Ali Khalili's blog

conTEXT: Exploiting Linked Data for Content Analysis

conTEXT is a platform for lightweight text analytics. It allows to semantically analyze text corpora (such as blogs, RSS/Atom feeds, Facebook, G+, Twitter or SlideWiki.org decks) and provides novel ways for browsing and visualizing the results. Furthermore, conTEXT allows researchers to provide a semantic overview of their written text in terms of the concepts and their relations in the text.

conTEXT workflow

The process of text analytics in conTEXT starts by collecting information from the web. conTEXT utilizes standard information access methods and protocols such as RSS/ATOM feeds, SPARQL endpoints and REST APIs as well as customized crawlers for WordPress and Blogger to build a corpus of information relevant for a certain user. The assembled text corpus is then processed by Natural Language Processing (NLP) services (currently FOX and DBpedia-Spotlight) which link unstructured information sources to the Linked Open Data cloud through DBpedia. The processed corpus is then further enriched by de-referencing the  DBpedia URIs as well as  matching with pre-defined natural-language patterns for DBpedia predicates (BOA patterns). The processed data can also be joined with other existing corpora in a text analytics mashup. The creation of analytics mashups requires dealing with the heterogeneity of different corpora as well as the heterogeneity of different NLP services utilized for annotation. conTEXT employs NIF (NLP Interchange Format) to deal with this heterogeneity. The processed, enriched and possibly mixed results are presented to users using different views for exploration and visualization of the data. Additionally, conTEXT provides an annotation refinement user interface based on the RDFa Content Editor (RDFaCE) to enable users to revise the annotated results. User-refined annotations are sent back to the NLP services as feedback for the purpose of learning in the system.

For more information on conTEXT visit:

Ali Khalili

FORCE11 Member since March 5, 2013
  • Organization/Institution: AKSW, University of Leipzig

Biography

The Research Group Agile Knowledge Engineering and Semantic Web (AKSW) is hosted by the Chair of Business Information Systems (BIS) of the Institute of Computer Science (IfI) / University of Leipzig as well as the Institute for Applied Informatics (InfAI).

AKSW 's goals are:

  • Development of methods, tools and applications for adaptive Knowledge Engineering in the context of the Semantic Web
  • Research of underlying Semantic Web technologies and development of fundamental Semantic Web tools and applications
  • Maturation of strategies for fruitfully combining the Social Web paradigms with semantic knowledge representation techniques

AKSW is committed to the free software, open source, open access and open knowledge movements.

Ali Khalili is a researcher working on IT-based tools and approaches for facilitating knowledge sharing and innovation. I have worked as a Web developer for many years and have been an expert in Web Mashup development. My current research focuses on Social Semantic Web. I am working on Adaptive User Interfaces which will decrease the complexity of backend technologies for the promotion of Semantic Web . I am also working on Crowdsourcing approaches for elicitation, sharing and managing of knowledge. Fore more information please refer to my personal page at http://ali1k.com.