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Semantic Search Engines

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Title: Semantic Search Engines


1
Semantic Search Engines On the Way to Web 3.0
  • ????? ????? ???????
  • Web ???? ?-3.0

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??-???? ariel_at_cs.biu.ac.il
2
Contents
  • Web 3.0 Semantic Search
  • General Search
  • "Natural Language" Search
  • Vertical Search
  • "Social Networking" Search
  • Personalized Search

3
What is Web 2.0?!
, Open Gardens blog, Ajit Jaokar http//opengarden
sblog.futuretext.com/archives/2005/12/mobile_Web_2
0_w.html
4
The good, the bad and the
5
Web 1.0, Web 2.0, Web 3.0, Web X.0
6
Semantic Search
  • Syntactic search can match the query against
  • index of the textual content of the resources
  • URIs (URLs, URNs) in the system
  • literals in the RDF metadata
  • or a combination of these, possibly using
  • Exact, prefix or substring match, stemming,
    minimal edit distance
  • Semantic search in addition to syntactic
    search, can use
  • index of the meaning of sentences in each
    resource
  • semantic information and analysis
  • the graph structure of RDF metadata
  • or a combination of these, possibly using
  • query expansion, classification/categorization,
    tagging, graph traversal, microformats, RDF OWL
    inferencing and reasoning

7
Can Semantic SEs answer this -?)
8
Types/Examples of Semantic SEs
  • General Search
  • MetaWeb Freebase, Yahoo! Microsearch,
  • "Natural Language" Search
  • Powerset, Hakia, AskMeNow AskWiki,
  • Vertical Search
  • Kango, AdaptiveBlue, ReportLinker,
  • "Social Networking" Search
  • SemantiNet, Delver, Google Social Graph API,
  • Personalized Search
  • Twine, MavinIT PSS,

9
Contents
  • Web 3.0 Semantic Search
  • General Search
  • "Natural Language" Search
  • Vertical Search
  • "Social Networking" Search
  • Personalized Search

10
MetaWeb Technologies - Freebase
  • Based in San Francisco, MetaWeb Technologies was
    spun out of Applied Minds in July 2005.
  • Goal build a better infrastructure for the Web
    application developers and publishers.

11
Freebase Rational
  • Open, shared database of the worlds knowledge
    that collects data from the Web to build a
    massive, collaboratively-edited database of
    cross-linked data.
  • It is built by the community, for the community.
  • Free for anyone to query, contribute to, build
    applications on top of, or integrate into their
    Web sites.
  • Focus is on organizing and managing complex data
    structures by use of Semantic Web technologies.
  • Enables extraction of ordered knowledge out of
    the information chaos that is the current Web.

12
Freebase
13
Freebase Repository
  • Covers millions of topics in hundreds of
    categories.
  • Draws from large open repositories like
    Wikipedia, MusicBrainz, and the SEC archives.
  • Contains structured information on many popular
    topics, like movies, music, people and locations
    all reconciled and freely available via an open
    API.
  • Freebase information is supplemented by the
    efforts of a passionate global community of
    users, who are working together to add structured
    information on everything relevant.

14
Domains and Types
15
Google Company
16
Freebase Help Center
17
Freebase Semantics
  • Freebase spans domains, but requires that a
    particular topic exist only once, even if it
    might normally be found in multiple databases.
  • For example, Arnold Schwarzenegger would appear
    in a movie database as an actor, a political
    database as a governor and a bodybuilder
    database as a Mr. Universe.
  • In Freebase, there is only one topic for Arnold
    Schwarzenegger, with all three facets of his
    public persona brought together.
  • The unified topic acts as an information hub,
    making it easy to find and contribute
    information about him.

18
Arnold Schwarzenegger (1)
19
Arnold Schwarzenegger (2)
20
Freebase Dynamics
  • If the user is a developer, or just mildly
    technical, Freebase offers tools that make it
    easy to query and integrate the data into Web
    applications, blogs, wikis, user pages or
    anything else that would benefit from an
    injection of structured information. 
  • In addition to reconciling many facets of one
    topic, the underlying structure of Freebase lets
    the user run more complex queries.
  • For example, if Freebase is asked for films
    starring Jennifer Connelly and actors who have
    appeared in Steven Spielberg movies, a list of 8
    movies is given.

