1. MCN: A New Semantics Towards Effective XML Keyword Search
Junfeng Zhou, Xiaofeng Meng
Renmin University of China
Zhifeng Bao, Tok Wang Ling
National University of Singapore

2. Outline Introduction Preliminaries XML Keyword Search Semantics
Query Processing
Experiment
Conclusions
2019/2/18

3. Outline Introduction Preliminaries XML Keyword Search Semantics
Query Processing
Experiment
Conclusions
2019/2/18

4. Introduction XML data is pervasive nowadays
Effective search mechanism is indispensable
Structured query languages, e.g. XQuery or XPath, may be too complex for naïve users
XML keyword search is EFFECTIVE when
The query language is complex
E.g. XQuery or XPath
The underlying schema is complex or unavailable
E.g. XMark (327 schema elements) [1]
Querying heterogeneous XML documents
[1] C. Yu etc. Querying complex structured databases. VLDB2007.
2019/2/18

5. Introduction
Observations for XML keyword search on data-centric XML document
Each data element has a category, e.g. entity or attribute of ER-model
Users query intensions are based on the relationships of entity nodes
A data fragment is meaningful means the relationships between entity nodes can be interpreted by the information of the data fragment itself
2019/2/18

6. Introduction Existing XML Keyword Search Semantics
Lowest Common Ancestor (LCA)
Smallest Lowest Common Ancestor (SLCA)
Connected Network (CN)
Existing semantics consider only structural information when determining whether a data fragment is a matched one
XML Document
2019/2/18

7. Introduction Problems of Existing Semantics
Return meaningless results, R1,R2,R4
Lose meaningful results, R1’,R4’
The relationship of the two person nodes cannot be interpreted by R4 itself
R4’ means that Mike bought the item sold by John
The relationship of the two person nodes cannot be interpreted by R2 itself
R1’ means that both Mike and John provided photos about the same item
The relationship of the two photo nodes cannot be interpreted by R1 itself
XML Document
2019/2/18

8. Introduction Contributions An XML Keyword Search Semantics
MCN (Meaningful Connected Network)
R3 means that Mike and John are watching the same auction
R4’ means that Mike bought the item sold by John
R1’ means that both Mike and John provided photos about the same item
XML Document
2019/2/18

9. Introduction Query Processing of directed graph based methods [1,5-8]
Finding all Connected Networks from XML Graph is NP-Complete [9]
The first group [7,8] finds only a subset of all results
The second group [1,5,6] uses a two-step strategy
(1) Identify the set of structured Query Patterns
(2) Evaluate Query Patterns to get the matching results
[1] Cong, Y., etc. Querying Complex Structured Databases. VLDB2007
[5] Vagelis, H., etc. Keyword Proximity Search on XML Graphs. ICDE2003
[6] Sara, C., etc. Interconnection semantics for keyword search in XML. CIKM2005
[7] Konstantin, G., etc. Keyword proximity search in complex data graphs. SIGMOD2008
[8] Hao, H., etc. BLINKS: ranked keyword searches on graphs. SIGMOD2007
[9] Reich, G.,etc. Beyond Steiner’s problem: a VLSI oriented generalization. WG Workship (1990)
2019/2/18

10. Keywords used to compute query patterns are schema elements
Introduction
Q1 = {person: Mike, auction: }
Q2 = {person: Mike, person: John}
Problems of the two-step methods [1,5,6]
For the first step
[1,6] find Query Patterns of schema elements from the schema graph, where text values are attached to different schema elements
Keywords used to compute query patterns are schema elements
Cannot process queries involving text values attached to the two schema elements of same name
Schema graph
2019/2/18
[1] Cong, Y., etc. Querying Complex Structured Databases. VLDB2007
[5] Vagelis, H., etc. Keyword Proximity Search on XML Graphs. ICDE2003
[6] Sara, C., etc. Interconnection semantics for keyword search in XML. CIKM2005

