Pattern overlapping in PCL

The change patterns available in PCL can overlap each other. That means, a set of atomic change operations can exist as part of two or more identified change patterns. Such overlapping can be either complete or partial.

5.4.2.1 Complete change pattern overlapping

If the sequence of atomic change operations in a change pattern p is actually a subse- quence of atomic change operations available in an another change pattern q, we can say that change pattern p is completely overlapped by change pattern q (i.e. change pattern p is a subpattern of q).

Definition 5.5: For the given two change patterns p1 =< ac1, ac2, ac3· · · acn > and p2 =< bc1, bc2, bc3· · · bcm >, the change pattern p1 is completely overlapped by change pattern p2 if

- ack∈ p1 for k = 1, 2 · · · n, then - ack∈ p2 for all values of k. i.e. p2 ∩ p1 = p1.

Example 5.1: In Figure 5.7, change patterns pc1.1 and pc1.2 are completely overlapped by change pattern pc1.

5.4.2.2 Partial change pattern overlapping

Two change patterns are partially overlapped if they share one or more atomic change operations.

Definition 5.6: For the given two change patterns p1 =< ac1, ac2, ac3· · · acn > and p2 =< bc1, bc2, bc3· · · bcm >, the change pattern p1 is partially overlapped by change pattern p2 if

- ack∈ p1 for k = 1, 2 · · · n, then - ack∈ p2 for at least one value of k. i.e. p2 ∩ p1 6= ∅.

Example 5.2: In Figure 5.7, change patterns pc1 and pc2 are partially overlapped as they share the atomic change operation “6” among their change pattern sequences.

5.5

Summary

The impact of an applied change operation can affect the consistency of the ontology. Thus, a definition of the ontology change operators that supports preservation of con- sistency and maintenance of overall integrity at each level of granularity becomes vital. Most of the time, the intent of an ontology change is not explicitly defined in lower-level

change operators, thus requires representation of changes at higher levels. A layered change log framework fills this gap and helps an ontology engineer in explicitly under- standing ontology changes.

In this chapter, we introduced the layered change log model for explicit operational representation of applied ontology changes. These layered ontology change logs not only provide operational support during the evolution of an ontology, but can also be utilized to extract implicit knowledge, such as change patterns, composite change patterns, causal dependencies among the ontology taxonomy etc. The atomic changes that perform a single change on a single entity of the ontology are stored in a lower level atomic change log (ACL). We utilized five different knowledge gathering aspects (five W’s) in order to represent a single atomic ontology change. We distinguished between the metadata and the change data of the atomic change. The metadata refers to the Id of the change, user and timestamp etc., whereas the change data refers to the change operation, element and the set of parameters.

The changes on a higher level of granularity are stored as change patterns in the pattern change log (PCL). The pattern change log represents and captures the semantics of the applied atomic change operations. To do so, the PCL includes only those segments of evolution of a domain ontology where a generic or domain-specific change pattern has been applied. Similar to the atomic changes, we distinguish between the descriptive data and the pattern data. The descriptive data refers to the metadata of the pattern change (which includes details of the user, label of the pattern, change Id, etc.) and pattern data refers to the involved atomic change operations. The structure of the pattern change log can get complicated due to the existence of overlapping among the identified change patterns (from ACL) and the evolution gaps.

Ontology changes can be stored in RDF triple format. Such triple-based storage facilitates the storage of ontology changes at fine-grained level and can easily be queried using a query language such as SPARQL. One can utilize our pattern mining mechanism

in order to extract higher level change patterns from atomic change logs. We extract the composite changes and the domain-specific change patterns from the atomic change log, which is discussed in detail in Chapters 7 and 8.

Chapter 6

Knowledge extraction from the

atomic change log (ACL)

This chapter acts as an inter-linkage between operational aspects of ontology evolution (discussed in Chapter 4 and 5) and the analytical aspects (discussed in Chapter 7 and 8). The aim here is to give an overview of our approach and discuss the metrics used for mining of change patterns from atomic change log (ACL). We discuss a graph-based formalization of atomic change log data and introduce the metrics utilized for the iden- tification of sequential abstractions from the change log graph. The algorithms for the identification of higher level (level two and three) change patterns are not given here and are discussed in detail in Chapter 7 and 8.

The chapter is structured as follows: In Section 6.1, we discuss the atomic change log data processing steps. It consists of various stages including data cleaning, session identification and data transformation. In Section 6.2, we discuss the formalization of processed atomic change log data into a change log graph. In Section 6.3, we present the analysis of the ontology change log graph and discuss a number of metrics that are used for pattern mining. We discuss the mining of the sequential abstractions from the ontology change log graph in Section 6.4. A brief summary is given in Section 6.5.

6.1

Knowledge extraction process

In this section, we discuss the steps taken for the extraction of the higher level change patterns from the atomic change log. One of the main stages is the data preparation, which overall consists of several steps including necessary data cleaning and filtering and data transformation (Figure 6.1). Researchers use different steps in different arrange- ments based on their viewpoints and requirements. We adopt the main data preparation steps from knowledge discovery for web log data [Ivancsy et al., 2006, Pabarskaite et al., 2007]. For our discussion here, we assume that the unnecessary and incorrect entries from the atomic change log data have been removed.