Practical and effective IR-style keyword search over semantic web

Ning, Xiaomin; Jin, Hai; Jia, Weijia; Yuan, Pingpeng

doi:10.1016/j.ipm.2008.12.005

Cited by 22 publications

(13 citation statements)

References 13 publications

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“…Additionally, since keywords provide an easy and familiar means for data access [18], [23], some studies have proposed keyword-based linked data retrieval [5], [8], [36], or have advocated utilizing the advantages of keywords [6], [17], [20], [28], [35] when creating linked data access paradigms.…”

Section: Related Workmentioning

confidence: 99%

“…As a result, finding links on linked data is often difficult, especially for general-purpose users who have very little knowledge about the internal structure of linked data, such as schema infor- mation or structured query language (SQL) type expressive queries (RQL, RDQL, or SPARQL [24]). Keyword-based link data access methods are considered easy to use because of their familiarity [18], [23]. However, such data access options are different from other traditional keyword-based data retrieval types because they require adapting keywords to semantics.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Inclusion of Semantics over Keyword-Based Linked Data Retrieval

Rahoman

Ichise

2014

IEICE Trans. Inf. & Syst.

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Md-Mizanur RAHOMAN†a) , Nonmember and Ryutaro ICHISE †, † †b) , Member SUMMARY Keyword-based linked data information retrieval is an easy choice for general-purpose users, but the implementation of such an approach is a challenge because mere keywords do not hold semantic information. Some studies have incorporated templates in an effort to bridge this gap, but most such approaches have proven ineffective because of inefficient template management. Because linked data can be presented in a structured format, we can assume that the data's internal statistics can be used to effectively influence template management. In this work, we explore the use of this influence for template creation, ranking, and scaling. Then, we demonstrate how our proposal for automatic linked data information retrieval can be used alongside familiar keyword-based information retrieval methods, and can also be incorporated alongside other techniques, such as ontology inclusion and sophisticated matching, in order to achieve increased levels of performance. key words: linked data, keyword, information access, data statistics IntroductionThe Linked Open Data [4] initiative, where data are connected in a network-like structure [7] and which was motivated by the potential for link construction and identification among various data, has opened new worlds in data usage. The concept of this storage paradigm deviates from traditional repository-centric infrastructures to an open publishing model that allows other applications to access and interpret stored data [16]. As of September 2011, 295 knowledge-bases consisting of over 31 billion resource document framework (RDF) triples on various domains, have become interlinked via approximately 504 million RDF links * . Efficient and easy-to-use information access over linked data is now a necessity because these days such linked data hold vast amounts of knowledge. Usually, obtaining information access over a linked data network requires following links [2], [3], [15]. However, simply following links introduces a very basic problem, which is that the use of a network presentation makes it very hard to find endpoints, at least within a reasonable cost [1]. As a result, finding links on linked data is often difficult, especially for general-purpose users who have very little knowledge about the internal structure of linked data, such as schema infor- . However, such data access options are different from other traditional keyword-based data retrieval types because they require adapting keywords to semantics. Since keywords do not contain semantic information (specifically ontology information), a number of researchers have proposed automatic ontology inclusion [28] to bridge that gap. However, automatic ontology inclusion is a challenge because, in such cases, the system itself needs to incorporate ontology that is, as yet, unavailable.In attempts to resolve the abovementioned problems, such as link finding and keyword semantics inclusion, recently, a number of other researchers have worked to incorpora...

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Inclusion of Semantics over Keyword-Based Linked Data Retrieval

Rahoman

Ichise

2014

IEICE Trans. Inf. & Syst.

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“…Note, that most semantic search approaches (e.g. [16,11,18,24,17]) perform induction first on the ontology level to extract appropriate graph pattern templates, and then apply those templates to the instance level. We, however, do not separate induction in the ontology level from the instance level since ontology statements are usually available either in the knowledge base or via Linked Data de-referencing as RDF triples.…”

Section: B Terminology and Definitionsmentioning

confidence: 99%

“…(2) Detecting just entities in the user query and discovering relations between these entities by analysing the knowledge base. Examples for this second group are KIM [18] and OntoLook [13] and [20,6,24,17]. In these two approaches the RDF data is considered to be a directed graph and relations among entities are found through sequences of links (e.g.…”

Section: Related Workmentioning

confidence: 99%

Keyword-Driven SPARQL Query Generation Leveraging Background Knowledge

Shekarpour

Auer

Ngomo

et al. 2011

2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology

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Abstract-The search for information on the Web of Data is becoming increasingly difficult due to its dramatic growth. Especially novice users need to acquire both knowledge about the underlying ontology structure and proficiency in formulating formal queries (e. g. SPARQL queries) to retrieve information from Linked Data sources. So as to simplify and automate the querying and retrieval of information from such sources, we present in this paper a novel approach for constructing SPARQL queries based on user-supplied keywords. Our approach utilizes a set of predefined basic graph pattern templates for generating adequate interpretations of user queries. This is achieved by obtaining ranked lists of candidate resource identifiers for the supplied keywords and then injecting these identifiers into suitable positions in the graph pattern templates. The main advantages of our approach are that it is completely agnostic of the underlying knowledge base and ontology schema, that it scales to large knowledge bases and is simple to use. We evaluate 17 possible valid graph pattern templates by measuring their precision and recall on 53 queries against DBpedia. Our results show that 8 of these basic graph pattern templates return results with a precision above 70%. Our approach is implemented as a Web search interface and performs sufficiently fast to return instant answers to the user even with large knowledge bases.

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“…(2) Detecting just entities in the user query and discovering relations between these entities by analysing the knowledge base. Examples for this second group are KIM [18] and OntoLook [13] and [20,7,27,17]. In these two approaches the RDF data is considered to be a directed graph and relations among entities are found through sequences of links (e.g.…”

Section: State Of the Artmentioning

confidence: 99%

DC Proposal: Automatically Transforming Keyword Queries to SPARQL on Large-Scale Knowledge Bases

Shekarpour

2011

The Semantic Web – ISWC 2011

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Abstract. Most Web of Data applications focus mainly on using SPARQL for issuing queries. This leads to the Web of Data being difficult to access for non-experts. Another problem that will intensify this challenge is when applying the algorithms on large-scale and decentralized knowledge bases. In the current thesis, firstly we focus on the methods for transforming keyword-based queries into SPARQL automatically. Secondly, we will work on improving those methods in order to apply them on (a large subset of) the Linked Data Web. In an early phase, a heuristic method was proposed for generating SPARQL queries out of arbitrary number of keywords. Its preliminary evaluation showed promising results. So, we are working on the possible improvements for applying that on the large-scale knowledge bases.

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Practical and effective IR-style keyword search over semantic web

Cited by 22 publications

References 13 publications

Automatic Inclusion of Semantics over Keyword-Based Linked Data Retrieval

Automatic Inclusion of Semantics over Keyword-Based Linked Data Retrieval

Keyword-Driven SPARQL Query Generation Leveraging Background Knowledge

DC Proposal: Automatically Transforming Keyword Queries to SPARQL on Large-Scale Knowledge Bases

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