75,035 research outputs found
HepData reloaded: reinventing the HEP data archive
We describe the status of the HepData database system, following a major
re-development in time for the advent of LHC data. The new HepData system
benefits from use of modern database and programming language technologies, as
well as a variety of high-quality tools for interfacing the data sources and
their presentation, primarily via the Web. The new back-end provides much more
flexible and semantic data representations than before, on which new external
applications can be built to respond to the data demands of the LHC
experimental era. The HepData re-development was largely motivated by a desire
to have a single source of reference data for Monte Carlo validation and tuning
tools, whose status and connection to HepData we also briefly review.Comment: 7 pages, 3 figures, Presented at 13th International Workshop on
Advanced Computing and Analysis Techniques in Physics Research (ACAT 2010),
February 22-27, 2010, Jaipur, Indi
Understanding, Categorizing and Predicting Semantic Image-Text Relations
Two modalities are often used to convey information in a complementary and
beneficial manner, e.g., in online news, videos, educational resources, or
scientific publications. The automatic understanding of semantic correlations
between text and associated images as well as their interplay has a great
potential for enhanced multimodal web search and recommender systems. However,
automatic understanding of multimodal information is still an unsolved research
problem. Recent approaches such as image captioning focus on precisely
describing visual content and translating it to text, but typically address
neither semantic interpretations nor the specific role or purpose of an
image-text constellation. In this paper, we go beyond previous work and
investigate, inspired by research in visual communication, useful semantic
image-text relations for multimodal information retrieval. We derive a
categorization of eight semantic image-text classes (e.g., "illustration" or
"anchorage") and show how they can systematically be characterized by a set of
three metrics: cross-modal mutual information, semantic correlation, and the
status relation of image and text. Furthermore, we present a deep learning
system to predict these classes by utilizing multimodal embeddings. To obtain a
sufficiently large amount of training data, we have automatically collected and
augmented data from a variety of data sets and web resources, which enables
future research on this topic. Experimental results on a demanding test set
demonstrate the feasibility of the approach.Comment: 8 pages, 8 Figures, 5 table
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Characterization and classification of semantic image-text relations
The beneficial, complementary nature of visual and textual information to convey information is widely known, for example, in entertainment, news, advertisements, science, or education. While the complex interplay of image and text to form semantic meaning has been thoroughly studied in linguistics and communication sciences for several decades, computer vision and multimedia research remained on the surface of the problem more or less. An exception is previous work that introduced the two metrics Cross-Modal Mutual Information and Semantic Correlation in order to model complex image-text relations. In this paper, we motivate the necessity of an additional metric called Status in order to cover complex image-text relations more completely. This set of metrics enables us to derive a novel categorization of eight semantic image-text classes based on three dimensions. In addition, we demonstrate how to automatically gather and augment a dataset for these classes from the Web. Further, we present a deep learning system to automatically predict either of the three metrics, as well as a system to directly predict the eight image-text classes. Experimental results show the feasibility of the approach, whereby the predict-all approach outperforms the cascaded approach of the metric classifiers
Hypotheses, evidence and relationships: The HypER approach for representing scientific knowledge claims
Biological knowledge is increasingly represented as a collection of (entity-relationship-entity) triplets. These are queried, mined, appended to papers, and published. However, this representation ignores the argumentation contained within a paper and the relationships between hypotheses, claims and evidence put forth in the article. In this paper, we propose an alternate view of the research article as a network of 'hypotheses and evidence'. Our knowledge representation focuses on scientific discourse as a rhetorical activity, which leads to a different direction in the development of tools and processes for modeling this discourse. We propose to extract knowledge from the article to allow the construction of a system where a specific scientific claim is connected, through trails of meaningful relationships, to experimental evidence. We discuss some current efforts and future plans in this area
Evaluating the semantic web: a task-based approach
The increased availability of online knowledge has led to the design of several algorithms that solve a variety of tasks by harvesting the Semantic Web, i.e. by dynamically selecting and exploring a multitude of online ontologies. Our hypothesis is that the performance of such novel algorithms implicity provides an insight into the quality of the used ontologies and thus opens the way to a task-based evaluation of the Semantic Web. We have investigated this hypothesis by studying the lessons learnt about online ontologies when used to solve three tasks: ontology matching, folksonomy enrichment, and word sense disambiguation. Our analysis leads to a suit of conclusions about the status of the Semantic Web, which highlight a number of strengths and weaknesses of the semantic information available online and complement the findings of other analysis of the Semantic Web landscape
Leveraging Semantic Web Service Descriptions for Validation by Automated Functional Testing
Recent years have seen the utilisation of Semantic Web Service descriptions for automating a wide range of service-related activities, with a primary focus on service discovery, composition, execution and mediation. An important area which so far has received less attention is service validation, whereby advertised services are proven to conform to required behavioural specifications. This paper proposes a method for validation of service-oriented systems through automated functional testing. The method leverages ontology-based and rule-based descriptions of service inputs, outputs, preconditions and effects (IOPE) for constructing a stateful EFSM specification. The specification is subsequently utilised for functional testing and validation using the proven Stream X-machine (SXM) testing methodology. Complete functional test sets are generated automatically at an abstract level and are then applied to concrete Web services, using test drivers created from the Web service descriptions. The testing method comes with completeness guarantees and provides a strong method for validating the behaviour of Web services
Semantic Modeling of Analytic-based Relationships with Direct Qualification
Successfully modeling state and analytics-based semantic relationships of
documents enhances representation, importance, relevancy, provenience, and
priority of the document. These attributes are the core elements that form the
machine-based knowledge representation for documents. However, modeling
document relationships that can change over time can be inelegant, limited,
complex or overly burdensome for semantic technologies. In this paper, we
present Direct Qualification (DQ), an approach for modeling any semantically
referenced document, concept, or named graph with results from associated
applied analytics. The proposed approach supplements the traditional
subject-object relationships by providing a third leg to the relationship; the
qualification of how and why the relationship exists. To illustrate, we show a
prototype of an event-based system with a realistic use case for applying DQ to
relevancy analytics of PageRank and Hyperlink-Induced Topic Search (HITS).Comment: Proceedings of the 2015 IEEE 9th International Conference on Semantic
Computing (IEEE ICSC 2015
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A short survey of discourse representation models
With the advancement of technology and the wide adoption of ontologies as knowledge representation formats, in the last decade, a handful of models were proposed for the externalization of the rhetoric and argumentation captured within scientific publications. Conceptually, most of these models share a similar representation form of the scientific publication, i.e. as a series of interconnected elementary knowledge items. The main differences are given by the terminology used, the types of rhetorical and/or argumentation relations connecting the knowledge items and the foundational theories supporting these relations. This paper analyzes the state of the art and provides a concise comparative overview of the five most prominent discourse representation models, with the goal of sketching an unified model for discourse representation
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