11. Introduction Problems of the two-step methods [1,5,6]
For the first step
[1,6] find Query Patterns of schema elements from the schema graph, where text values are attached to different schema elements
[5] needs to scan data elements to produce Query Patterns
For the second step
All methods suffer from costly structural join operations1 to process all Query Patterns
1 structural join operation denotes the join operation determining ancestor-descendant or parent-child relationship
2019/2/18
[1] Cong, Y., etc. Querying Complex Structured Databases. VLDB2007
[5] Vagelis, H., etc. Keyword Proximity Search on XML Graphs. ICDE2003
[6] Sara, C., etc. Interconnection semantics for keyword search in XML. CIKM2005

12. Introduction Contributions An XML Keyword Search Semantics
MCN (Meaningful Connected Network)
An efficient query processing algorithm
Uses the two-step strategy
For the first step
Avoids scanning real data
For the second step
Avoids costly structural join operations
2019/2/18

13. Outline Introduction Preliminaries XML Keyword Search Semantics
Query Processing
Experiment
Conclusions
2019/2/18

14. Preliminaries Schema Directed Graph
Always available, otherwise we can infer it by [1,2]
Containment Edge
Reference Edge
1:1 relationship
1:n relationship
Schema Graph
[1] Geert, J.B., etc.: XML Schema Definitions from XML Data. VLDB2007
[2] Geert, J.B., etc.: Inference of Concise DTDs from XML Data. VLDB2006
2019/2/18

15. Preliminaries Node Categories Entity, Attribute, Connection Node
How to specify the category of each node?
Heuristics [1,2] + Manual adjustment
Used to organize data
Connection Node
Entity Node
Corresponding to a “*”
Same to the concept of ER-model
Attribute Node
Corresponding to a “leaf” node
Schema Graph
[1] C. Yu etc. Querying complex structured databases. VLDB2007
[2] Ziyang, L etc. Identifying meaningful return information for XML keyword search. SIGMOD2007
2019/2/18

16. Outline Introduction Preliminaries XML Keyword Search Semantics
Query Processing
Experiment
Conclusions
2019/2/18

17. XML Keyword Search Semantics
Observation of a “meaningful” data fragment
The directions of the edges on a path from one element to another may conflicts with each other
Traversing the XML graph to compute all meaningful data fragments is infeasible because of its large size
An alternative way is finding meaningful relationship from schema graph
A possible relationship of mixed direction getting from the given schema graph may be meaningless
We need
Firstly identify all possible relationships of schema elements
Then identify those meaningless ones and just keep the meaningful ones
Schema graph
2019/2/18
Meaningless, because R’ doesn’t have database instances in practice

18. XML Keyword Search Semantics
For the mixed direction problem
Define “Walk” to return all connected relationships
A walk consider a schema graph as an undirected graph, starts at a schema node, after a series of nodes and edges, ends at another schema node
For the meaningfulness problem
Define “Meaningful Entity Walk (MEW)” to filter out useless relationships
A MEW is a walk that denotes a relationship between two entity nodes that can be interpreted by the walk itself
Meaningful, means that keywords may be about the same person
Meaningless, because W2 doesn’t have database instances in practice √ √ √
2019/2/18
Meaningless, because W5 cannot tell what is the relationship between item and person
Meaningful, means that the photo and video contain information of the same item
Meaningful, means that a person is watching an auction

19. XML Keyword Search Semantics
For keyword search query
Define “Meaningful Connected Network”
Contains each keyword at least once
There exists at least one Meaningful Entity Walk between each pair of entity nodes
Meaningless, the relationship of the two person nodes in R2 and R4 cannot be interpreted by R2 and R4 themselves
Meaningless, the relationship of the two photo nodes cannot be interpreted by R1
Meaningful, the two persons provided photos about the same item
Meaningful, one person bought the stuff sold by another person
Meaningful, the two persons are watching the same auction √
2019/2/18
Data fragments containing Mike and John

20. XML Keyword Search Semantics
Since the joining sequence of two data elements is data bound1, usually, users are required to specify the maximum size of the returned results.
Keyword Search Problem
For a given keyword query Q, find all matched MCNs from the given XML document D, where each MCN contains at most C entity instances.
R3.size = 12
R1’.size = 7
R4’.size = 11
If MAXsize = 10, R3 and R4’ will be discarded
But they all contain 3 entity nodes
In our method, C is the number of maximum entity nodes, so we can avoid returning data fragments conveying very weak semantics with overwhelming entity nodes
C = 3 by default
2019/2/18
1Data bound means the size of a result may be as large as the number of nodes in an XML document

