The system uses the following three techniques to extract entities from the web: Phrase Extra We now want to explore how good similar machine learning classification algorithms perform on extracted en Hobbs [33] described a methodology for building a large knowledge base for a natural language system.
Documents: Advanced Search Include Citations. Authors: Advanced Search Include Citations. Results 1 - 4 of 4. Abstract This paper describes a system for entity extraction from the web. The sys-tem uses three different extraction techniques which are tightly coupled with mech-anisms for retrieving entity rich web pages.
The main contributions of this paper are a new entity retrieval approach, a comparison of Abstract - Cited by 4 1 self - Add to MetaCart Abstract This paper describes a system for entity extraction from the web.
The main contributions of this paper are a new entity retrieval approach, a comparison of different extraction techniques and a more precise entity extraction algorithm.
The presented approach allows to extract domain-independent information from the web, requiring only little human effort. Citation Context Thom, Er Schill. This paper describes a system for entity extraction from the web. Campus of Bologna. Course Timetable from Mar 21, to May 03, This course aims to initiate to methods for interpretation of data and content as knowledge sources. At the end of the new course the students will be able to: master the basics of knowledge representation and reasoning, with application to the Semantic Web ontologies, linked data, knowledge patterns ; be familiar with the state-of-the-art in knowledge representation and extraction technologies; use applications to automatically extract knowledge from text; analyse the knowledge requirements of a customer, and produce a plan to implement them.
The students will master the basics of representation and extraction of knowledge, intended as data suitable to machine querying jointly with automated reasoning and generalised inferences. The course includes the following themes:.
Specific teaching materials, including papers, slides and exercises, will be available on the course site.
Anyways, the following texts can be used as a generic reference for the course:. The course will be given in frontal lectures of 3 hours each, possibly including host lectures.
A tutor will complement the course with hands-on and small projects in order to improve practical skills in realistic settings. Time permitting, a small project will be implemented by groups of students. Informal contests, such as a semantic treasure hunt, may be proposed to students. Students will work in small teams typically 3 members , choosing a realistic domain or problem that can be modelled, investigated, evaluated, and published on the Web. Extended Semantic Web Conference. Authors Authors and affiliations Aldo Gangemi.
Conference paper. This process is experimental and the keywords may be updated as the learning algorithm improves. Download to read the full conference paper text. Androutsopoulos, I. Banko, M. Berry, M. Springer Google Scholar. Bhattacharya, I. Blomqvist, E. In: Bernstein, A. ISWC LNCS, vol. Bos, J. In: Bos, J. Semantics in Text Processing, pp. College Publications Google Scholar. Buckley, C.
Ciaramita, M. Cimiano, P. Coppola, B. In: Aroyo, L. ESWC
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