26,668 research outputs found
Utilising semantic technologies for intelligent indexing and retrieval of digital images
The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion
Improving average ranking precision in user searches for biomedical research datasets
Availability of research datasets is keystone for health and life science
study reproducibility and scientific progress. Due to the heterogeneity and
complexity of these data, a main challenge to be overcome by research data
management systems is to provide users with the best answers for their search
queries. In the context of the 2016 bioCADDIE Dataset Retrieval Challenge, we
investigate a novel ranking pipeline to improve the search of datasets used in
biomedical experiments. Our system comprises a query expansion model based on
word embeddings, a similarity measure algorithm that takes into consideration
the relevance of the query terms, and a dataset categorisation method that
boosts the rank of datasets matching query constraints. The system was
evaluated using a corpus with 800k datasets and 21 annotated user queries. Our
system provides competitive results when compared to the other challenge
participants. In the official run, it achieved the highest infAP among the
participants, being +22.3% higher than the median infAP of the participant's
best submissions. Overall, it is ranked at top 2 if an aggregated metric using
the best official measures per participant is considered. The query expansion
method showed positive impact on the system's performance increasing our
baseline up to +5.0% and +3.4% for the infAP and infNDCG metrics, respectively.
Our similarity measure algorithm seems to be robust, in particular compared to
Divergence From Randomness framework, having smaller performance variations
under different training conditions. Finally, the result categorization did not
have significant impact on the system's performance. We believe that our
solution could be used to enhance biomedical dataset management systems. In
particular, the use of data driven query expansion methods could be an
alternative to the complexity of biomedical terminologies
A Word Sense-Oriented User Interface for Interactive Multilingual Text Retrieval
In this paper we present an interface for supporting a user in an interactive cross-language search process using semantic classes. In order to enable users to access multilingual information, different problems have to be solved: disambiguating and translating the query words, as well as categorizing and presenting the results appropriately. Therefore, we first give a brief introduction to word sense disambiguation, cross-language text retrieval and document categorization and finally describe recent achievements of our research towards an interactive multilingual retrieval system. We focus especially on the problem of browsing and navigation of the different word senses in one source and possibly several target languages. In the last part of the paper, we discuss the developed user interface and its functionalities in more detail
Intelligent indexing of crime scene photographs
The Scene of Crime Information System's automatic image-indexing prototype goes beyond extracting keywords and syntactic relations from captions. The semantic information it gathers gives investigators an intuitive, accurate way to search a database of cases for specific photographic evidence. Intelligent, automatic indexing and retrieval of crime scene photographs is one of the main functions of SOCIS, our research prototype developed within the Scene of Crime Information System project. The prototype, now in its final development and evaluation phase, applies advanced natural language processing techniques to text-based image indexing and retrieval to tackle crime investigation needs effectively and efficiently
A Relation Extraction Approach for Clinical Decision Support
In this paper, we investigate how semantic relations between concepts
extracted from medical documents can be employed to improve the retrieval of
medical literature. Semantic relations explicitly represent relatedness between
concepts and carry high informative power that can be leveraged to improve the
effectiveness of retrieval functionalities of clinical decision support
systems. We present preliminary results and show how relations are able to
provide a sizable increase of the precision for several topics, albeit having
no impact on others. We then discuss some future directions to minimize the
impact of negative results while maximizing the impact of good results.Comment: 4 pages, 1 figure, DTMBio-KMH 2018, in conjunction with ACM 27th
Conference on Information and Knowledge Management (CIKM), October 22-26
2018, Lingotto, Turin, Ital
Automatic Classification of Text Databases through Query Probing
Many text databases on the web are "hidden" behind search interfaces, and
their documents are only accessible through querying. Search engines typically
ignore the contents of such search-only databases. Recently, Yahoo-like
directories have started to manually organize these databases into categories
that users can browse to find these valuable resources. We propose a novel
strategy to automate the classification of search-only text databases. Our
technique starts by training a rule-based document classifier, and then uses
the classifier's rules to generate probing queries. The queries are sent to the
text databases, which are then classified based on the number of matches that
they produce for each query. We report some initial exploratory experiments
that show that our approach is promising to automatically characterize the
contents of text databases accessible on the web.Comment: 7 pages, 1 figur
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