TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 13: Web Technologies 2 - The Semantic Web

The semantic web represents a current research effort to increase the capability of machines to make sense of content on the web. In this class, Peter Scheir will give a guest lecture on the basic principles underlying the semantic web vision, including RDF, OWL and other standards.

Dynamic Document Generation from Semantic Web Data

This talk will present an overview of the ongoing ERCIM project SMARTDOCS (SeMAntically-cReaTed DOCuments) which aims at automatically generating webpages from RDF data. It will particularly focus on the current issues and the investigated solutions in the different modules of the project, which are related to document planning, natural language generation and multimedia perspectives. The second part of the talk will be dedicated to the KODA annotation system, which is a knowledge-base-agnostic annotator designed to provide the RDF annotations required in the document generation process.

Annotation and characterization of the Plasmodium vivax rhoptry neck protein 4 (PvRON4)

Background: The tight junction (TJ) is one of the most important structures established during merozoite invasion of host cells and a large amount of proteins stored in Toxoplasma and Plasmodium parasites’ apical organelles are involved in forming the TJ. Plasmodium falciparum and Toxoplasma gondii apical membrane antigen 1 (AMA-1) and rhoptry neck proteins (RONs) are the two main TJ components. It has been shown that RON4 plays an essential role during merozoite and sporozoite invasion to target cells. This study has focused on characterizing a novel Plasmodium vivax rhoptry protein, RON4, which is homologous to PfRON4 and PkRON4. Methods: The ron4 gene was re-annotated in the P. vivax genome using various bioinformatics tools and taking PfRON4 and PkRON4 amino acid sequences as templates. Gene synteny, as well as identity and similarity values between open reading frames (ORFs) belonging to the three species were assessed. The gene transcription of pvron4, and the expression and localization of the encoded protein were also determined in the VCG-1 strain by molecular and immunological studies. Nucleotide and amino acid sequences obtained for pvron4 in VCG-1 were compared to those from strains coming from different geographical areas. Results: PvRON4 is a 733 amino acid long protein, which is encoded by three exons, having similar transcription and translation patterns to those reported for its homologue, PfRON4. Sequencing PvRON4 from the VCG-1 strain and comparing it to P. vivax strains from different geographical locations has shown two conserved regions separated by a low complexity variable region, possibly acting as a “smokescreen”. PvRON4 contains a predicted signal sequence, a coiled-coil α-helical motif, two tandem repeats and six conserved cysteines towards the carboxyterminus and is a soluble protein lacking predicted transmembranal domains or a GPI anchor. Indirect immunofluorescence assays have shown that PvRON4 is expressed at the apical end of schizonts and co-localizes at the rhoptry neck with PvRON2.

fMRI delineation of working memory for emotional prosody in the brain: commonalities with the lexico-semantic emotion network

Decoding emotional prosody is crucial for successful social interactions, and continuous monitoring of emotional intent via prosody requires working memory. It has been proposed by Ross and others that emotional prosody cognitions in the right hemisphere are organized in an analogous fashion to propositional language functions in the left hemisphere. This study aimed to test the applicability of this model in the context of prefrontal cortex working memory functions. BOLD response data were therefore collected during performance of two emotional working memory tasks by participants undergoing fMRI. In the prosody task, participants identified the emotion conveyed in pre-recorded sentences, and working memory load was manipulated in the style of an N-back task. In the matched lexico-semantic task, participants identified the emotion conveyed by sentence content. Block-design neuroimaging data were analyzed parametrically with SPM5. At first, working memory for emotional prosody appeared to be right-lateralized in the PFC, however, further analyses revealed that it shared much bilateral prefrontal functional neuroanatomy with working memory for lexico-semantic emotion. Supplementary separate analyses of males and females suggested that these language functions were less bilateral in females, but their inclusion did not alter the direction of laterality. It is concluded that Ross et al.'s model is not applicable to prefrontal cortex working memory functions, that evidence that working memory cannot be subdivided in prefrontal cortex according to material type is increased, and that incidental working memory demands may explain the frontal lobe involvement in emotional prosody comprehension as revealed by neuroimaging studies. (c) 2007 Elsevier Inc. All rights reserved.

