Innovation  &  Technology in Semiotics

Cases of application

Web information acquisition You wish to gather from the web up-to-date information about product, companies and business. The unstructured data crawled from the website need structuring into the database for further processing.
Business intelligence You have extensive collection of data and contents. In order to tap the resources further for business intelligence analysis, you need look into their free text parts of databases to uncover new parametres and their correlations.
Refined search & retrieval In addition to key words search and data classification, you wish to leverage the free text data or metadata to make information delivery more relevant to the query. A job portal can match candidates and job offers by competences described as work experience and job profiles, to complement their current key word search and job category matching. Context-sensitive search & retrieval Your contents portal provides for searching by key word and browsing by categories. As the contents base enriches itself and user needs vary, the effective contents delivery depends on the dynamic indexing and matching, sensitive to the context of the specific user and needs.
Interoperability You have backend access to data and contents from multiple origins, in multiple languages, or across multiple subject domains. You need to treat them dynamically on the same business logics in your information system, regardless of their multiplicity.

Recommendation Your system makes recommendations to the user on products, services, contents or activities. More intelligent recommendation lies with a better knowledge of the user and the recommended. Your recommendation engine needs to source and explore additional parameters and their correlations.

Illustrations of application

Intelartes WISED Platform

The WISED platform is a web information collection and extraction platform for intelligence analysis. WISED stands for Web Information Search, Extraction and Delivery. The underlying technology is an assembly of the Web Utilities, Language Engine and Text Engine. It features a functionality of meta-search over different search engines, web crawling, text transformation, text information extraction and reports of extractions. It is a process to transform unstructured into structured data to enable further information analysis from structured data base.


Competence-based Employment Advisor - Try Online Demo

The Competence-based Employment Advisor is a demo of how the Intelartes Semantics Engine compares the similarity and gap of the competence profiles between job offers and candidates. The demo features basic tasks of a recruitment agent.

  • Browse the competence profile of job candidates
  • Browse the competence requirements of job offers
  • Rank candidates by competences, with reference to a job offer
  • Rank job offer by competences, with reference to a job request

The Semantic Engine is driven by the Intelartes ABAS Ontology and the competence model built from the competence descriptions in ROME 2.0 from ANEP.

Competence-based Employment Advisor

It ranks the search results in terms of networks of semantic entities and relations underlying competence narratives in ROME. It visualizes competence profiles in graph for comparison.

comparison graph

The figure above shows competence profiles of nine candidates in different colours. The x axis shows the extent of a competence. The y axis shows 7 competence statements with which the candidates are compared. The similarity and difference of competence profiles are computed in terms of semantic networks.

Text analytics for business process modeling

The comparison of business process models is important in process integration, re-engineering, and quality assurance of the newly created models. The Text Engine can store the narrative descriptions, create their content profiles and compare their similarity for further improvement.

business process modeling