Semantic Web Machine Reading with FRED

نویسندگان

  • Aldo Gangemi
  • Valentina Presutti
  • Diego Reforgiato Recupero
  • Andrea Giovanni Nuzzolese
  • Francesco Draicchio
  • Misael Mongiovì
چکیده

A machine reader is a tool able to transform natural language text to formal structured knowledge so as the latter can be interpreted by machines, according to a shared semantics. FRED is a machine reader for the semantic web: its output is a RDF/OWL graph, whose design is based on frame semantics. Nevertheless, FRED’s graph are domain and task independent making the tool suitable to be used as a semantic middleware for domainor taskspecific applications. To serve this purpose, it is available both as REST service and as Python library. This paper provides details about FRED’s capabilities, design issues, implementation and evaluation.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A Machine Reader for the Semantic Web

FRED is a machine reading tool for converting text into internally well-connected and quality linked-data-ready ontologies in webservice-acceptable time. It implements a novel approach for ontology design from natural language sentences, combining Discourse Representation Theory (DRT), linguistic frame semantics, and Ontology Design Patterns (ODP). The current version of the tool includes Earma...

متن کامل

SHELDON: Semantic Holistic FramEwork for LinkeD ONtology Data

SHELDON is the first true hybridization of NLP machine reading and Semantic Web. It is a framework that builds upon a machine reader for extracting RDF graphs from text so that the output is compliant to Semantic Web and Linked Data patterns. It extends the current human-readable web by using Semantic Web practices and technologies in a machine-processable form. Given a sentence in any language...

متن کامل

Presenting a method for extracting structured domain-dependent information from Farsi Web pages

Extracting structured information about entities from web texts is an important task in web mining, natural language processing, and information extraction. Information extraction is useful in many applications including search engines, question-answering systems, recommender systems, machine translation, etc. An information extraction system aims to identify the entities from the text and extr...

متن کامل

WOOGLE meets Semantic Web Fred

A major merit of the Web Service Modeling Ontology WSMO is the wellstructured and unambiguous definition of the description elements for its components. This allows developing concise, generic inference mechanisms for basic Semantic Web Service technologies like Web Service Discovery as the detection of suitable Wed Services for solving a Goal. This paper introduces WOOGLE, a basic but powerful...

متن کامل

Extracting knowledge from text using SHELDON, a Semantic Holistic framEwork for LinkeD ONtology data

SHELDON is the first true hybridization of NLP machine reading and the Semantic Web. It extracts RDF data from text using a machine reader: the extracted RDF graphs are compliant to Semantic Web and Linked Data. It goes further and applies Semantic Web practices and technologies to extend the current human-readable web. The input is represented by a sentence in any language. SHELDON includes di...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:
  • Semantic Web

دوره 8  شماره 

صفحات  -

تاریخ انتشار 2017