Result presentation Search Interface Input and output functionality

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Result presentation

Result presentation

Search Interface • Input and output functionality – helping the user to formulate complex

Search Interface • Input and output functionality – helping the user to formulate complex queries – presenting the results in an intelligent manner • Semantic Search brings improvements in – Query formulation – Snippet generation – Adaptive and interactive presentation • Presentation adapts to the kind of query and results presented • Object results can be actionable, e. g. buy this product – Aggregated search • Grouping similar items, summarizing results in various ways • Filtering (facets), possibly across different dimensions – Task completion • Help the user to fulfill the task by placing the query in a task context

Query interpretation • “Snap-to-grid”: find the most likely interpretation of the query given the

Query interpretation • “Snap-to-grid”: find the most likely interpretation of the query given the ontology or a summary of the data – See Query Processing • Display the system’s interpretation of the user query – Offer one or more interpretations, possibly while the user is typing

Example: Freebase suggest

Example: Freebase suggest

Example: True. Knowledge Q: “How many people live in Shanghai? ” I: What is

Example: True. Knowledge Q: “How many people live in Shanghai? ” I: What is the population of Shanghai (Shanghainese: Zånhae), the metropolis in eastern China and a direct-controlled municipality of the People's Republic of China? A: The population of Shanghai on November 7 th 2010 is approximately 19, 300, 389. (Extrapolated from a population of 18, 884, 600 in 2008 and a population of 19, 210, 000 on June 6 th 2010. )

Snippet generation using metadata • Yahoo displays enriched search results for pages that contain

Snippet generation using metadata • Yahoo displays enriched search results for pages that contain microformat or RDFa markup using recognized ontologies – Displaying data, images, video – Example: Good. Relations for products – Enhanced results also appear for sites from which we extract information ourselves • Also used for generating facets that can be used to restrict search results by object type – Example: “Shopping sites” facet for products • Documentation and validator for developers – http: //developer. search. yahoo. com • Formerly: Search. Monkey allowed developers to customize the result presentation and create new ones for any object type

Example: Yahoo! Enhanced Results Enhanced result with deep links, rating, address.

Example: Yahoo! Enhanced Results Enhanced result with deep links, rating, address.

Automated snippet summarization • Generate search result snippets given a query and a search

Automated snippet summarization • Generate search result snippets given a query and a search result • Penin et al. Snippet Generation for Semantic Web Search Engines, ASWC 2010 – Search results are ontologies

Example: Facets in Yahoo! Search Click to restrict results to shopping sites

Example: Facets in Yahoo! Search Click to restrict results to shopping sites

Example: Yahoo! Vertical Intent Search Related actors and movies

Example: Yahoo! Vertical Intent Search Related actors and movies

Adaptive presentation: semantic bookmarking • Extract objects from pages tagged/bookmarked by a user •

Adaptive presentation: semantic bookmarking • Extract objects from pages tagged/bookmarked by a user • Visualize the extracted objects – Tabular display – Sorting on attributes – Map • Tracking changes in data – Alert me when the price drops below… • Prototype: house search application – Delicious profiles – Extracting housing data from popular Spanish real-estate sites

Adaptive presentation: semantic bookmarking

Adaptive presentation: semantic bookmarking

Interactive presentation: Time Explorer • Deliverable of the Living. Knowledge European Project – Not

Interactive presentation: Time Explorer • Deliverable of the Living. Knowledge European Project – Not a Yahoo product – http: //fbmya 01. barcelonamedia. org: 8080/future/ • Won the HCIR 2010 challenge • Tool for understanding current news stories – what are the events that led to a particular situation? – what are the important entities for a given topic? (people, places, dates, etc. ) – what entities are important at a given time? How do their relationships change? – what are the predictions made of a given topic?

Interactive presentation: Time Explorer • Technology – Named Entity Recognition (persons, organizations) – Temporal

Interactive presentation: Time Explorer • Technology – Named Entity Recognition (persons, organizations) – Temporal expression mining – Inverted (sentence and document) index – Forward index (archive) for retrieving relevant entities – Ranking of both documents and relevant entities • Display – Two synchronized timelines showing relevant documents and the volume of documents – Entity relationships – Sentiments (future work)

Example: Time Explorer

Example: Time Explorer