Multimodal semantic-associative collateral labelling and indexing of still images

A novel framework for multimodal semantic-associative collateral image labelling, aiming at associating image regions with textual keywords, is described. Both the primary image and collateral textual modalities are exploited in a cooperative and complementary fashion. The collateral content and context based knowledge is used to bias the mapping from the low-level region-based visual primitives to the high-level visual concepts defined in a visual vocabulary. We introduce the notion of collateral context, which is represented as a co-occurrence matrix, of the visual keywords, A collaborative mapping scheme is devised using statistical methods like Gaussian distribution or Euclidean distance together with collateral content and context-driven inference mechanism. Finally, we use Self Organising Maps to examine the classification and retrieval effectiveness of the proposed high-level image feature vector model which is constructed based on the image labelling results.

Semantic-driven context-aware visual information indexing and retrieval: applied in the film post-production domain

A large volume of visual content is inaccessible until effective and efficient indexing and retrieval of such data is achieved. In this paper, we introduce the DREAM system, which is a knowledge-assisted semantic-driven context-aware visual information retrieval system applied in the film post production domain. We mainly focus on the automatic labelling and topic map related aspects of the framework. The use of the context- related collateral knowledge, represented by a novel probabilistic based visual keyword co-occurrence matrix, had been proven effective via the experiments conducted during system evaluation. The automatically generated semantic labels were fed into the Topic Map Engine which can automatically construct ontological networks using Topic Maps technology, which dramatically enhances the indexing and retrieval performance of the system towards an even higher semantic level.

Ontology-based metadata for e-learning content

Nowadays, the popularity of the Web encourages the development of Hypermedia Systems dedicated to e-learning. Nevertheless, most of the available Web teaching systems apply the traditional paper-based learning resources presented as HTML pages making no use of the new capabilities provided by the Web. There is a challenge to develop educative systems that adapt the educative content to the style of learning, context and background of each student. Another research issue is the capacity to interoperate on the Web reusing learning objects. This work presents an approach to address these two issues by using the technologies of the Semantic Web. The approach presented here models the knowledge of the educative content and the learner’s profile with ontologies whose vocabularies are a refinement of those defined on standards situated on the Web as reference points to provide semantics. Ontologies enable the representation of metadata concerning simple learning objects and the rules that define the way that they can feasibly be assembled to configure more complex ones. These complex learning objects could be created dynamically according to the learners’ profile by intelligent agents that use the ontologies as the source of their beliefs. Interoperability issues were addressed by using an application profile of the IEEE LOM- Learning Object Metadata standard.

Sistema multiagente web semântico para gestão de conteúdos educacionais

Pós-graduação em Ciência da Computação - IBILCE

Discrimination of media moments and media intervals: sticker-based watch-and-comment annotation

In this paper we discuss the problem of how to discriminate moments of interest on videos or live broadcast shows. The primary contribution is a system which allows users to personalize their programs with previously created media stickers-pieces of content that may be temporarily attached to the original video. We present the system's architecture and implementation, which offer users operators to transparently annotate videos while watching them. We offered a soccer fan the opportunity to add stickers to the video while watching a live match: the user reported both enjoying and being comfortable using the stickers during the match-relevant results even though the experience was not fully representative.

Searching and retrieving in content-based repositories of formal mathematical knowledge

In this thesis, the author presents a query language for an RDF (Resource Description Framework) database and discusses its applications in the context of the HELM project (the Hypertextual Electronic Library of Mathematics). This language aims at meeting the main requirements coming from the RDF community. in particular it includes: a human readable textual syntax and a machine-processable XML (Extensible Markup Language) syntax both for queries and for query results, a rigorously exposed formal semantics, a graph-oriented RDF data access model capable of exploring an entire RDF graph (including both RDF Models and RDF Schemata), a full set of Boolean operators to compose the query constraints, fully customizable and highly structured query results having a 4-dimensional geometry, some constructions taken from ordinary programming languages that simplify the formulation of complex queries. The HELM project aims at integrating the modern tools for the automation of formal reasoning with the most recent electronic publishing technologies, in order create and maintain a hypertextual, distributed virtual library of formal mathematical knowledge. In the spirit of the Semantic Web, the documents of this library include RDF metadata describing their structure and content in a machine-understandable form. Using the author's query engine, HELM exploits this information to implement some functionalities allowing the interactive and automatic retrieval of documents on the basis of content-aware requests that take into account the mathematical nature of these documents.