21
Films starring Jennifer Connelly
22
Freebase vs. Wikipedia
  • The difference lies in the way they store
    information.
  • Wikipedia arranges information in the form of
    articles.
  • Freebase lists facts and statistics. Its list
    form is good not only for people who like to
    glance at facts, but also for people who want to
    use the data to build other Web sites and
    software. (Information in an article form cant
    be reused in the same way.)
  • Topics covered by Freebase include subjects that
    are too obscure for Wikipedia, which strives for
    notability appropriate to an encyclopedia.  

23
Contents
  • Web 3.0 Semantic Search
  • General Search
  • "Natural Language" Search
  • Vertical Search
  • "Social Networking" Search
  • Personalized Search

24
Powerset
  • Powerset is a Silicon Valley company.
  • Goal build a transformative consumer search
    engine based on Natural Language Processing
    (NLP).

25
Powerset Rational
  • Unlike conventional search engines that use
    keywords, Powerset reads and understands every
    sentence on a Webpage and allows asking questions
    in plain English.
  • Unique innovations in search are rooted in
    breakthrough technologies that take advantage of
    the structure and nuances of natural language.
  • Using these advanced techniques, Powerset is
    building a large-scale search engine that breaks
    the confines of keyword search.
  • By making search more natural and intuitive,
    Powerset is fundamentally changing how we search
    the Web, by delivering higher quality results.

26
Who proved Fermats last theorem?
27
What did Steve Jobs say about the iPod?
28
What did Bush say about Gore?
29
Powerlabs
  • Powerlabs is a community where users can
  • interact with demonstrations of Powersets
    technology before search engine launches in 2008
  • give feedback to help improve the "Natural
    Language" indexing
  • suggest ideas for the ideal search engine.
  • Utilizes the participation of users on such a
    scale and at such an early stage of development,
    as a recognition of the potential of crowds
    wisdom to guide Powerset.

30
Powerlabs Sign In
31
Wiki Search Sneak Peek
  • Access to first open search box covering
    Wikipedia.
  • Powerset uses linguistic analyses of both the
    query and Wikipedia to find the best matches.
  • The Miniviewer allows to view highlighted matches
    in the context of a Wikipedia article without
    ever having to leave the results page.
  • By incorporating semantic information from
    Powersets indexing process into republished Wiki
    pages, internal page search enables a whole new
    kind of search semantic-search-within-the-page.

32
Explore Wikipedia
33
Google acquire something
34
Google acquire company
35
Search Wikipedia
36
Companies acquired in 2001
37
Powerset PowerMouse
  • PowerMouse is an application that provides a view
    into Powersets technology, letting users examine
    how structured information is extracted from open
    text.
  • It is not intended as a search application per
    se, but allows to search for and navigate through
    facts encoded in Powersets Wikipedia index.
  • It allows to see in dramatic fashion how
    compactly large amounts of data can be organized
    and displayed based on a few semantic
    relationships.

38
PowerMouse Examples
39
Google acquire something
40
something eats carrot
41
person won nobel
42
Contents
  • Web 3.0 Semantic Search
  • General Search
  • "Natural Language" Search
  • Vertical Search
  • "Social Networking" Search
  • Personalized Search

43
Kango
  • Vertical semantic search engine for personalized
    travel information.
  • Goal first step to deciding where to go, where
    to stay or what to do finds the trip that is
    right for you.

44
Kango Rational
  • Kango indexes the collective wisdom on travel
    from the entire Web.
  • Recommendations are based on a gestalt of voices
    heard in over 20 million reviews, ratings,
    blogs, journals, and articles collected from over
    a thousand sources such as Web sites, books and
    magazines.
  • Organizes and presents the most relevant opinions
    and product details in a "federated" search
    display based on whats known about travel
    preferences.

45
Kango Repository
  • Kango has scoured the Web to collect all kinds of
    places to go, things to do and places to stay.
  • It then analyzed and organized millions of
    travelers' opinions to enable search based on
    exact travel requirements and preferences.
  • Kango brings together
  • more than a thousand sites
  • 400,000 lodging, activity and destinations
    options
  • 20 million reviews, ratings and blogs.

46
How Kango Works
47
Kango Semantics
  • It provides many options for specifying a trip.
  • Kango thinks about those options in terms of the
    Long Tail concept to help make the trips
    distinct and memorable.
  • It "understands" the travel lingo, so it helps
    make informed decisions about what best fits
    specific travel preferences for each user.
  • Kango is creating an ontology of global travel
    content that includes ranking of superlatives
    within review sites.

