43,760 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
Entity Query Feature Expansion Using Knowledge Base Links
Recent advances in automatic entity linking and knowledge base
construction have resulted in entity annotations for document and
query collections. For example, annotations of entities from large
general purpose knowledge bases, such as Freebase and the Google
Knowledge Graph. Understanding how to leverage these entity
annotations of text to improve ad hoc document retrieval is an open
research area. Query expansion is a commonly used technique to
improve retrieval effectiveness. Most previous query expansion
approaches focus on text, mainly using unigram concepts. In this
paper, we propose a new technique, called entity query feature
expansion (EQFE) which enriches the query with features from
entities and their links to knowledge bases, including structured
attributes and text. We experiment using both explicit query entity
annotations and latent entities. We evaluate our technique on TREC
text collections automatically annotated with knowledge base entity
links, including the Google Freebase Annotations (FACC1) data.
We find that entity-based feature expansion results in significant
improvements in retrieval effectiveness over state-of-the-art text
expansion approaches
On the Impact of Entity Linking in Microblog Real-Time Filtering
Microblogging is a model of content sharing in which the temporal locality of
posts with respect to important events, either of foreseeable or unforeseeable
nature, makes applica- tions of real-time filtering of great practical
interest. We propose the use of Entity Linking (EL) in order to improve the
retrieval effectiveness, by enriching the representation of microblog posts and
filtering queries. EL is the process of recognizing in an unstructured text the
mention of relevant entities described in a knowledge base. EL of short pieces
of text is a difficult task, but it is also a scenario in which the information
EL adds to the text can have a substantial impact on the retrieval process. We
implement a start-of-the-art filtering method, based on the best systems from
the TREC Microblog track realtime adhoc retrieval and filtering tasks , and
extend it with a Wikipedia-based EL method. Results show that the use of EL
significantly improves over non-EL based versions of the filtering methods.Comment: 6 pages, 1 figure, 1 table. SAC 2015, Salamanca, Spain - April 13 -
17, 201
Multi-Source Spatial Entity Linkage
Besides the traditional cartographic data sources, spatial information can
also be derived from location-based sources. However, even though different
location-based sources refer to the same physical world, each one has only
partial coverage of the spatial entities, describe them with different
attributes, and sometimes provide contradicting information. Hence, we
introduce the spatial entity linkage problem, which finds which pairs of
spatial entities belong to the same physical spatial entity. Our proposed
solution (QuadSky) starts with a time-efficient spatial blocking technique
(QuadFlex), compares pairwise the spatial entities in the same block, ranks the
pairs using Pareto optimality with the SkyRank algorithm, and finally,
classifies the pairs with our novel SkyEx-* family of algorithms that yield
0.85 precision and 0.85 recall for a manually labeled dataset of 1,500 pairs
and 0.87 precision and 0.6 recall for a semi-manually labeled dataset of
777,452 pairs. Moreover, we provide a theoretical guarantee and formalize the
SkyEx-FES algorithm that explores only 27% of the skylines without any loss in
F-measure. Furthermore, our fully unsupervised algorithm SkyEx-D approximates
the optimal result with an F-measure loss of just 0.01. Finally, QuadSky
provides the best trade-off between precision and recall, and the best
F-measure compared to the existing baselines and clustering techniques, and
approximates the results of supervised learning solutions
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