Demo: HistoryViz - Visualizing Events and Relations Extracted from Wikipedia
نویسندگان
چکیده
HistoryViz provides a new perspective on a certain kind of textual data, in particular the data available in the Wikipedia, where different entities are described and put in historical perspective. Instead of browsing through pages each describing a certain topic, we can look at the relations between entities and events connected with the selected entities. The presented solution implemented in HistoryViz provides user with a graphical interface allowing viewing events concerning the selected person on a timeline and viewing relations to other entities as a graph that can be dynamically expanded.
منابع مشابه
QAKiS: an Open Domain QA System based on Relational Patterns
We present QAKiS, a system for open domain Question Answering over linked data. It addresses the problem of question interpretation as a relation-based match, where fragments of the question are matched to binary relations of the triple store, using relational textual patterns automatically collected. For the demo, the relational patterns are automatically extracted from Wikipedia, while DBpedi...
متن کاملWikiNet: A Very Large Scale Multi-Lingual Concept Network
This paper describes a multi-lingual concept network obtained automatically by mining for concepts and relations and exploiting a variety of sources of knowledge from Wikipedia. Concepts and their lexicalizations are extracted from Wikipedia pages. Relations are extracted from the category and page network, infoboxes and the body of the articles. The network consists of a central, language inde...
متن کاملAdvertising 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 کاملHypernym Discovery Based on Distributional Similarity and Hierarchical Structures
This paper presents a new method of developing a large-scale hyponymy relation database by combining Wikipedia and other Web documents. We attach new words to the hyponymy database extracted from Wikipedia by using distributional similarity calculated from documents on the Web. For a given target word, our algorithm first finds k similar words from the Wikipedia database. Then, the hypernyms of...
متن کاملThe QuALiM Question Answering Demo: Supplementing Answers with Paragraphs drawn from Wikipedia
This paper describes the online demo of the QuALiM Question Answering system. While the system actually gets answers from the web by querying major search engines, during presentation answers are supplemented with relevant passages from Wikipedia. We believe that this additional information improves a user’s search experience.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2009