Towards an Emergent Semantic Web

In his in uential article about the evolution of the Web, Berners-Lee [1] envisions a Semantic Web in which humans and computers alike are capable of understanding and processing information. This vision is yet to materialize. The main obstacle for the Semantic Web vision is that in today's Web meaning is rooted most often not in formal semantics, but in natural language and, in the sense of semiology, emerges not before interpretation and processing. Yet, an automated form of interpretation and processing can be tackled by precisiating raw natural language. To do that, Web agents extract fuzzy grassroots ontologies through induction from existing Web content. Inductive fuzzy grassroots ontologies thus constitute organically evolved knowledge bases that resemble automated gradual thesauri, which allow precisiating natural language [2]. The Web agents' underlying dynamic, self-organizing, and best-effort induction, enable a sub-syntactical bottom up learning of semiotic associations. Thus, knowledge is induced from the users' natural use of language in mutual Web interactions, and stored in a gradual, thesauri-like lexical-world knowledge database as a top-level ontology, eventually allowing a form of computing with words [3]. Since when computing with words the objects of computation are words, phrases and propositions drawn from natural languages, it proves to be a practical notion to yield emergent semantics for the Semantic Web. In the end, an improved understanding by computers on the one hand should upgrade human- computer interaction on the Web, and, on the other hand allow an initial version of human- intelligence amplification through the Web.

Altered Electrophysiology of Semantic Word Processing in Alzheimer's Disease and Semantic Dementia

With the progressing course of Alzheimer's disease (AD), deficits in declarative memory increasingly restrict the patients' daily activities. Besides the more apparent episodic (biographical) memory impairments, the semantic (factual) memory is also affected by this neurodegenerative disorder. The episodic pathology is well explored; instead the underlying neurophysiological mechanisms of the semantic deficits remain unclear. For a profound understanding of semantic memory processes in general and in AD patients, the present study compares AD patients with healthy controls and Semantic Dementia (SD) patients, a dementia subgroup that shows isolated semantic memory impairments. We investigate the semantic memory retrieval during the recording of an electroencephalogram, while subjects perform a semantic priming task. Precisely, the task demands lexical (word/nonword) decisions on sequentially presented word pairs, consisting of semantically related or unrelated prime-target combinations. Our analysis focuses on group-dependent differences in the amplitude and topography of the event related potentials (ERP) evoked by related vs. unrelated target words. AD patients are expected to differ from healthy controls in semantic retrieval functions. The semantic storage system itself, however, is thought to remain preserved in AD, while SD patients presumably suffer from the actual loss of semantic representations.

Implicit memory for the content but not the speaker of sleep-played messages

We presented 28 sentences uttered by 28 unfamiliar speakers to sleeping participants to investigate whether humans can encode new verbal messages, learn voices of unfamiliar speakers, and form associations between speakers and messages during EEG-defined deep sleep. After waking, participants performed three tests which assessed the unconscious recognition of sleep-played speakers, messages, and speaker-message associations. Recognition performance in all tests was at chance level. However, response latencies revealed implicit memory for sleep-played messages but neither for speakers nor for speaker-message combinations. Only participants with excellent implicit memory for sleep-played messages also displayed implicit memory for speakers but not speaker-message associations. Hence, deep sleep allows for the semantic encoding of novel verbal messages.

Using SCXML to integrate semantic sensor information into context-aware user interfaces

This paper describes a novel architecture to introduce automatic annotation and processing of semantic sensor data within context-aware applications. Based on the well-known state-charts technologies, and represented using W3C SCXML language combined with Semantic Web technologies, our architecture is able to provide enriched higher-level semantic representations of user’s context. This capability to detect and model relevant user situations allows a seamless modeling of the actual interaction situation, which can be integrated during the design of multimodal user interfaces (also based on SCXML) for them to be adequately adapted. Therefore, the final result of this contribution can be described as a flexible context-aware SCXML-based architecture, suitable for both designing a wide range of multimodal context-aware user interfaces, and implementing the automatic enrichment of sensor data, making it available to the entire Semantic Sensor Web