21. Outline Introduction Preliminaries XML Keyword Search Semantics
Query Processing
Experiment
Conclusions
2019/2/18

22. Query Processing Schema Graph Entity Graph Entity Path Partial path
An entity path is a meaningful entity walk of the schema graph that contains just two entity nodes
Partial Path
a partial path is a path of the schema graph that starts from and entity node and ends at an attribute node
Entity Graph: a schema graph that just keeps all entity nodes and their connection relationships, i.e., entity path
Partial path
Entity Graph
Schema Graph
2019/2/18

23. Query Processing Identify Query Patterns Entity Graph Query Patterns
e3: entity path
Partial path
Only shows the entity path
Identify Query Patterns
Query Patterns
Entity Graph
Do not need to scan real data
Theorem: For a given keyword query Q, our method produces all query patterns satisfying that each one has at most C entity nodes
Observation: A Query Pattern consists of a set of Entity Paths and a set of Partial Paths
1 selfE(k) returns a set of entity nodes that have entity instances containing k as their attribute or attribute values
2019/2/18

24. Query Processing Process all Query Patterns Entity Graph XML document
Observation: A Query Pattern consists of a set of Entity Paths and a set of Partial Paths
Process all Query Patterns
Entity Graph
XML document
Entity Path Index: for each entity path e, store the set of path instances of e
Partial Path Index: for each keyword k, it records the set of partial paths and their database instances that contain k as their text value
2019/2/18

25. Query Processing Process all Query Patterns
Q = {Mike, John}
Entity path
Partial path
Process all Query Patterns
A keyword query Q corresponds to a set of Query Patterns
A Query Pattern consists of a set of Entity Paths and a set of Partial Paths
The result set of each entity path and each partial path can be got by probing Entity Path Index and Partial Path Index
Theorem: Let Q be a given keyword query. Using EPI and PPI, the structural join operations1 can be avoided from the evaluation of Q
2019/2/18
1 structural join operation denotes the join operation determining ancestor-descendant or parent-child relationship

26. Query Processing Process all Query Patterns Identifying redundant QPs
“~” means containment relationship
Process all Query Patterns
Identifying redundant QPs
Correct Answer
Q = {Mike, John}
schema
doc
Query Patterns
Results
2019/2/18
both provider nodes denote the same data elements, and their text value are same to each other, which contradicts with the given xml document

27. First identify which selection will produce empty result set
Query Processing
Process all Query Patterns
Q = {Mike, John}
First identify which selection will produce empty result set
Entity
Path
Index
Partial
Path
Index
2019/2/18

28. Outline Introduction Preliminaries XML Keyword Search Semantics
Query Processing
Experiment
Conclusions
2019/2/18

29. Experiment Experiment Setup
Implemented SLCA, XSEarch, IM (our method) algorithms using Microsoft Visual C++ 6.0
Query engines used in our experiment
X-Hive1 and MonetDB2
2019/2/18 1 2

30. PPI+EPI+Assistant index
Experiment
Datasets, Indices
Queries
40 keyword queries
4 groups with 2,3,4,5 keywords respectively
PPI+EPI+Assistant index
2019/2/18

31. Experiment Evaluation Metrics Precision & Recall Running time
Users submit a keyword query
We write XQuery expressions corresponding to their keyword query by asking their query intension, the result set is R, which is got by running MonetDB query engine
Process the given keyword query using different algorithms, the result of a special algorithm is RQ
Running time
2019/2/18

32. Experiment
Experimental Results
2019/2/18

33. Outline Introduction Preliminaries XML Keyword Search Semantics
Query Processing
Experiment
Conclusions
2019/2/18

34. Conclusions
We proposed a new semantics, i.e. MCN, based on relationships of entity nodes to capture meaningful information considering IDREF
We proposed an entity graph based method, which can produce all query patterns while avoid scanning real data
We proposed two efficient indices, based on which our method can avoid structural join operations by equivalently transforming structural join operations into value join operations
We conducted experiments to verify the effectiveness and efficiency of our method
On going work
A good ranking mechanism considering node categories
2019/2/18

35. Thank you!
2019/2/18