PERICLES Deliverable 4.3:Content Semantics and Use Context Analysis Techniques

The current deliverable summarises the work conducted within task T4.3 of WP4, focusing on the extraction and the subsequent analysis of semantic information from digital content, which is imperative for its preservability. More specifically, the deliverable defines content semantic information from a visual and textual perspective, explains how this information can be exploited in long-term digital preservation and proposes novel approaches for extracting this information in a scalable manner. Additionally, the deliverable discusses novel techniques for retrieving and analysing the context of use of digital objects. Although this topic has not been extensively studied by existing literature, we believe use context is vital in augmenting the semantic information and maintaining the usability and preservability of the digital objects, as well as their ability to be accurately interpreted as initially intended.PERICLE

Improving text classification by a sense spectrum approach to term expansion

CoNLL 2009 - Proceedings of the Thirteenth Conference on Computational Natural Language Learning183-19

A Potential Surface Underlying Meaning?

Machine learning algorithms utilizing gradient descent to identify concepts or more general learnables hint at a so-far ignored possibility, namely that local and global minima represent any vocabulary as a landscape against which evaluation of the results can take place. A simple example to illustrate this idea would be a potential surface underlying gravitation. However, to construct a gravitation-based representation of, e.g., word meaning, only the distance between localized items is a given in the vector space, whereas the equivalents of mass or charge are unknown in semantics. Clearly, the working hypothesis that physical fields could be a useful metaphor to study word and sentence meaning is an option but our current representations are incomplete in this respect.For a starter, consider that an RBF kernel has the capacity to generate a potential surface and hence create the impression of gravity, providing one with distance-based decay of interaction strength, plus a scalar scaling factor for the interaction, but of course no term masses. We are working on an experiment design to change that. Therefore, with certain mechanisms in neural networks that could host such quasi-physical fields, a novel approach to the modeling of mind content seems plausible, subject to scrutiny.Work in progress in another direction of the same idea indicates that by using certain algorithms, already emerged vs. still emerging content is clearly distinguishable, in line with Aristotle’s Metaphysics. The implications are that a model completed by “term mass” or “term charge” would enable the computation of the specific work equivalent of sentences or documents, and that via replacing semantics by other modalities, vector fields of more general symbolic content could exist as well. Also, the perceived hypersurface generated by the dynamics of language use may be a step toward more advanced models, for example addressing the Hamiltonian of expanding semantic systems, or the relationship between reaction paths in quantum chemistry vs. sentence construction by gradient descent.PERICLE

Landing Propp in Interaction Space: First Steps Toward Scalable Open Domain Narrative Analysis With Predication-based Semantic Indexing

In this paper, we explore the possibility of applying high-dimensionalvector representations of concept-relation-concept triplets, which have been successfullyapplied to model a small set of relationship types in the biomedicaldomain, to the task of modeling folk tales. In doing so, our ultimate aim is todevelop representations of narratives through which their underlying structurecan be compared. The current paper describes our progress toward this aim, withemphasis on addressing the technical challenges involved in moving from therelatively constrained set of relations that have been extracted from biomedicaltext to the much larger set of unnormalized relations that have been extractedfrom the open domain. A toy example using graded vectors demonstrates that ourapproach will be feasible once more material will be added to the test collection

A Potential Surface Underlying Meaning?

Machine learning algorithms utilizing gradient descent to identify concepts or more general learnables hint at a so-far ignored possibility, namely that local and global minima represent any vocabulary as a landscape against which evaluation of the results can take place. A simple example to illustrate this idea would be a potential surface underlying gravitation. However, to construct a gravitation-based representation of, e.g., word meaning, only the distance between localized items is a given in the vector space, whereas the equivalents of mass or charge are unknown in semantics. Clearly, the working hypothesis that physical fields could be a useful metaphor to study word and sentence meaning is an option but our current representations are incomplete in this respect.For a starter, consider that an RBF kernel has the capacity to generate a potential surface and hence create the impression of gravity, providing one with distance-based decay of interaction strength, plus a scalar scaling factor for the interaction, but of course no term masses. We are working on an experiment design to change that. Therefore, with certain mechanisms in neural networks that could host such quasi-physical fields, a novel approach to the modeling of mind content seems plausible, subject to scrutiny.Work in progress in another direction of the same idea indicates that by using certain algorithms, already emerged vs. still emerging content is clearly distinguishable, in line with Aristotle’s Metaphysics. The implications are that a model completed by “term mass” or “term charge” would enable the computation of the specific work equivalent of sentences or documents, and that via replacing semantics by other modalities, vector fields of more general symbolic content could exist as well. Also, the perceived hypersurface generated by the dynamics of language use may be a step toward more advanced models, for example addressing the Hamiltonian of expanding semantic systems, or the relationship between reaction paths in quantum chemistry vs. sentence construction by gradient descent.PERICLE

Conceptual machinery of the mythopoetic mind : Attis, a case study

In search for the right interpretation regarding a body of related content, we screened a small corpus of myths about Attis, a minor deity from the Hellenistic period in Asia Minor to identify the noncommutativity of key concepts used in storytelling. Looking at the protagonist's typical features, our experiment showed incompatibility with regard to his gender and downfall. A crosscheck for entanglement found no violation of a Bell inequality, its best approximation being on the border of the local polytope

On the Semantics of Concept Drift: Towards Formal Definitions of Semantic Change

Semantic change and concept drift are studied in many differentacademic fields. Different domains have different understandings ofwhat a concept and, thus, concept drift is making it harder for researchersto build upon work in other disciplines. In this paper, we aim to addressthis challenge and propose definitions for these phenomena which applyacross fields. We provide formal definitions and illustrate how conceptdrift and related phenomena can be modeled in RDF through the useof context. We explain and support the definitions through an examplefrom historical research and argue that a formal modeling of semanticchange in RDF can help to better interpret data

Distributed-Temperature-Sensing Using Optical Methods: A First Application in the Offshore Area of Campi Flegrei Caldera (Southern Italy) for Volcano Monitoring

A temperature profile 2400 m along the off-shore active caldera of Campi Flegrei (Gulf of Pozzuoli) was obtained by the installation of a permanent fiber-optic monitoring system within the framework of the Innovative Monitoring for Coastal and Marine Environment (MON.I.C.A) project. The system consists of a submerged, reinforced, multi-fiber cable containing six single-mode telecom grade optical fibers that, exploiting the stimulated Brillouin scattering, provide distributed temperature sensing (DTS) with 1 m of spatial resolution. The obtained data show that the offshore caldera, at least along the monitored profile, has many points of heat discharge associated with fluid emission. A loose association between the temperature profile and the main structural features of the offshore caldera was also evidenced by comparing DTS data with a high-resolution reflection seismic survey. This represents an important advancement in the monitoring of this high-risk volcanic area, since temperature variations are among the precursors of magma migration towards the surface and are also crucial data in the study of caldera dynamics. The adopted system can also be applied to many other calderas which are often partially or largely submerged and hence difficult to monitor