Personal Semantics

International audienceQuantified self, life logging, digital eyeglasses, technology is ad- vancing rapidly to a point where people can gather masses of data about their own persons and their own life. Large-scale models of what people are doing are being built by credit companies, advertising agencies, and national security agencies, using digital traces that people leave behind them. How can individuals exploit their own data for their own benefit? With this mass of personal data, we will need to induce personal semantic dimensions to sift data and find what is meaningful to each individual. In this chapter, we present semantic dimensions, made by experts, and by crowds. We show the type of information that individuals will have access to once lifelogging becomes common, and we will sketch what personal semantic dimensions might look like

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Sharing Semantic Resources

The Semantic Web is an extension of the current Web in which information, so far created for human consumption, becomes machine readable, “enabling computers and people to work in cooperation”. To turn into reality this vision several challenges are still open among which the most important is to share meaning formally represented with ontologies or more generally with semantic resources. This Semantic Web long-term goal has many convergences with the activities in the field of Human Language Technology and in particular in the development of Natural Language Processing applications where there is a great need of multilingual lexical resources. For instance, one of the most important lexical resources, WordNet, is also commonly regarded and used as an ontology. Nowadays, another important phenomenon is represented by the explosion of social collaboration, and Wikipedia, the largest encyclopedia in the world, is object of research as an up to date omni comprehensive semantic resource. The main topic of this thesis is the management and exploitation of semantic resources in a collaborative way, trying to use the already available resources as Wikipedia and Wordnet. This work presents a general environment able to turn into reality the vision of shared and distributed semantic resources and describes a distributed three-layer architecture to enable a rapid prototyping of cooperative applications for developing semantic resources

Information Extraction from Biomedical Texts

V poslední době bylo vynaloženo velké úsilí k tomu, aby byly biomedicínské znalosti, typicky uložené v podobě vědeckých článků, snadněji přístupné a bylo možné je efektivně sdílet. Ve skutečnosti ale nestrukturovaná podstata těchto textů způsobuje velké obtíže při použití technik pro získávání a vyvozování znalostí. Anotování entit nesoucích jistou sémantickou informaci v textu je prvním krokem k vytvoření znalosti analyzovatelné počítačem. V této práci nejdříve studujeme metody pro automatickou extrakci informací z textů přirozeného jazyka. Dále zhodnotíme hlavní výhody a nevýhody současných systémů pro extrakci informací a na základě těchto znalostí se rozhodneme přijmout přístup strojového učení pro automatické získávání exktrakčních vzorů při našich experimentech. Bohužel, techniky strojového učení často vyžadují obrovské množství trénovacích dat, která může být velmi pracné získat. Abychom dokázali čelit tomuto nepříjemnému problému, prozkoumáme koncept tzv. bootstrapping techniky. Nakonec ukážeme, že během našich experimentů metody strojového učení pracovaly dostatečně dobře a dokonce podstatně lépe než základní metody. Navíc v úloze využívající techniky bootstrapping se podařilo významně snížit množství dat potřebných pro trénování extrakčního systému.Recently, there has been much effort in making biomedical knowledge, typically stored in scientific articles, more accessible and interoperable. As a matter of fact, the unstructured nature of such texts makes it difficult to apply  knowledge discovery and inference techniques. Annotating information units with semantic information in these texts is the first step to make the knowledge machine-analyzable.  In this work, we first study methods for automatic information extraction from natural language text. Then we discuss the main benefits and disadvantages of the state-of-art information extraction systems and, as a result of this, we adopt a machine learning approach to automatically learn extraction patterns in our experiments. Unfortunately, machine learning techniques often require a huge amount of training data, which can be sometimes laborious to gather. In order to face up to this tedious problem, we investigate the concept of weakly supervised or bootstrapping techniques. Finally, we show in our experiments that our machine learning methods performed reasonably well and significantly better than the baseline. Moreover, in the weakly supervised learning task we were able to substantially bring down the amount of labeled data needed for training of the extraction system.