CHAPTER 5 EXPERT LOCATOR USING CONCEPT LINKING

Transcription

1 94 CHAPTER 5 EXPERT LOCATOR USING CONCEPT LINKING 5.1 INTRODUCTION Expert locator addresses the task of identifying the right person with the appropriate skills and knowledge. In large organizations, it is standard to employ an expert locator system to find out candidates who are well matched to a job requirement (Dan Crow & DeSanto 2005) and there are several researches have been performed to mine the information from the structured/semi structured resume documents. These systems work by extracting meaningful concepts from resumes and matching those concepts to job descriptions. In any academic and research institute, it is necessary to find professional people and subject expert in a particular subject area for problem consulting or team building with certain attributes (concepts). This chapter describes the significant features of such a system that locates professional people from any educational and research institutes all over the world. It is an interesting real-world problem and is the most important necessity for every academician and researcher. four major steps: ed in this chapter has the following Crawling the live data from educational web pages, blogs, News wires, Article pages etc.

2 95 Concept Detection and Extraction - It analyzes and extracts concepts Concept Linking It identifies links between the concepts and thus helps the user to make a decision or generate hypothesis. Faceted browsing A Web Crawler is software that browses the World Wide Web in a methodical and automated manner. Crawlers can be used to gather specific types of information from web pages. There are lots of open source tools available for crawling data. Carrot 2 tool for crawling data from web was used in this system. Concept Detection and Extraction is the task of automatically extracting concepts from unstructured documents. In this system, concepts ficial information, educational and professional qualification, experience in the subject, knowledge, skills, research information, publication details etc. Concept Extraction is a specific type of information extraction as described in (Boris et al 1998) and is driven by a domain specific knowledge. Concept Linking is the process of connecting related data/documents by identifying their commonly shared concepts based on their co-occurrence and closeness within the document. Concept is an event/record/object which has high relevance in that context. Concepts can be considered as the key phrases that contribute to the context. Key phrases can be extracted and converted to concepts, using a thesaurus or with the help of well-formed rules. The mappings of words to concepts can be ambiguous, as each word in a given language can relate to several possible concepts. The wealth of recorded knowledge (entire corpus) is greater than the sum of its parts, and interesting links and hidden information that connect facts can be formed to discover previously unknown logic connections.

3 96 Some discoveries done using concept linking are o Connection implying patient benefit between fish oil and that the benefit was real. o Magnesium deficiency was identified as a contributor to - -> Figure 5.1 Overview of Concept Linking Figure 5.1 gives a brief overview of concept linking. The process is basically repetitive, that is, the entire process is repeated until satisfactory results are obtained. Text refining This involves detecting the format of the text and converting it to usable machine understandable format, then followed by the basic text preprocessing and feature selection.

4 97 Concept extraction Concept extraction plays a primary role in various fields like concept linking and information retrieval. It involves extraction of semantic features from raw texts which are linked to form an intermediary form. There are two approaches in concept extraction. The first approach involves extracting only the nouns in the text, that is, the document is decomposed into semantic themes (nouns). These themes are clustered to retain minimal set of themes that ensure a correct coverage of a corpus. Then words that represent each concept are found. The second approach involves feature extraction. Not just the nouns but all important features (like named entities, common relationships associated with person or organization, subject-verb-object relationship) are extracted. Intermediary form creation The extracted concepts are represented in one of the machine understandable intermediary forms. The intermediary form gives the concepts and relations among those concepts. The extracted concepts are represented in ontology. Ontology is a conceptual graph where nodes are concepts and links are relations, and where each relation links a certain number of concepts. A concept can represent an entity, an attribute, a state or an event. o o o It involves identifying frequent item sets (FIS). FIS are taken as vertices of weighted graph (weight being distance between concepts) It has edges with corresponding weights which represent the strength of the relationship between the connected concepts.

5 98 Generating concept chain Concept chain is the path that connects two concepts. Two concepts A and B can be connected through multiple paths. This phase extracts all these paths and finds an optimal degree of relationship between these concepts. It involves concept chain querying. It aims to query out the concept chain between two concepts. The retrieved chains are first grouped by association degree, which is indicated by the length of the links, and then ranked by association strength (estimated potential) within each group. Maximum length threshold can be set. To identify links between two concepts A and B o o Test for direct connection (association) between them (e.g. cooccurrence) Test if they are connected by intermediary concepts (path) Evidence trail generation Presenting that two concepts are related, and giving a score to represent the strength of the relationship is just not sufficient. There have to be some proof available to say that the concepts are related. Thus, in this phase, in addition to the fact that two concepts are related, the text snippets that prove that the concepts are related can also be mined out. Presentation of results This is the final visualization stage where the related concepts and the links between them are presented in a way which will help user understand easily.

6 99 Faceted browsing is a technique for accessing a collection of information represented using a faceted classification, allowing users to explore by filtering available information. 5.2 PROBLEM STATEMENT expertise and/or experience for a given subject. The aim of the system is to provide user with the list of experts that matches their searching criteria. The key requirements for the Expert Locator are the ability to: a) Identify Experts b) Classify the type and level of expertise c) Validate for relevant expert d) Recommend experts by ranking. The proposed approach is a concept linking approach. Identifying experts is a difficult task because experts matching the required skills and knowledge are rare, expensive, (unevenly) distributed, difficult to qualify, continuously changing, varying in level, and often expertise using one or two parameters. M qualification, teaching experience, teaching subjects, department, research interest, context of their activities like publications as author and co-author, associations, patents and awards received, member in any board / panel, involvement in discussion forums, etc are used to identify the expert. This information are collected s web page, publication repository, social network sites, blogs, research forums, etc. The system strives to provide the user with linked critical information which in turn helps in saving the effort of manually analyzing and inferring from the web pages saving time that the user might otherwise spend

7 100 by searching for an expert with specific skills by shifting through each web page. The system extracts out details or concepts and semantically annotates the concepts by finding the category they belong to and allows the users to explore by filtering available information. 5.3 LITERATURE SURVEY To reflect the growing interest in expert finding TREC introduced an expert finding task at its enterprise track in 2005 (Craswell et al 2006). There are several researches that have been performed to propose various methods for expert finding. All these methods can be classified into either candidate model or document model (Balog et al 2006). For the document model, normal document retrieval is carried out to score all the documents in the corpora at first. Then each candidate is evaluated against the related document. Since this approach is not domain specific, the accuracy is low. That is, the rules for text in one document will not apply to another document. It is clear from Wei Zhang et al (2009) that the mean average precision is only (which is less than 50%). So it is understood that document model results in less accuracy. Candidate models (Fang & