Assisted Knowledge Base Generation, Management and Competence Retrieval

Abstract

Despite the presence of many systems for developing and managing structured taxonomies and/or SKOS models for a given domain for which small documents set are accessible, the production and maintenance of these domain knowledge bases is still a very expensive and time consuming process. This paper proposes a solution for assisting expert users in the development and management of knowledge base, including SKOS and ontologies modeling structures and relationships. The proposed solution accelerates the knowledge production by crawling and exploiting di®erent kinds of sources (in multiple languages and with several inconsistencies among them). The proposed tool supports the experts in de¯ning relationships among the most recurrent concepts, reducing the time to SKOS production and allowing assisted production. The validity of the produced knowledge base has been assessed by using SPARQL query interface and a precision and recall model. The results have demonstrated better performance with respect to the state of the art. The solution has been developed for Open Space Innovative Mind project, with the aim of creating a portal to allow industries at posing semantic queries to discover potential competences in a large institution such as the University of Florence, in which several distinct domains are associated with its own departments.

Km4City ontology building vs data harvesting and cleaning for smart-city services

“…The next step will be to identify famous names, points of interest, locality names that can be linked to other data set as DBpedia 2 or GeoNames 3 according to a Linked Open Data model. This process can be performed with a simple NLP algorithm [6], [8].…”

“…Temporal Macroclass Finally, the last macroclass, called Temporal Macroclass, is now only "sketchy" within the ontology, and it is based on the Time ontology [5] as it has been used into OSIM ontology [8]. It requires the integration of the concept of time as it will be of paramount importance to be able to calculate differences between time instants, and the Time ontology comes to help us in this task.…”

Ge(o)Lo(cator): Geographic Information Extraction from Unstructured Text Data and Web Documents

“…Another problem is represented by those place names which are homonymic with names of persons or organizations in different languages: as an example, some of the most common words in the English, such as And, To, Be and By, represent also different places in the world (located in Ireland, Ghana, India and Sweden, respectively) [4]. Many efforts have been recently made in the direction of building smarter and more efficient machine readable models, thanks to the development of Semantic technologies in the form of web ontologies, thesauri, general Knowledge Organization Systems (KOS), taxonomies, inference mechanisms and reasoning engines [26].…”

Modeling performing arts metadata and relationships in content service for institutions

“…Moreover, these names are mentioned into metadata fields defined as free text, and available as free text in the former archive of the CPs. The fields of Table 2, fields identified by column Person Name have to be processed by a natural language processing engine to extract Person Names in all their possible forms and languages with the aim of disambiguating and normalizing them in the system using natural language processing tools [1]. The problem of name entity recognition with synonyms in text is well known and it can be solved with a variety of solutions ranging from simple grammars to machine learning.…”