disambiguation. However, categories in Wikipedia are dirty, as is the thematic domain representation, so it is not clean enough for purpose of ontology purpose [63]. Han and Zhao [37] use a reference entity table for name disambiguation. The reference entity table is a set of personal names and the corresponding occupation (e.g. Michael Jordan, Basketball Player) which is extracted from Freebase. Han and Zhao use only a single occupation to describe a person.
In our work, we propose a new occupation taxonomy architecture: OAPnDis, which is derived from YAGO and the web directory. We employ a clean and well-formed occupation taxonomy which provides a more details to represent a personal concept in each person. Our approach differs from Han and Zhao because we introduce the personal name concept, which is a list of occupations to describe a person.
3.7
Conclusions
This chapter introduces a new technique to create a personal surface form, a new occupation taxonomy OAPnDis and a PNDDC structure.
Firstly, PNSFM is introduced to construct the personal surface form and the personal name entity relations. Unlike the previous study, our technique is independent from the data source and includes a data pre-processing component prior to integration in a database. The independent source means that the personal surface form or the personal name entity relations can be generated from any knowledge base. Data pre-processing is a data cleaning technique that is used to remove dirty data before extracting and matching facts.
Secondly, we propose a new occupation taxonomy: OAPnDis. The architecture is based on two well known knowledge bases: Dmoz and YAGO. The new occupation architecture can create a well-formed and semantic occupation taxonomy. As the result, personal name concepts are created based on OAPnDis.
Finally, we introduce the PNDDC structure. PNDDC can be separated into two parts: personal name surface form and personal name entity details (personal name entity, per- sonal name concepts and personal name entity relations). This structure is advantageous because this thesis wants to distinguish information between the searcher component and the disambiguator component in personal named entity linking, as described in Chapter 5.
We have found that occupation is the best feature to disambiguate lexical ambiguity and that only 0.56% of lexical ambiguity cannot be distinguished using a single occupation.
Chapter 4
Personal Name Transformation With
Context Free Grammar
In this chapter we introduce Personal Name Transformation Modules (PNTM) that are based on Context Free Grammar (CFG) rules and personal name dictionary. PNTM is used to transform a variety of name formats (e.g. nickname, alias, different order) into a uniform representation (e.g. DB7 vs. David Beckham or Beckham, David vs. David Beckham).
4.1
Motivation
A proper name is a key component in personal name entity linking due to a lack of primary key representation. Textual similarity metrics (e.g. Jaro-Winkler and cosine similarity) or the learning based approaches (e.g. SVMs, decision tree and naïve Bayes) are widely used in data matching [2]. Jaro-Winkler is a character-based comparison metric that was designed to solve typographical error problems [25]. Cosine similarity is a token-based similarity metric that aims to handle the rearrangement of words. However, several studies [6, 15, 54, 74] have revealed that the Jaro-Winkler metric performs well with personal name data. Furthermore, the study [18] found that the cosine similarity technique is faster than Jaro- Winkler by 16.67 times. Moreover, the cosine similarity metric is used in [8, 19, 24] for name entity disambiguation.
62 Personal Name Transformation With Context Free Grammar
Table 4.1 Example of personal name variations.
ID Personal names A unique personal name format 1 George W. Bush George W. Bush
2 Bush, George W. George W. Bush 3 Bush, G.W. G.W. Bush 4 43rd President of the United States George W. Bush 5 Bill Clinton William Clinton 6 William Clinton William Clinton 7 Timberlake Justin Timberlake 8 Justin Timberlake Justin Timberlake
A fundamental problem in personal name matching is referential ambiguity [3, 7]. Ac- tually, a wide variety of contexts can be used to refer to an individual person (a person has many names) such as the official position, nickname, short name or full name. As shown in Table 4.1, we can see that:
1. ID 1-4, four different terms are used to refer to the same person: George W. Bush. 2. Personal names ID 1-2, the same personal name but appears in a different order. 3. The nickname Bill is used to refer to the given name William in ID 5.
4. The alternative name Timberlake is used to refer to the personal name Justin Timber- lake.
Arasu and Kaushik [3] have suggested that text similarity measurement may provide a poor effective similarity value when we use different words to mention the same person. The per- sonal name IDs: 1 and 2 are equal but are represented using a different order and cannot be detected by Jaro-Winkler. On the other hand, cosine similarity can detect the similarity be- tween IDs: 1 and 2 because the order of words is insignificant for this algorithm. Moreover, both Jaro-Winkler and cosine similarity cannot detect the similarity between two strings if two strings are completely used a different letters (e.g. IDs: 1 and 4). Likewise, the similar- ity scores between IDs: 2 vs. 3 and IDs: 7 vs. 8 based on Jaro-Winkler or cosine similarity are less because only some parts of names are similar.
Christen et al. [16] introduced probabilistic Hidden Markov Models (HMM) for cleaning and standardisation of personal names and addresses. The HMM is used to segment the personal name component. The accuracy results show that a rules based approach is better than HMM. The HMM failed to segment the personal name component when a person has
4.1 Motivation 63
either two given names or two last names. Furthermore, this technique is insufficient for our problem because this method only solves the different order in a personal name.
Arasu [3] proposed a programmatic framework based on CFG and a personal name dictionary to transform personal names and their affiliations which are represented in mul- tiple forms into a uniform representation. A programmatic framework means how an input record is transferred to output records and it is specified using a declarative programme[3]. The advantage of this module is not only dealing with the different sequences of personal components but also transforming a nickname into a given name. The CFG method differs from the black-box similarity function because it enables us to understand the internal struc- ture of a personal name component. For example, the word Rose that occurs in a personal name cannot transform into Roxana if it is not a given name. However, there are two certain drawbacks with the use of this module:
Firstly, the objective of this framework is to transform personal names that are stored in a database that are less variation than a mentioned name within a web page. In our work, we expand the personal name dictionary by adding alternative names that we extracted from the YAGO knowledge base. Therefore, the new personal name dictionary contains a list of prefixes, suffixes, given names, last names, nicknames and alternative names.
Secondly, Arasu parsed a whole name over personal name dictionary and then the uni- form weighting scheme is used to evaluate whether each word in a whole name could be a prefix, suffix, given name or last name. Arasu gave a different non-negative real number weight to a CFG rule and the output which has the lowest weight will be considered. How- ever, the weight value has limitations when a word can be qualified as multiple answers and the weighting scores in CFG rules are equal. For example, the words Ferguson or Black can be used as both a given name and a last name. To handle this problem, we combine regular grammar of English personal name patterns and a set of CFG rules to segment a sequence of strings in a personal name . After that, the type of each word in a sequence is identified (e.g. it is a prefix, a suffix, a given name, a last name, a nickname or an alternative name) and exactly matched over the type of names in the dictionary. As a result, the uniform weighting scheme is removed from Personal Name Transformation Modules (PNTM).
The purposes of this chapter as follows. Firstly, Section 4.2 describe the background to the regular grammar for English personal name, Context Free Grammar and String match- ing techniques. Section 4.3 describes the PNTM based on CFG and personal name dic- tionary to transform multiple personal name patterns into uniform representation. Section 4.4 describes experimental results and discussion. This section aims to compare the perfor- mance of two string matching techniques: Jaro-Winkler and cosine similarity within terms of which technique will provide the best results in personal name matching when working
64 Personal Name Transformation With Context Free Grammar
with PNTM.