data

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“…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

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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.

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“…In this category, we distinguish keyword-based approaches, such as [13,33,34,40,51,52,55], where queries consist of lists of keywords, and natural-language-based approaches, such as [16,23,29,32,42,43], where users can express queries in natural language. Finally, we also discuss related work on ranking techniques for the Semantic Web.…”

Section: Related Workmentioning

confidence: 99%

Semantic Web Search Based on Ontological Conjunctive Queries

Fazzinga

Gianforme

Gottlob

et al. 2010

Lecture Notes in Computer Science

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Abstract. Many experts predict that the next huge step forward in Web information technology will be achieved by adding semantics to Web data, and will possibly consist of (some form of) the Semantic Web. In this paper, we present a novel approach to Semantic Web search, called Serene, which allows for a semantic processing of Web search queries, and for evaluating complex Web search queries that involve reasoning over the Web. More specifically, we first add ontological structure and semantics to Web pages, which then allows for both attaching a meaning to Web search queries and Web pages, and for formulating and processing ontology-based complex Web search queries (i.e., conjunctive queries) that involve reasoning over the Web. Here, we assume the existence of an underlying ontology (in a lightweight ontology language) relative to which Web pages are annotated and Web search queries are formulated. Depending on whether we use a general or a specialized ontology, we thus obtain a general or a vertical Semantic Web search interface, respectively. That is, we are actually mapping the Web into an ontological knowledge base, which then allows for Semantic Web search relative to the underlying ontology. The latter is then realized by reduction to standard Web search on standard Web pages and logically completed ontological annotations. That is, standard Web search engines are used as the main inference motor for ontologybased Semantic Web search. We develop the formal model behind this approach and also provide an implementation in desktop search. Furthermore, we report on extensive experiments, including an implemented Semantic Web search on the Internet Movie Database.

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From Keywords to Semantic Queries Incremental Query Construction on the Semantic Web

Cited by 25 publications

References 22 publications

Detecting SPARQL Query Templates for Data Prefetching

Detecting SPARQL Query Templates for Data Prefetching

Effectively Interpreting Keyword Queries on RDF Databases with a Rear View

Semantic Web Search Based on Ontological Conjunctive Queries

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