Population of a Knowledge Base for News Metadata from Unstructured Text and Web Data

International audienceWe present a practical use case of knowl- edge base (KB) population at the French news agency AFP. The target KB instances are en- tities relevant for news production and con- tent enrichment. In order to acquire uniquely identified entities over news wires, i.e. tex- tual data, and integrate the resulting KB in the Linked Data framework, a series of data mod- els need to be aligned: Web data resources are harvested for creating a wide coverage entity database, which is in turn used to link entities to their mentions in French news wires. Fi- nally, the extracted entities are selected for in- stantiation in the target KB. We describe our methodology along with the resources created and used for the target KB population

Social media analytics: a survey of techniques, tools and platforms

This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing

Automatic extraction of knowledge from web documents

A large amount of digital information available is written as text documents in the form of web pages, reports, papers, emails, etc. Extracting the knowledge of interest from such documents from multiple sources in a timely fashion is therefore crucial. This paper provides an update on the Artequakt system which uses natural language tools to automatically extract knowledge about artists from multiple documents based on a predefined ontology. The ontology represents the type and form of knowledge to extract. This knowledge is then used to generate tailored biographies. The information extraction process of Artequakt is detailed and evaluated in this paper

Using background knowledge for ontology evolution

One of the current bottlenecks for automating ontology evolution is resolving the right links between newly arising information and the existing knowledge in the ontology. Most of existing approaches mainly rely on the user when it comes to capturing and representing new knowledge. Our ontology evolution framework intends to reduce or even eliminate user input through the use of background knowledge. In this paper, we show how various sources of background knowledge could be exploited for relation discovery. We perform a relation discovery experiment focusing on the use of WordNet and Semantic Web ontologies as sources of background knowledge. We back our experiment with a thorough analysis that highlights various issues on how to improve and validate relation discovery in the future, which will directly improve the task of automatically performing ontology changes during evolution

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Geospatial database generation from digital newspapers: use case for risk and disaster domains.

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies.The generation of geospatial databases is expensive in terms of time and money. Many geospatial users still lack spatial data. Geographic Information Extraction and Retrieval systems can alleviate this problem. This work proposes a method to populate spatial databases automatically from the Web. It applies the approach to the risk and disaster domain taking digital newspapers as a data source. News stories on digital newspapers contain rich thematic information that can be attached to places. The use case of automating spatial database generation is applied to Mexico using placenames. In Mexico, small and medium disasters occur most years. The facts about these are frequently mentioned in newspapers but rarely stored as records in national databases. Therefore, it is difficult to estimate human and material losses of those events. This work present two ways to extract information from digital news using natural languages techniques for distilling the text, and the national gazetteer codes to achieve placename-attribute disambiguation. Two outputs are presented; a general one that exposes highly relevant news, and another that attaches attributes of interest to placenames. The later achieved a 75% rate of thematic relevance under qualitative analysis

Constructing a Personal Knowledge Graph from Disparate Data Sources

This thesis revolves around the idea of a Personal Knowledge Graph as a uniform coherent structure of personal data collected from multiple disparate sources: A knowledge base consisting of entities such as persons, events, locations and companies interlinked with semantically meaningful relationships in a graph structure where the user is at its center. The personal knowledge graph is intended to be a valuable resource for a digital personal assistant, expanding its capabilities to answer questions and perform tasks that require personal knowledge about the user. We explored techniques within Knowledge Representation, Knowledge Extraction/ Information Extraction and Information Management for the purpose of constructing such a graph. We show the practical advantages of using Knowledge Graphs for personal information management, utilizing the structure for extracting and inferring answers and for handling resources like documents, emails and calendar entries. We have proposed a framework for aggregating user data and shown how existing ontologies can be used to model personal knowledge. We have shown that a personal knowledge graph based on the user's personal resources is a viable concept, however we were not able to enrich our personal knowledge graph with knowledge extracted from unstructured private sources. This was mainly due to sparsity of relevant information, the informal nature and the lack of context in personal correspondence