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Chapter 8
A SURVEY OF ALGORITHMS FOR KEYWORD
SEARCH ON GRAPH DATA
Haixun Wang
Microsoft Research Asia
Beijing, China 100190

Charu C. Aggarwal
IBM T. J. Watson Research Center
Hawthorne, NY 10532

Abstract In this chapter, we survey methods that perform keyword search on graph data.
Keyword search provides a simple but user-friendly interface to retrieve infor-
mation from complicated data structures. Since many real life datasets are repre-
sented by trees and graphs, keyword search has become an attractive mechanism
for data of a variety of types. In this survey, we discuss methods of keyword
search on schema graphs, which are abstract representation for XML data and
relational data, and methods of keyword search on schema-free graphs. In our
discussion, we focus on three major challenges of keyword search on graphs.
First, what is the semantics of keyword search on graphs, or, what qualifies as
an answer to a keyword search; second, what constitutes a good answer, or, how
to rank the answers; third, how to perform keyword search efficiently. We also
discuss some unresolved challenges and propose some new research directions
on this topic.
Keywords: Keyword Search, Information Retrieval, Graph Structured Data, Semi-
Structured Data
© Springer Science+Business Media, LLC 2010

C.C. Aggarwal and H. Wang (eds.), Managing and Mining Graph Data,
Advances in Database Systems 40, DOI 10.1007/978-1-4419-6045-0_8,
249
250 MANAGING AND MINING GRAPH DATA
1. Introduction
Keyword search is the de facto information retrieval mechanism for data on
the World Wide Web. It also proves to be an effective mechanism for querying
semi-structured and structured data, because of its user-friendly query inter-
face. In this survey, we focus on keyword search problems for XML documents
(semi-structured data), relational databases (structured data), and all kinds of
schema-free graph data.
Recently, query processing over graph-structured data has attracted increas-
ing attention, as myriads of applications are driven by and producing graph-
structured data [14]. For example, in semantic web, two major W3C standards,
RDF and OWL, conform to node-labeled and edge-labeled graph models. In
bioinformatics, many well-known projects, e.g., BioCyc (),
build graph-structured databases. In social network analysis, much inter-
est centers around all kinds of personal interconnections. In other applica-
tions, raw data might not be graph-structured at the first glance, but there are
many implicit connections among data items; restoring these connections of-
ten allows more effective and intuitive querying. For example, a number of
projects [1, 18, 3, 26, 8] enable keyword search over relational databases.
In personal information management (PIM) systems [10, 5], objects such as
emails, documents, and photos are interwoven into a graph using manually or
automatically established connections among them. The list of examples of
graph-structured data goes on.
For data with relational and XML schema, specific query languages, such
as SQL and XQuery, have been developed for information retrieval. In or-
der to query such data, the user must master a complex query language and
understand the underlying data schema. In relational databases, information

about an object is often scattered in multiple tables due to normalization con-
siderations, and in XML datasets, the schema are often complicated and em-
bedded XML structures often create a lot of difficulty to express queries that
are forced to traverse tree structures. Furthermore, many applications work on
graph-structured data with no obvious, well-structured schema, so the option
of information retrieval based on query languages is not applicable.
Both relational databases and XML databases can be viewed as graphs.
Specifically, XML datasets can be regarded as graphs when IDREF/ID links
are taken into consideration, and a relational database can be regarded as a data
graph that has tuples and keywords as nodes. In the data graph, for example,
two tuples are connected by an edge if they can be joined using a foreign key;
a tuple and a keyword are connected if the tuple contains the keyword. Thus,
traditional graph search algorithms, which extract features (e.g., paths [27],
frequent-patterns [30], sequences [20]) from graph data, and convert queries
into searches over feature spaces, can be used for such data.
A Survey of Algorithms for Keyword Search on Graph Data 251
However, traditional graph search methods usually focus more on the struc-
ture of the graph rather than the semantic content of the graph. In XML and re-
lational data graphs, nodes contain keywords, and sometimes nodes and edges
are labeled. The problem of keyword search requires us to determine a group
of densely linked nodes in the graph, which may satisfy a particular keyword-
based query. Thus, the keyword search problem makes use of both the content
and the linkage structure. These two sources of information actually re-enforce
each other, and improve the overall quality of the results. This makes keyword
search a more preferred information retrieval method. Keyword search allows
users to query the databases quickly, with no need to know the schema of
the respective databases. In addition, keyword search can help discover unex-
pected answers that are often difficult to obtain via rigid-format SQL queries.
It is for these reasons that keyword search over tree- and graph-structured data
has attracted much attention [1, 18, 3, 6, 13, 16, 2, 28, 21, 26, 24, 8].

