Google Kernel Function

Mehran Sahami
Timothy D. Heilman
Google Inc.

Presented by Beibei Yang

With credits to:
Mehran Sahami, Stanford University,
and
Ellen Spertus, Google Inc.

Mehran Sahami
Associate Professor, Stanford Univ.,
2007—
Google Inc., 2002-2007

Timothy D. Heilman
Sr.
Sr Software Engineer, Google Inc
Engineer Inc.

Presented By: Beibei Yang 2/19/2009 2

Semantic Web
It’s all about understanding!
Semantic similarity
A concept whereby a set of d
h b f documents or terms
within term lists are assigned a metric based on
the likeness of their meaning / semantic content.
Semantic Relatedness
Publicly available means for approximating the
relative meaning o wo ds docu e ts.
elat ve ea g of words/documents.
Have been used for essay-grading by the
Educational Testing Service, search engine
technology, predicting which links people are
likely to click on, etc.

What are they?
Example: Amazon


“What to do when your TiVo thinks you’re
What you re
gay”, Wall Street Journal, Nov. 26, 2002

http://tinyurl.com/2qyepg

What you re


Wal Mart
Wal-Mart DVD recommendations

http://tinyurl.com/2gp2hm

Wal Mart


It s
It’s the degree to which text passages have
the same meaning.
Quite often we want to find how similar two
short text snippets are:
Search engine queries
Course d
C description
i ti
Policies of two insurance company
You name it!


The simplest way to calculate similarity of two
words is to find the minimum length of path
connecting these two. (Has its limitations.)
For example:
Similarity(boy,girl) = 4
Similarity(boy,teacher) = 6

Fig 1: An ISA hierarchical semantic knowledge base

Search Engine
Sahami and Heilman's web-based kernel function.
Bollegala, Matsuo, and Ishizuka's algorithm using page
counts and text snippets.
Iosif and Potamianos's two metric based approach
Potamianos s two-metric approach.
Liu and Birnbaum's approach using Google Directory.
WordNet
Varelas et al 's ontology mapping approach
al. s approach.
Yang and Powers's two-variant based approach: bidirectional
depth-limit search (BDLS) and unidirectional breadth-first
search (UBFS)
Text Corpus
Islam and Inkpen's modified LCS (Longest Common
Subsequence) string-matching algorithm.
Others
Li et al.'s approach using multiple information sources.

Chris Buckley, 1994
Buckley, C., Salton, G., Allan, J., and Singhal, A.
Automatic query expansion using smart: Trec 3. In TREC
(1994), pp. 0-.
The definition and emphasis changed along the way
way.
The process of reformulating a seed query to improve
retrieval performance in information retrieval
operations.
p
Involves:
Finding synonyms of words, and searching for the
synonyms
Finding all the various morphological forms of words by
stemming each word in the search query
Fixing spelling errors and automatically searching for the
corrected form or suggesting it in the results
Re-weighting the terms in the original query


Let x represent a short text snippet, we
calculate the query expansion of x, QE(x) in this
way:
1. Issue x as a query to a search engine S.
2. Let R(x) be the set of (at most) n retrieved
documents d1, d2, … , dn
3. Compute th TFIDF t
C t the term vector vi f each
t for h
document di R(x)
4. Truncate each vector vi to include its m highest
weighted terms
5. Let C(x) be the centroid of the L2 normalized
vectors vi:

6. Let QE(x) be the L2 normalization of the centroid
C(x):


By G Salton and C Buckley 1988
G. C. Buckley,
Weight wi,j associated with with term ti in
document dj is defined to be:

tfi,j is the frequency of ti in dj
N is the total number of documents in the
corpus
dfi is the total number of documents that
contain ti.
t i


Define the semantic kernel function K as the
inner product of the query expansions for
two text snippets.
Given two short text snippets x and y, we
define the semantic similarity kernel
between them as:
K(x, y) = QE(x)·QE(y)


Acronyms:


Individuals and their positions:


Multi faceted
Multi-faceted terms:


Search engine: this approach could be used
g pp
to generate the related query suggestions in
a large-scale system.
Question-answering
Question answering system: the question
could be matched against a list of candidate
answers to determine which is the most
similar semantically.
i il i ll
Since this kernel is not limited to use on the
web,
web it can also be computed using query
expansions generated over domain-specific
corpora in order to better capture contextual
semantics in particular domains
domains.


Berners-Lee, T., Hendler, J., and Lassila, O. The Semantic Web. Scientific American
284, 5 (
, (2001), 34-43.
),
Sahami, M. and Heilman, T. D. 2006. A web-based kernel function for measuring the
similarity of short text snippets. In Proceedings of the 15th international Conference
on World Wide Web (Edinburgh, Scotland, May 23 - 26, 2006). WWW '06. ACM, New
York, NY.
Buckley, C., Salton, G., Allan, J.,
Buckley C Salton G Allan J and Singhal A Automatic query expansion using
Singhal, A.
smart: Trec 3. In TREC (1994), pp. 0-.
Abhishek, V., and Hosanagar, K. Keyword generation for search engine advertising
using semantic similarity between terms. In ICEC '07: Proceedings of the ninth
international conference on electronic commerce (New York, NY, USA, 2007), ACM
pp
pp. 89-94.
Bollegala, D., Matsuo, Y., and Ishizuka, M. Measuring semantic similarity between
words using web search engines. In WWW '07: Proceedings of the 16th international
conference on World Wide Web (New York, NY, USA, 2007), ACM, pp. 757-766.
Iosif, E., and Potamianos, A. Unsupervised semantic similarity computation using
web search engines. IEEE/WIC/ACM international conference on web intelligence
(Nov. 2007), 381-387.
Li, Y., Bandar, Z., and Mclean, D. An approach for measuring semantic similarity
between words using multiple information sources. IEEE transactions on knowledge
and data engineering 15, 4 (July-Aug. 2003), 871-882.


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