Semantic Representation of Provenance in Wikipedia

نویسندگان

  • Fabrizio Orlandi
  • Pierre-Antoine Champin
  • Alexandre Passant
چکیده

Wikis are often considered as being a wide source of information. However, identifying provenance information about their content is crucial, whether it is for computing trust in public wiki pages or to identify experts in corporate wikis. In this paper, we address this issue by providing a lightweight ontology for provenance management in wikis, based on the W7 model. Furthermore, we showcase the use of our model in a framework that computes provenance information in Wikipedia, also using DBpedia to compute provenance and contribution information per category, and not only per page.

برای دانلود 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

Named Entity Recognition in Persian Text using Deep Learning

Named entities recognition is a fundamental task in the field of natural language processing. It is also known as a subset of information extraction. The process of recognizing named entities aims at finding proper nouns in the text and classifying them into predetermined classes such as names of people, organizations, and places. In this paper, we propose a named entity recognizer which benefi...

متن کامل

Coloring RDF Triples to Capture Provenance

Recently, the W3C Linking Open Data effort has boosted the publication and inter-linkage of large amounts of RDF datasets on the Semantic Web. Various ontologies and knowledge bases with millions of RDF triples from Wikipedia and other sources, mostly in e-science, have been created and are publicly available. Recording provenance information of RDF triples aggregated from different heterogeneo...

متن کامل

Advertising Keyword Suggestion Using Relevance-Based Language Models from Wikipedia Rich Articles

When emerging technologies such as Search Engine Marketing (SEM) face tasks that require human level intelligence, it is inevitable to use the knowledge repositories to endow the machine with the breadth of knowledge available to humans. Keyword suggestion for search engine advertising is an important problem for sponsored search and SEM that requires a goldmine repository of knowledge. A recen...

متن کامل

A Joint Semantic Vector Representation Model for Text Clustering and Classification

Text clustering and classification are two main tasks of text mining. Feature selection plays the key role in the quality of the clustering and classification results. Although word-based features such as term frequency-inverse document frequency (TF-IDF) vectors have been widely used in different applications, their shortcoming in capturing semantic concepts of text motivated researches to use...

متن کامل

Sense-aware Semantic Analysis: A Multi-prototype Word Representation Model using Wikipedia

Human languages are naturally ambiguous, which makes it difficult to automatically understand the semantics of text. Most vector space models (VSM) treat all occurrences of a word as the same and build a single vector to represent the meaning of a word, which fails to capture any ambiguity. We present sense-aware semantic analysis (SaSA), a multi-prototype VSM for word representation based on W...

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

برای دانلود 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

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2010