3,671 research outputs found
Semantic keyword search for expert witness discovery
In the last few years, there has been an increase in the amount of information stored in semantically enriched knowledge bases, represented in RDF format. These improve the accuracy of search results when the queries are semantically formal. However framing such queries is inappropriate for inexperience users because they require specialist knowledge of ontology and syntax. In this paper, we explore an approach that automates the process of converting a conventional keyword search into a semantically formal query in order to find an expert on a semantically enriched knowledge base. A case study on expert witness discovery for the resolution of a legal dispute is chosen as the domain of interest and a system named SKengine is implemented to illustrate the approach. As well as providing an easy user interface, our experiment shows that SKengine can retrieve expert witness information with higher precision and higher recall, compared with the other system, with the same interface, implemented by a vector model approach
Semantic keyword search for expert witness discovery
In the last few years, there has been an increase in the amount of information stored in semantically enriched knowledge bases, represented in RDF format. These improve the accuracy of search results when the queries are semantically formal. However framing such queries is inappropriate for inexperience users because they require specialist knowledge of ontology and syntax. In this paper, we explore an approach that automates the process of converting a conventional keyword search into a semantically formal query in order to find an expert on a semantically enriched knowledge base. A case study on expert witness discovery for the resolution of a legal dispute is chosen as the domain of interest and a system named SKengine is implemented to illustrate the approach. As well as providing an easy user interface, our experiment shows that SKengine can retrieve expert witness information with higher precision and higher recall, compared with the other system, with the same interface, implemented by a vector model approach
Ranking Archived Documents for Structured Queries on Semantic Layers
Archived collections of documents (like newspaper and web archives) serve as
important information sources in a variety of disciplines, including Digital
Humanities, Historical Science, and Journalism. However, the absence of
efficient and meaningful exploration methods still remains a major hurdle in
the way of turning them into usable sources of information. A semantic layer is
an RDF graph that describes metadata and semantic information about a
collection of archived documents, which in turn can be queried through a
semantic query language (SPARQL). This allows running advanced queries by
combining metadata of the documents (like publication date) and content-based
semantic information (like entities mentioned in the documents). However, the
results returned by such structured queries can be numerous and moreover they
all equally match the query. In this paper, we deal with this problem and
formalize the task of "ranking archived documents for structured queries on
semantic layers". Then, we propose two ranking models for the problem at hand
which jointly consider: i) the relativeness of documents to entities, ii) the
timeliness of documents, and iii) the temporal relations among the entities.
The experimental results on a new evaluation dataset show the effectiveness of
the proposed models and allow us to understand their limitation
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