SemSearchPro – Using semantics throughout the search process

Tran, Thanh; Herzig, Daniel M.; Ladwig, Günter

doi:10.1016/j.websem.2011.08.004

Cited by 29 publications

(10 citation statements)

References 39 publications

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“…When it comes to accessing semantic data from a set of keywords, there are quite a lot different approaches, but most of them start with query building step which translates the input into an structured query [8,9,16,18]. In [16], the input is matched to semantic entities by means of text indexes, and then a set of predefined templates is used to interpret the queries in the language SeRQL.…”

Section: Related Workmentioning

confidence: 99%

“…Other relevant systems in the area of semantic search are SemSearchPro [18], Q2Semantic [11], SPARK [21], and QUICK [20]. These systems find all the paths that can be derived from a RDF graph, until a predefined depth, to generate the queries.…”

Section: Related Workmentioning

confidence: 99%

“…To manage it, several approaches have been proposed to perform keyword query interpretation [9], which is the process to translate the input set of keywords into a structured query. However, the current methods require highly expressive ontologies [8] or building large graphs out of the underlying data [18], which might not be completely available to certain users (e.g., when we only have access to a single data endpoint to which pose our queries). In the Linked Data scenario, the data to be searched is stored in RDF 2 format.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enabling keyword search on Linked Data repositories: An ontology-based approach

Bobed

Esteban

Mena

2013

KES

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The Web is experiencing a continuous change that is leading to the realization of the Semantic Web. Initiatives such as Linked Data have made a huge amount of structured information publicly available, encouraging the rest of the Internet community to tag their resources with it. Unfortunately, the amount of interlinked domains and information is so big that handling it efficiently has become really difficult for final users. Thus, we have to provide them with tools to search the needed resources in an easy way. In this paper, we propose an approach to provide users with different domain views on a general data repository, enabling them to perform both keyword and refinement searches. Our system exploits the knowledge stored in ontologies to 1) perform efficient keyword searches over a specified domain, and 2) refine the user's domain searches. In this way, we enable the definition of different semantic views on Linked Data datasets without having to change the original semantics. We present a prototype of our approach that focuses on the case of DBpedia, which provides a semantic way to access to Wikipedia.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enabling keyword search on Linked Data repositories: An ontology-based approach

Bobed

Esteban

Mena

2013

KES

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“…A recent survey (Tran et al, 2011) presented a general model for semantic search and identified different types of semantic search. In general, there are two key approaches.…”

Section: Related Workmentioning

confidence: 99%

A Linked Science investigation: enhancing climate change data discovery with semantic technologies

et al. 2013

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Linked Science is the practice of inter-connecting scientific assets by publishing, sharing and linking scientific data and processes in end-to-end loosely coupled workflows that allow the sharing and re-use of scientific data. Much of this data does not live in the cloud or on the Web, but rather in multi-institutional data centers that provide tools and add value through quality assurance, validation, curation, dissemination, and analysis of the data. In this paper, we make the case for the use of scientific scenarios in Linked Science. We propose a scenario in river-channel transport that requires biogeochemical experimental data and global climate-simulation model data from many sources. We focus on the use of ontologies—formal machine-readable descriptions of the domain—to facilitate search and discovery of this data. Mercury, developed at Oak Ridge National Laboratory, is a tool for distributed metadata harvesting, search and retrieval. Mercury currently provides uniform access to more than 100,000 metadata records; 30,000 scientists use it each month. We augmented search in Mercury with ontologies, such as the ontologies in the Semantic Web for Earth and Environmental Terminology (SWEET) collection by prototyping a component that provides access to the ontology terms from Mercury. We evaluate the coverage of SWEET for the ORNL Distributed Active Archive Center (ORNL DAAC).

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“…Some researches introduce query translation which converts a keyword-based query to a formal notation acceptable by the conventional semantic search engines [3]. To apply their approaches to mobile devices, mobile data have to be converted into a form of semantic data.…”

Section: Introductionmentioning

confidence: 99%

Keyword Based Semantic Search for Mobile Data

Shin

Eom

et al. 2014

2014 IEEE 15th International Conference on Mobile Data Management

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Most of the mobile platforms provide a keywordbased full text search (FTS) for users to find what they want. However, FTS has difficulties in dealing with the cases where a user cannot remember the exact keywords about target data or the number of search results is too many. To overcome these limitations of FTS, we propose a semantically enhanced method of searching for data on mobile devices along with mobile ontology. Experimental results of the proposed method show that our method provides accurate search results and is suitable for a mobile environment.

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SemSearchPro – Using semantics throughout the search process

Cited by 29 publications

References 39 publications

Enabling keyword search on Linked Data repositories: An ontology-based approach

Enabling keyword search on Linked Data repositories: An ontology-based approach

A Linked Science investigation: enhancing climate change data discovery with semantic technologies

Keyword Based Semantic Search for Mobile Data

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