Ontology‐lexicon–based question answering over linked data

Abstract

Recently, Linked Open Data has become a large set of knowledge bases. Therefore, the need to query Linked Data using question answering (QA) techniques has attracted the attention of many researchers. A QA system translates natural language questions into structured queries, such as SPARQL queries, to be executed over Linked Data. The two main challenges in such systems are lexical and semantic gaps. A lexical gap refers to the difference between the vocabularies used in an input question and those used in the knowledge base. A semantic gap refers to the difference between expressed information needs and the representation of the knowledge base. In this paper, we present a novel method using an ontology lexicon and dependency parse trees to overcome lexical and semantic gaps. The proposed technique is evaluated on the QALD‐5 benchmark and exhibits promising results.

Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: A Review

“…Recently, LOD has become a large KB, and interest in querying these data using QA techniques has attracted many researchers. Linked data possesses two known challenges: the lexical and semantic gaps [62]. The lexical gap is primarily the difference between the vocabularies used in an input question and those used in the KB, while the semantic gap is the difference between the KB representation and the information that needs expressing.…”

“…The lexical gap is primarily the difference between the vocabularies used in an input question and those used in the KB, while the semantic gap is the difference between the KB representation and the information that needs expressing. The study conducted by [62] provided a novel method for querying these data, using ontology lexicon and dependency parse trees to overcome the lexical and semantic gaps. The system architecture component was constituted of six components.…”

“…This is primarily because the existing techniques used by the system cannot address the effectiveness and efficiency of semantic issues in their NLQ analysis. Some scholars [11,79,80] reported positive outcomes to lexical solutions on the topic of word sense disambiguation (WSD), and several studies [42,62] appraised this issue in the context of QA, but only a few examined the role of WSD when returning potential answers [12,81].…”

“…Alternative systems discussed take into account QA as a semantic parsing issue and aim to train a semantic parser to plan input questions in suitable logical forms to be performed over a base of knowledge. These systems require identification of important data when training, which is time-consuming and labour intensive [62]. The QA systems based on IR engines require that text wording comprising an answer should be identical to the wording employed in the question [78].…”

Comprehensive Knowledge Archive Network harvester improvement for efficient open‐data collection and management

“…CKAN has more than 230 functions registered as extensions, including visualization, document preview, custom theme, other storage, site link, and metadata management. Representative examples include the DCAT Extension for linked data [17,18], CKAN harvester or DCAT [19] RDF/ JSON harvester for data collection, and DB Extension for storage linkage to MongoDB [20] or Amazon S3 [21]. We analyzed herein the following extensions related to harvesting.…”