48
Lodging
49
Things to Do
50
Kango Dynamics
  • Enables new ways of filtering through its
    collection to get the recommendations that are
    most relevant to preferences and priorities.
  • Based on persons traveled with, the kind of
    destination looked for, and what is likely to be
    done, it sifts through its information to deliver
    the right getaway.
  • For example, returns
  • one set of hotel and activity recommendations
    when traveling to Monterey for a romantic getaway
  • a different set when going to Monterey with the
    family to visit the aquarium and hang out on the
    beaches.

51
Old Monterey Inn
52
Campgrounds in Hawai
53
Contents
  • Web 3.0 Semantic Search
  • General Search
  • "Natural Language" Search
  • Vertical Search
  • "Social Networking" Search
  • Personalized Search

54
SemantiNet
  • SemantiNet is a startup, based in Tel Aviv, that
    is creating a new revolutionary technology that
    is based on Semantic Web concepts.
  • Goal leverage Web information in a meaningful
    way to boost the manner users experience the
    Internet.

A. Frank
55
SemantiNet Rational
  • SemantiNet makes life easy by allowing users to
    take advantage of the variety and richness of
    information and services that exist on the
    Internet, but in a way that is simple, smart and
    intuitive.
  • SemantiNet leverages Semantic Web concepts to
    seamlessly integrate information and services
    enabling users to achieve more while working
    less!

A. Frank
56
SemantiNet Repository
  • SemantiNet collects relevant information from
    common social networks and established Web sites
    in order to provide users with a customized and
    efficient personalized and contextual browsing
    experience.
  • Relevant personal information can be
  • entered on their Web site
  • provided by users through use of SemantiNet
  • or extracted from "traffic data" generated by
    browser use.

A. Frank
57
SemantiNet Semantics
  • Develops a semantic framework solution that
    allows for rapid deployment of Web mashups,
    applications and services, in a way that enhances
    the way people use the internet.
  • Rather than simply aggregating information,
    SemantiNets technology, integrates information
    as well as mashing it as needed.
  • The idea is to bring the relevant online content
    to the user rather than the user to the content.

A. Frank
58
SemantiNet Demo
59
SemantiNet Demo
60
SemantiNet Demo
61
SemantiNet Demo
62
Example of Social Graph
63
Delver
  • Delver (formerly Semingo) is headquartered in
    Herzeliya and will officially open U.S. offices
    in Silicon Valley in spring of 2008.
  • Goal provide a semantic search engine that
    allows users to search for information created
    and referenced by their own social graph.

64
Delver Rational
  • Delver provides a connected search engine that
    allows users to find content, media and people
    within their network via a simple search
    interface.
  • Delver organizes and ranks content from the
    users network because social connections are
    critical for discovering more personally relevant
    information.
  • It indexes the social Web (social networks,
    blogs, social applications, etc.), and
    cross-connects the data with users social
    graph.
  • Improves the relevancy of Web search results by
    prioritizing these results based upon the
    specific searchers social network.

65
Delver Repository
  • Delver begins by crawling the Web in order to map
    users social connections.
  • It specifically indexes people's social
    connections on flickr, MySpace, LinkedIn,
    YouTube, hi5, facebook, Blogger, and more sites
    are being added all the time. 
  • Instead of just looking at a Web site's
    popularity, Delver looks at information like
    whether your friends have tagged the site or if
    it's found on their social network profiles,
    bookmarking sites, photos and video sharing
    sites, or on their blogs.
  • The results are more relevant because they
    account for who a person is and what it finds
    valuable.

66
Liad Agmon
67
Venture Funding
68
Delver Semantics
  • Delver knows who a user is and who his friends
    are even if users didn't import their address
    book or add their "Social Networking" profiles.
  • Instead, Delver leverages the social graph to map
    out a user's social connections.
  • Since everyone's social graph is unique, like a
    fingerprint, the same Delver query will yield
    significantly different results for each user
    as reflected through the collective experiences
    of each persons contacts.
  • The results are more personal and meaningful to
    users than a generic search using a "normal"
    search engine.

