Merging and Ranking Answers in the Semantic Web: The Wisdom of Crowds

López, Vanessa; Nikolov, Andriy; Fernández, Miriam; Sabou, Marta; Uren, Victoria; Motta, Enrico

doi:10.1007/978-3-642-10871-6_10

Cited by 19 publications

(23 citation statements)

References 10 publications

(12 reference statements)

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“…In case of the system failing to automatically interpret the question, it can prompt the user with the dialog as is the case with FREyA. The fine balance is in the combination of these approaches: disambiguate as much as possible and use the ranking mechanisms (e.g., those that exist in FREyA, or any other methods for effective ranking such as [13]), and correct them if necessary using the interactive features of FREyA.…”

Section: Querying Linked Data With Freyamentioning

confidence: 99%

FREyA: An Interactive Way of Querying Linked Data Using Natural Language

Damljanović

Agatonović²,

Cunningham

2012

Lecture Notes in Computer Science

116

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Abstract. Natural Language Interfaces are increasingly relevant for information systems fronting rich structured data stores such as RDF and OWL repositories, mainly because of the conception of them being intuitive for human. In the previous work, we developed FREyA, an interactive Natural Language Interface for querying ontologies. It uses syntactic parsing in combination with the ontology-based lookup in order to interpret the question, and involves the user if necessary. The user's choices are used for training the system in order to improve its performance over time. In this paper, we discuss the suitability of FREyA to query the Linked Open Data. We report its performance in terms of precision and recall using the MusicBrainz and DBpedia datasets.

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Section: Querying Linked Data With Freyamentioning

confidence: 99%

FREyA: An Interactive Way of Querying Linked Data Using Natural Language

Damljanović

Agatonović²,

Cunningham

2012

Lecture Notes in Computer Science

116

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“…Gingseng does not provide full natural language interface, but rather guides user input with respect to the considered ontologies, suggesting term and relations present in the underlying ontologies. PowerAqua [12] implements a strategy for (i) combining and (ii) ranking answers from distributed heterogeneous data sources in the context of a multi-ontology Question Answering task. The result list integrates triples coming from different data stores, ranked using a multi-level ranking algorithm, which combines three different criteria (popularity, confidence and semantic interpretation).…”

Section: Related Workmentioning

confidence: 99%

“…More advanced approaches therefore enrich their query with semantically similar words. For example, by looking up synonyms in a dictionary, often WordNet ( [18], [17], [12]), but dictionaries in general contain a very limited vocabulary and even WordNet has very few named entities.…”

Section: Introductionmentioning

confidence: 99%

Mapping Keywords to Linked Data Resources for Automatic Query Expansion

Augenstein

Gentile

Norton³

et al. 2013

Advanced Information Systems Engineering

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Abstract. Linked Data is a gigantic, constantly growing and extremely valuable resource, but its usage is still heavily dependent on (i) the familiarity of end users with RDF's graph data model and its query language, SPARQL, and (ii) knowledge about available datasets and their contents. Intelligent keyword search over Linked Data is currently being investigated as a means to overcome these barriers to entry in a number of different approaches, including semantic search engines and the automatic conversion of natural language questions into structured queries. Our work addresses the specific challenge of mapping keywords to Linked Data resources, and proposes a novel method for this task. By exploiting the graph structure within Linked Data we determine which properties between resources are useful to discover, or directly express, semantic similarity. We also propose a novel scoring function to rank results. Experiments on a publicly available dataset show a 17% improvement in Mean Reciprocal Rank over the state of the art.

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“…Lopez et al [11] and later by Damljanovic et al [4], who extend work from [18] by allowing user feedback, query refinement, and query expansion. In contrast to the most of other systems, PowerAqua [10] (building on [11]) is able to work with multiple heterogeneous ontologies. It transforms the input keyword query into the intermediate triple form, similarly to the principle of other approaches, the intermediate format is than mapped to the candidate entities in distinct ontologies.…”

Section: Related Workmentioning

confidence: 99%

The SemSets model for ad-hoc semantic list search

Ciglan

Nørvåg

Hluchý

2012

Proceedings of the 21st International Conference on World Wide Web

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The amount of semantic data on the web has been growing rapidly in recent years. One of the key challenges triggered by this growth is the ad-hoc querying, i.e., the ability to retrieve answers from semantic resources using natural language queries. This facilitates interaction with semantic resources for the users so they can benefit from the knowledge covered by semantic data without the complexities of semantic query languages. In this paper, we focus on semantic queries, where the aim is to retrieve objects belonging to a set of semantically related entities. An example of such an adhoc type query is "Apollo astronauts who walked on the Moon". In order to address the task, we propose the SemSets retrieval model that exploits and combines traditional document-based information retrieval, link structure of the semantic data and entity membership in semantic sets, in order to provide the answers. The novelty of the approach lies in the utilization of semantic sets, i.e., groups of semantically related entities. We propose two approaches to identify such semantic sets from the knowledge bases; the first one requires involvement of an expert user knowledgeable of the data set structure, the second one is fully automatic and provides results that are comparable with those delivered by the expert users. As demonstrated in the experimental evaluation, the proposed model has the state-of-the-art performance on the SemSearch2011 data set, which has been designed especially for the semantic list search evaluation.

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Merging and Ranking Answers in the Semantic Web: The Wisdom of Crowds

Cited by 19 publications

References 10 publications

FREyA: An Interactive Way of Querying Linked Data Using Natural Language

FREyA: An Interactive Way of Querying Linked Data Using Natural Language

Mapping Keywords to Linked Data Resources for Automatic Query Expansion

The SemSets model for ad-hoc semantic list search

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