2,108 research outputs found
Multi modal multi-semantic image retrieval
PhDThe rapid growth in the volume of visual information, e.g. image, and video can
overwhelm usersâ ability to find and access the specific visual information of interest
to them. In recent years, ontology knowledge-based (KB) image information retrieval
techniques have been adopted into in order to attempt to extract knowledge from these
images, enhancing the retrieval performance. A KB framework is presented to
promote semi-automatic annotation and semantic image retrieval using multimodal
cues (visual features and text captions). In addition, a hierarchical structure for the KB
allows metadata to be shared that supports multi-semantics (polysemy) for concepts.
The framework builds up an effective knowledge base pertaining to a domain specific
image collection, e.g. sports, and is able to disambiguate and assign high level
semantics to âunannotatedâ images.
Local feature analysis of visual content, namely using Scale Invariant Feature
Transform (SIFT) descriptors, have been deployed in the âBag of Visual Wordsâ
model (BVW) as an effective method to represent visual content information and to
enhance its classification and retrieval. Local features are more useful than global
features, e.g. colour, shape or texture, as they are invariant to image scale, orientation
and camera angle. An innovative approach is proposed for the representation,
annotation and retrieval of visual content using a hybrid technique based upon the use
of an unstructured visual word and upon a (structured) hierarchical ontology KB
model. The structural model facilitates the disambiguation of unstructured visual
words and a more effective classification of visual content, compared to a vector
space model, through exploiting local conceptual structures and their relationships.
The key contributions of this framework in using local features for image
representation include: first, a method to generate visual words using the semantic
local adaptive clustering (SLAC) algorithm which takes term weight and spatial
locations of keypoints into account. Consequently, the semantic information is
preserved. Second a technique is used to detect the domain specific ânon-informative
visual wordsâ which are ineffective at representing the content of visual data and
degrade its categorisation ability. Third, a method to combine an ontology model with
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a visual word model to resolve synonym (visual heterogeneity) and polysemy
problems, is proposed. The experimental results show that this approach can discover
semantically meaningful visual content descriptions and recognise specific events,
e.g., sports events, depicted in images efficiently.
Since discovering the semantics of an image is an extremely challenging problem, one
promising approach to enhance visual content interpretation is to use any associated
textual information that accompanies an image, as a cue to predict the meaning of an
image, by transforming this textual information into a structured annotation for an
image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct
types of information representation and modality, there are some strong, invariant,
implicit, connections between images and any accompanying text information.
Semantic analysis of image captions can be used by image retrieval systems to
retrieve selected images more precisely. To do this, a Natural Language Processing
(NLP) is exploited firstly in order to extract concepts from image captions. Next, an
ontology-based knowledge model is deployed in order to resolve natural language
ambiguities. To deal with the accompanying text information, two methods to extract
knowledge from textual information have been proposed. First, metadata can be
extracted automatically from text captions and restructured with respect to a semantic
model. Second, the use of LSI in relation to a domain-specific ontology-based
knowledge model enables the combined framework to tolerate ambiguities and
variations (incompleteness) of metadata. The use of the ontology-based knowledge
model allows the system to find indirectly relevant concepts in image captions and
thus leverage these to represent the semantics of images at a higher level.
Experimental results show that the proposed framework significantly enhances image
retrieval and leads to narrowing of the semantic gap between lower level machinederived
and higher level human-understandable conceptualisation
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and âenablersâ, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
Exploiting Ontology Recommendation Using Text Categorization Approach
Semantic Web is considered as the backbone of web 3.0 and ontologies are an integral part of the Semantic Web. Though an increase of ontologies in different domains is reported due to various benefits which include data heterogeneity, automated information analysis, and reusability, however, finding an appropriate ontology according to user requirement remains cumbersome task due to time and efforts required, context-awareness, and computational complexity. To overcome these issues, an ontology recommendation framework is proposed. The Proposed framework employs text categorization and unsupervised learning techniques. The benefits of the proposed framework are twofold: 1) ontology organization according to the opinion of domain experts and 2) ontology recommendation with respect to user requirement. Moreover, an evaluation model is also proposed to assess the effectiveness of the proposed framework in terms of ontologies organization and recommendation. The main consequences of the proposed framework are 1) ontologies of a corpus can be organized effectively, 2) no effort and time are required to select an appropriate ontology, 3) computational complexity is only limited to the use of unsupervised learning techniques, and 4) due to no requirement of context awareness, the proposed framework can be effective for any corpus or online libraries of ontologies
Expanding the Usage of Web Archives by Recommending Archived Webpages Using Only the URI