Keyword search over graph data presents many challenges. The first ques-
tion we must answer is that, what constitutes an answer to a keyword. For
information retrieval on the Web, answers are simply Web documents that
contain the keywords. In our case, the entire dataset is considered as a sin-
gle graph, so the algorithms must work on a finer granularity and decide what
subgraphs are qualified as answers. Furthermore, since many subgraphs may
satisfy a query, we must design ranking strategies to find top answers. The
definition of answers and the design of their ranking strategies must satisfy
users’ intention. For example, several papers [16, 2, 12, 26] adopt IR-style
answer-tree ranking strategies to enhance semantics of answers. Finally, a ma-
jor challenge for keyword search over graph data is query efficiency, which to a
large extent hinges on the semantics of the query and the ranking strategy. For
instance, some ranking strategies score an answer by the sum of edge weights.
In this case, finding the top-ranked answer is equivalent to the group Steiner
tree problem [9], which is NP-hard. Thus, finding the exact top 𝑘 answers
is inherently difficult. To improve search efficiency, many systems, such as
BANKS [3], propose ways to reduce the search space. As another example,
BLINKS [14] avoids the inherent difficulty of the group Steiner tree problem
by proposing an alternative scoring mechanism, which lowers complexity and
enables effective indexing and pruning.
Before we delve into the details of various keyword search problems for
graph data, we briefly summarize the scope of this survey chapter. We classify
algorithms we survey into three categories based on the schema constraints in
the underlying graph data.
Keyword Search on XML Data:
Keyword search on XML data [11, 6, 13, 23, 25] is a simpler prob-
lem than on schema-free graphs. They are basically constrained to tree
252 MANAGING AND MINING GRAPH DATA
structures, where each node only has a single incoming path. This prop-
erty provides great optimization opportunities [28]. Connectivity infor-

mation can also be efficiently encoded and indexed. For example, in
XRank [13], the Dewey inverted list is used to index paths so that a key-
word query can be evaluated without tree traversal.
Keyword Search over Relational Databases:
Keyword search on relational databases [1, 3, 18, 16, 26] has attracted
much interest. Conceptually, a database is viewed as a labeled graph
where tuples in different tables are treated as nodes connected via
foreign-key relationships. Note that a graph constructed this way usu-
ally has a regular structure because schema restricts node connections.
Different from the graph-search approach in BANKS [3], DBXplorer [1]
and DISCOVER [18] construct join expressions and evaluate them, re-
lying heavily on the database schema and query processing techniques
in RDBMS.
Keyword Search on Graphs: A great deal of work on keyword query-
ing of structured and semi-structured data has been proposed in re-
cent years. Well known algorithms includes the backward expanding
search [3], bidirectional search [21], dynamic programming techniques
DPBF [8], and BLINKS [14]. Recently, work that extend keyword
search to graphs on external memory has been proposed [7].
This rest of the chapter is organized as follows. We first discuss keyword
search methods for schema graphs. In Section 2 we focus on keyword search
for XML data, and in Section 3, we focus on keyword search for relational
data. In Section 4, we introduce several algorithms for keyword search on
schema-free graphs. Section 5 contains a discussion of future directions and
the conclusion.
2. Keyword Search on XML Data
Sophisticated query languages such as XQuery have been developed for
querying XML documents. Although XQuery can express many queries pre-
cisely and effectively, it is by no means a user-friendly interface for accessing
XML data: users must master a complex query language, and in order to use

