Matches in ScholarlyData for { ?s ?p ?o. }
- 29 creator masahiro-inoue.
- 29 type InProceedings.
- 29 label "Noise Robust Detection of the Emergence and Spread of Topics on the Web".
- 29 sameAs 29.
- 29 abstract "As the same information appears on many Web pages, we often want to know which page is the first one that discussed it, or how the information has spread on the Web as time passes. In this paper, we develop two methods: a method of detecting the first page that discussed the given information, and a method of generating a graph showing how the number of pages discussing it has changed along the timeline. To extract such information, we need to determine which pages discuss the given topic, and also need to determine when these pages were created. For the former step, we design a metric for estimating inclusion degree between information and a page. For the latter step, we develop a technique of extracting creation timestamps on web pages. Although timestamp extraction is a crucial component in temporal Web analysis, no research has shown how to do it in detail. Both steps are, however, still error-prone. In order to improve noise elimination, we examine not only the properties of each page, but also temporal relationship between pages. If temporal relationship between some candidate page and other pages are unlikely in typical patterns of information spread on the Web, we eliminate the candidate page as a noise. Results of our experiments show that our methods achieve high precision and can be used for practical use.".
- 29 hasAuthorList authorList.
- 29 isPartOf proceedings.
- 29 keyword "information propagation".
- 29 keyword "information provenance".
- 29 keyword "timestamp extraction".
- 29 title "Noise Robust Detection of the Emergence and Spread of Topics on the Web".
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- item-1 hasContent masahiro-inoue.
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- item-2 hasContent keishi-tajima.
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- 30 creator erdal-kuzey.
- 30 creator gerhard-weikum.
- 30 type InProceedings.
- 30 label "Extraction of Temporal Facts and Events from Wikipedia".
- 30 sameAs 30.
- 30 abstract "Recently, large-scale knowledge bases have been constructed by automatically extracting relational facts from text. Unfortunately, most of the current knowledge bases focus on static facts and ignore the temporal dimension. However, the vast majority of facts are evolving with time or are valid only during a particular time period. Thus, time is a significant dimension that should be included in knowledge bases.In this paper, we introduce a complete information extraction framework that harvests temporal facts and events from semi-structured data and free text of Wikipedia articles to create a temporal ontology. First, we extend a temporal data representation model by making it aware of events. Second, we develop an information extraction method which harvests temporal facts and events from Wikipedia infoboxes, categories, lists, and article titles in order to build a temporal knowledge base. Third, we show how the system can use its extracted knowledge for further growing the knowledge base.We demonstrate the effectiveness of our proposed methods through several experiments. We extracted more than one million temporal facts with precision over 90% for extraction from semi-structured data and almost 70% for extraction from text.".
- 30 hasAuthorList authorList.
- 30 isPartOf proceedings.
- 30 keyword "Information Extraction".
- 30 keyword "Ontologies".
- 30 keyword "Temporal Knowledge".
- 30 title "Extraction of Temporal Facts and Events from Wikipedia".
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- item-1 hasContent erdal-kuzey.
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- item-2 hasContent gerhard-weikum.
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- 34 creator alipio-jorge.
- 34 creator celia-nunes.
- 34 creator gael-dias.
- 34 creator ricardo-campos.
- 34 type InProceedings.
- 34 label "Enriching Temporal Query Understanding through Date Identification: How to Tag Implicit Temporal Queries?".
- 34 sameAs 34.
- 34 abstract "Generically, search engines fail to understand the users’ temporal intents, when expressed by means of implicit temporal queries. This causes the retrieval of less relevant information and prevents users to be aware of the possible temporal dimension of the query results. In this paper, we aim to develop a language-independent model that tackles the temporal dimensions of a query and identifies its most relevant time periods. For this purpose, we propose a temporal similarity measure capable of associating relevant date(s) to a given query and filter out irrelevant ones. Our approach is based on the exploitation of temporal information from web content, particularly within the set of k-top retrieved web snippets returned in response to a query. We are particularly focused on extracting year dates, which are a kind of temporal information that often appears in this type of collections. We evaluate our methodology, over a set of real-world text temporal queries, which are clear concepts (i.e. queries which are non-ambiguous in concept and temporal in their purpose). Experiments show that determining the most relevant dates related to any given implicit temporal query can be improved with a new temporal similarity measure compared to baseline methods.".
- 34 hasAuthorList authorList.
- 34 isPartOf proceedings.
- 34 keyword "Dating Implicit Temporal Queries".
- 34 keyword "Temporal Information Retrieval".
- 34 keyword "Temporal Query Understanding".
- 34 title "Enriching Temporal Query Understanding through Date Identification: How to Tag Implicit Temporal Queries?".
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- item-1 hasContent ricardo-campos.
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- item-2 hasContent gael-dias.
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- item-3 hasContent alipio-jorge.
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- item-4 hasContent celia-nunes.
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- 35 creator costas-stefanatos.
- 35 creator margarita-karkali.
- 35 creator michalis-vazirgiannis.
- 35 creator vassilis-plachouras.
- 35 type InProceedings.
- 35 label "Keeping Keywords Fresh: A BM25 Variation for Personalized Keyword Extraction".
- 35 sameAs 35.
- 35 abstract "Keyword extraction from web pages is essential to various text mining tasks including contextual advertising, recom- mendation selection, user proling and personalization. For example, extracted keywords in contextual advertising are used to match advertisements with the web page currently browsed by a user. Most of the keyword extraction methods mainly rely on the content of a single web page, ignoring the browsing history of a user, and hence, potentially leading to the same advertisements or recommendations. In this work we propose a new feature scoring algorithm for web page terms extraction that, assuming a recent brows- ing history per user, takes into account the freshness of key- words in the current page as means of shifting users interests. We propose BM25H, a variant of BM25 scoring function, implemented on the client-side, that takes into account the user browsing history and suggests keywords relevant to the currently browsed page, but also fresh with respect to the user's recent browsing history. In this way, for each web page we obtain a set of keywords, representing the time shifting interests of the user. BM25H avoids repetitions of keywords which may be simply domain specic stop-words, or may re- sult in matching the same ads or similar recommendations. Our experimental results show that BM25H achieves more than 70% in precision at 20 extracted keywords (based on human blind evaluation) and outperforms our baselines (TF and BM25 scoring functions), while it succeeds in keeping extracted keywords fresh compared to recent user history.".
- 35 hasAuthorList authorList.
- 35 isPartOf proceedings.
- 35 keyword "fresh keywords".
- 35 keyword "keyword extraction".
- 35 keyword "web personalization".
- 35 title "Keeping Keywords Fresh: A BM25 Variation for Personalized Keyword Extraction".
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