Web archives are a window to view past versions of webpages. When a user requests a webpage on the live Web, such as http://tripadvisor.com/where_to_t ravel/, the webpage may not be found, which results in an HyperText Transfer Protocol (HTTP) 404 response. The user then may search for the webpage in a Web archive, such as the Internet Archive. Unfortunately, if this page had never been archived, the user will not be able to view the page, nor will the user gain any information on other webpages that have similar content in the archive, such as the archived webpage http://classy-travel.net. Similarly, if the user requests the webpage http://hokiesports.com/football/ from the Internet Archive, the user will only find the requested webpage, and the user will not gain any information on other webpages that have similar content in the archive, such as the archived webpage http://techsideline.com. In this research, we will build a model for selecting and ranking possible recommended webpages at a Web archive. This is to enhance both HTTP 404 responses and HTTP 200 responses by surfacing webpages in the archive that the user may not know existed. First, we detect semantics in the requested Uniform Resource Identifier (URI). Next, we classify the URI using an ontology, such as DMOZ or any website directory. Finally, we filter and rank candidates based on several features, such as archival quality, webpage popularity, temporal similarity, and content similarity. We measure the performance of each step using different techniques, including calculating the F1 to measure of different tokenization methods and the classification. We tested the model using human evaluation to determine if we could classify and find recommendations for a sample of requests from the Internet Archiveâs Wayback Machine access log. Overall, when selecting the full categorization, reviewers agreed with 80.3% of the recommendations, which is much higher than âdo not agreeâ and âI do not knowâ. This indicates the reviewer is more likely to agree on the recommendations when selecting the full categorization. But when selecting the first level only, reviewers only agreed with 25.5% of the recommendations. This indicates that having deep level categorization improves the performance of finding relevant recommendations
Query Expansion Techniques for Enterprise Search
Although web search remains an active research area, interest in enterprise search has waned. This is despite the fact that the market for enterprise search applications is expected to triple within the next six years, and that knowledge workers spend an average of 1.6 to 2.5 hours each day searching for information. To improve search relevancy, and hence reduce this time, an enterprise- focused application must be able to handle the unique queries and constraints of the enterprise environment. The goal of this thesis research was to develop, implement, and study query expansion techniques that are most effective at improving relevancy in enterprise search.
The case-study instrument used in this investigation was a custom Apache Solr-based search application deployed at a local medium-sized manufacturing company. It was hypothesized that techniques specifically tailored to the enterprise search environment would prove most effective. Query expansion techniques leveraging entity recognition, alphanumeric term identification, intent classification, collection enrichment, and word vectors were implemented and studied using real enterprise data. They were evaluated against a test set of queries developed using relevance survey results from multiple users, using standard relevancy metrics such as normalized discounted cumulative gain (nDCG). Comprehensive analysis revealed that the current implementation of the collection enrichment and word vector query expansion modules did not demonstrate meaningful improvements over the baseline methods. However, the entity recognition, alphanumeric term identification, and query intent classification modules produced meaningful and statistically significant improvements in relevancy, allowing us to accept the hypothesis
Information retrieval and text mining technologies for chemistry
Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European
Communityâs Horizon 2020 Program (project reference:
654021 - OpenMinted). M.K. additionally acknowledges the
Encomienda MINETAD-CNIO as part of the Plan for the
Advancement of Language Technology. O.R. and J.O. thank
the Foundation for Applied Medical Research (FIMA),
University of Navarra (Pamplona, Spain). This work was
partially funded by ConselleriÌa
de Cultura, EducacioÌn e OrdenacioÌn Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic
funding of UID/BIO/04469/2013 unit and COMPETE 2020
(POCI-01-0145-FEDER-006684). We thank InÌigo GarciaÌ -Yoldi
for useful feedback and discussions during the preparation of
the manuscript.info:eu-repo/semantics/publishedVersio
Semantic multimedia analysis using knowledge and context
PhDThe difficulty of semantic multimedia analysis can be attributed to the
extended diversity in form and appearance exhibited by the majority of
semantic concepts and the difficulty to express them using a finite number
of patterns. In meeting this challenge there has been a scientific debate
on whether the problem should be addressed from the perspective of using
overwhelming amounts of training data to capture all possible instantiations
of a concept, or from the perspective of using explicit knowledge about
the conceptsâ relations to infer their presence. In this thesis we address
three problems of pattern recognition and propose solutions that combine
the knowledge extracted implicitly from training data with the knowledge
provided explicitly in structured form. First, we propose a BNs modeling
approach that defines a conceptual space where both domain related evi-
dence and evidence derived from content analysis can be jointly considered
to support or disprove a hypothesis. The use of this space leads to sig-
nificant gains in performance compared to analysis methods that can not
handle combined knowledge. Then, we present an unsupervised method
that exploits the collective nature of social media to automatically obtain
large amounts of annotated image regions. By proving that the quality of
the obtained samples can be almost as good as manually annotated images
when working with large datasets, we significantly contribute towards scal-
able object detection. Finally, we introduce a method that treats images,
visual features and tags as the three observable variables of an aspect model
and extracts a set of latent topics that incorporates the semantics of both
visual and tag information space. By showing that the cross-modal depen-
dencies of tagged images can be exploited to increase the semantic capacity
of the resulting space, we advocate the use of all existing information facets
in the semantic analysis of social media
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