| 1. |
- Maronidis, A., et al.
(författare)
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PERICLES Deliverable 4.3: Content Semantics and Use Context Analysis Techniques
- 2016
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Rapport (refereegranskat)abstract
- 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.
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| 2. |
- Kirilenko, Bogdan M., et al.
(författare)
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Integrating gene annotation with orthology inference at scale
- 2023
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Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 380:6643, s. 368-
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Tidskriftsartikel (refereegranskat)abstract
- Annotating coding genes and inferring orthologs are two classical challenges in genomics and evolutionary biology that have traditionally been approached separately, limiting scalability. We present TOGA (Tool to infer Orthologs from Genome Alignments), a method that integrates structural gene annotation and orthology inference. TOGA implements a different paradigm to infer orthologous loci, improves ortholog detection and annotation of conserved genes compared with state-of-the-art methods, and handles even highly fragmented assemblies. TOGA scales to hundreds of genomes, which we demonstrate by applying it to 488 placental mammal and 501 bird assemblies, creating the largest comparative gene resources so far. Additionally, TOGA detects gene losses, enables selection screens, and automatically provides a superior measure of mammalian genome quality. TOGA is a powerful and scalable method to annotate and compare genes in the genomic era.
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| 3. |
- Kontopoulos, E., et al.
(författare)
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Deliverable 4.5: Context-aware Content Interpretation
- 2016
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Rapport (refereegranskat)abstract
- The current deliverable summarises the work conducted within task T4.5 of WP4, presenting our proposed approaches for contextualised content interpretation, aimed at gaining insightful contextualised views on content semantics. This is achieved through the adoption of appropriate context-aware semantic models developed within the project, and via enriching the semantic descriptions with background knowledge, deriving thus higher level contextualised content interpretations that are closer to human perception and appraisal needs. More specifically, the main contributions of the deliverable are the following: A theoretical framework using physics as a metaphor to develop different models of evolving semantic content. A set of proof-of-concept models for semantic drifts due to field dynamics, introducing two methods to identify quantum-like (QL) patterns in evolving information searching behaviour, and a QL model akin to particle-wave duality for semantic content classification. Integration of two specific tools, Somoclu for drift detection and Ncpol2spda for entanglement detection. An “energetic” hypothesis accounting for contextualized evolving semantic structures over time. A proposed semantic interpretation framework, integrating (a) an ontological inference scheme based on Description Logics (DL), (b) a rule-based reasoning layer built on SPARQL Inference Notation (SPIN), (c) an uncertainty management framework based on non-monotonic logics. A novel scheme for contextualized reasoning on semantic drift, based on LRM dependencies and OWL’s punning mechanism. An implementation of SPIN rules for policy and ecosystem change management, with the adoption of LRM preconditions and impacts. Specific use case scenarios demonstrate the context under development and the efficiency of the approach. Respective open-source implementations and experimental results that validate all the above.All these contributions are tightly interlinked with the other PERICLES work packages: WP2 supplies the use cases and sample datasets for validating our proposed approaches, WP3 provides the models (LRM and Digital Ecosystem models) that form the basis for our semantic representations of content and context, WP5 provides the practical application of the technologies developed to preservation processes, while the tools and algorithms presented in this deliverable can be deployed in combination with test scenarios, which will be part of the WP6 test beds.
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| 4. |
- Kontopoulos, Efstratios, et al.
(författare)
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PERICLES Deliverable 4.4: Modelling Contextualised Semantics
- 2016
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Rapport (refereegranskat)abstract
- The current deliverable summarises the work conducted within task T4.4 of WP4, presenting our proposed models for semantically representing digital content and its respective context – the latter refers to any information coming from the environment of the digital object (DO) that offers a better insight into the object’s status, its interrelationships with other content items and information about the object’s context of use. Within PERICLES, we refer to the content semantics enriched with the contextual perspective as “contextualised semantics”. The deliverable presents two complementary modelling approaches, based respectively on (a) ontologies and logics, and, (b) multivariate statistics.
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| 5. |
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| 6. |
- Wittek, Peter, et al.
(författare)
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Monitoring Term Drift Based on SemanticConsistency in an Evolving Vector Field
- 2015
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Ingår i: Proceedings of IJCNN-15.
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Konferensbidrag (refereegranskat)abstract
- Based on the Aristotelian concept of potentialityvs. actuality allowing for the study of energy and dynamics inlanguage, we propose a field approach to lexical analysis. Fallingback on the distributional hypothesis to statistically model wordmeaning, we used evolving fields as a metaphor to express timedependentchanges in a vector space model by a combinationof random indexing and evolving self-organizing maps (ESOM).To monitor semantic drifts within the observation period, anexperiment was carried out on the term space of a collection of12.8 million Amazon book reviews. For evaluation, the semanticconsistency of ESOM term clusters was compared with theirrespective neighbourhoods in WordNet, and contrasted withdistances among term vectors by random indexing. We found thatat 0.05 level of significance, the terms in the clusters showed a highlevel of semantic consistency. Tracking the drift of distributionalpatterns in the term space across time periods, we found thatconsistency decreased, but not at a statistically significant level.Our method is highly scalable, with interpretations in philosophy.
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