it, they must have a full understanding of the schema of the underlying XML
data. Keyword search, on the other hand, offers a simple and user-friendly in-
terface. Furthermore, the tree structure of XML data gives nice semantics to
the query and enables efficient query processing.
A Survey of Algorithms for Keyword Search on Graph Data 253
2.1 Query Semantics
In the most basic form, as in XRank [13] and many other systems, a keyword
search query consists of 𝑛 keywords: 𝑄 = {𝑘
1
, ⋅⋅⋅ , 𝑘
𝑛
}. XSEarch [6] extends
the syntax to allow users to specify which keywords must appear in a satisfying
document, and which may or may not appear (although the appearance of such
keywords is desirable, as indicated by the ranking function).
Syntax aside, one important question is, what qualifies as an answer to a
keyword search query? In information retrieval, we simply return documents
that contain all the keywords. For keyword search on an XML document, we
want to return meaningful snippets of the document that contains the keywords.
One interpretation of meaningful is to find the smallest subtrees that contain all
the keywords.
A
B
C D
x
y
x
y
exclusive LCA node
minimal LCA node

Figure 8.1. Query Semantics for Keyword Search 𝑄 = {𝑥, 𝑦 } on XML Data
Specifically, for each keyword 𝑘
𝑖
, let 𝐿
𝑖
be the list of nodes in the XML
document that contain keyword 𝑘
𝑖
. Clearly, subtrees formed by at least one
node from each 𝐿
𝑖
, 𝑖 = 1, ⋅⋅⋅ , 𝑛 contain all the keywords. Thus, an answer to
the query can be represented by 𝑙𝑐𝑎(𝑛
1
, ⋅⋅⋅ , 𝑛
𝑛
), the lowest common ancestor
(LCA) of nodes 𝑛
1
, ⋅⋅⋅ , 𝑛
𝑛
where 𝑛
𝑖
∈ 𝐿
𝑖
. In other words, answering the
query is equivalent to finding:
𝐿𝐶𝐴(𝑘
1
, ⋅⋅⋅ , 𝑘

𝑛
) = {𝑙𝑐𝑎(𝑛
1
, ⋅⋅⋅ , 𝑛
𝑛
)∣𝑛
1
∈ 𝐿
1
, ⋅⋅⋅ , 𝑛
𝑛
∈ 𝐿
𝑛
}
Moreover, we are only interested in the “smallest” answer, that is,
𝑆𝐿𝐶𝐴(𝑘
1
, ⋅⋅⋅ , 𝑘
𝑛
) = {𝑣 ∣ 𝑣 ∈ 𝐿𝐶𝐴(𝑘
1
, ⋅⋅⋅ , 𝑘
𝑛
) ∧
∀𝑣
′
∈ 𝐿𝐶𝐴(𝑘
1
, ⋅⋅⋅ , 𝑘
𝑛

), 𝑣 ⊀ 𝑣
′
}
(8.1)
where ≺ denotes the ancestor relationship between two nodes in an XML
document. As an example, in Figure 8.1, we assume the keyword query
is 𝑄 = {𝑥, 𝑦}. We have 𝐶 ∈ 𝑆𝐿𝐶𝐴(𝑥, 𝑦) while 𝐴 ∈ 𝐿𝐶𝐴(𝑥, 𝑦) but
𝐴 ∕∈ 𝑆𝐿𝐶𝐴(𝑥, 𝑦).
Several algorithms including [28, 17, 29] are based on the SLCA semantics.
However, SLCA is by no means the only meaningful semantics for keyword