69
Delver Dynamics
  • When a user performs a query, results from all
    over his social Web are displayed.
  • Even if a user and others are not directly
    related as "friends" on a social network, the
    plus sign the beneath picture can still be
    clicked to add them as a connection.
  • This way, a user can view the relevant bookmarks,
    links, blog posts, photos, and videos of people
    like him even if he doesnt know them
    personally... and they don't have to confirm the
    connection on their end.
  • Alternately, a user can choose to exclude certain
    connections from his search results.

70
Roi Carthy
71
Visit New York
72
Contents
  • Web 3.0 Semantic Search
  • General Search
  • "Natural Language" Search
  • Vertical Search
  • "Social Networking" Search
  • Personalized Search

73
Radar Networks Twine
  • Radar Networks, a pioneer of Semantic Web
    technology, introduced Twine.
  • Goal enables individuals and groups to organize,
    share and discover information and knowledge
    around their interests.

74
Twine Rational
  • Twine is a "knowledge networking" tool designated
    as a revolutionary Semantic Web application.
  • It is a new service that helps organize, share
    and discover information about user interests,
    with networks of like-minded people.
  • Twine can be used alone, with friends, groups and
    communities, or even in a company.
  • It has aspects of social networking, wikis,
    blogging, knowledge management systems but its
    defining feature is that it's built with Semantic
    Web technologies.
  • It aims to bring a usable and scalable interface
    to the long-promised dream of the Semantic Web.

75
Twine Repository
  • Using Twine, a user can
  • add content via Wiki functionality (has many post
    types)
  • email content into the system
  • and "collect" something (as an object, e.g., a
    book object).
  • Twine ties it all together
  • As information is added to Twine, it is
    automatically tagged so that it can be easily
    found.
  • Users can connect with individuals and groups,
    gather and share content, and engage in
    discussions around interests.
  • Twine connects between new people, content and
    products that match their interests, and also
    helps users discover other people and their
    contributions.

76
Twine Semantics
  • Twine is powered by semantic understanding.
  • At first glance it is very much like Wikipedia,
    but there is a whole lot more smarts to the
    system.
  • It's not based around socializing, but aims to
    share information and automatically organize it,
    learn about user interests, and make varied
    connections and recommendations.
  • The more it is used, the better it understands
    the user interests and the more useful it
    becomes.
  • It is a "Semantic Graph", which maps
    relationships to both people and topics.

77
Twine Sign In
78
Twine Dynamics
  • Enables user commenting and viewing of related
    things.
  • Allows sharing of tags.
  • Enables import and export of user own data.
  • RSS feeds to track all kinds of things (topics,
    events, search, etc).
  • Semantic Web technologies are being used RDF,
    OWL, SPARQL, XSL, GRDDL.
  • An open platform - there will be SPARQL and REST
    APIs.

79
Welcome Steve to Twine
80
Explore Green Business and Investing
81
Steve Smiths Twine
82
Explore Green Tech
83
Semantically up -?)
84
Where does the MetaWeb fit?!
85
References
  • Web 3.0, In Wikipedia, The Free Encyclopedia,
    http//en.wikipedia.org/w/index.php?titleWeb_3.0
    oldid123368293
  • Entrepreneurs See a Web Guided by Common Sense,
    John Markoff , New York Times, November 12, 2006,
    http//www.nytimes.com/2006/11/12/business/12Web.h
    tml?ex1320987600en254d697964cedc62ei5088
  • Parts I II A Smarter Web, John Borland,
    Technology Review, March 19-20, 2007,
    http//www.technologyreview.com/Infotech/18396/

86
References
  • M. Hildebrand, J. R. van Ossenbruggen, L.
    Hardman, An Analysis of Search-based User
    Interaction on the Semantic Web, Report
    INS-E0706, May 2007, 6th Intl. Semantic Web
    Conference, November 2007, http//ftp.cwi.nl/CWIre
    ports/INS/INS-E0706.pdf
  • Jim Hendler, Web 3.0 Chicken Farms on the
    Semantic Web, IEEE Computer, January 2008,
    http//www.computer.org/portal/site/computer/menui
    tem.5d61c1d591162e4b0ef1bd108bcd45f3/index.jsp?pN
    amecomputer_level1_articleTheCat1075pathcompu
    ter/homepage/0108fileWebtech.xmlxslarticle.xsl
  • Richard Waters, World-wise Web?, Financial Times,
    http//www.ft.com/cms/s/0/4fba0434-e98c-11dc-8365
    -0000779fd2ac.html?nclick
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