Semantic search engines, which integrate the output of text mining (TM) methods, can significantly increase ease and efficiency finding relevant documents locating important information within them. We present a novel engine for construction industry, HSEarch (http://www.nactem.ac.uk/hse/), uses TM methods to provide semantically-enhanced, faceted over repository workplace accident reports. Com...

The sheer increase in volume of RDF data demands efficient solutions for the triple indexing problem, that is to devise a compressed structure compactly represent triples by guaranteeing, at same time, fast pattern matching operations. This problem lies heart delivering good practical performance resolution complex SPARQL queries on large datasets. In this work, we propose trie-based index layo...

Semantic search augments the goals and scope of traditional search and information retrieval tasks. The paper analyzes the main aspects of semantic search and the corresponding features of some popular semantic search systems. The presentation is focused on a discussion of the implementation of a successful project in building modern semantic search engine. A brief analysis of the gained experi...

Approaches towards semantic search can be mainly divided into two kinds: one is augmenting traditional keyword-based search engines with semantic techniques such as ontology, semantic inference; the other is proposing methods for directly searching on semantic repositories as RDF or OWL files. However, compared with those theoretics-driven approaches, semantic search still lacks applications wi...

Traditional search engines based on syntactic search are unable to solve key issues like synonymy and polysemy. Solving these issues leads to the invention of the semantic web. The semantic search engines indeed overcome these issues. Nowadays the most important part of the data remains unstructured documents. It is consequently very time consuming to annotate such big data. Concept based retri...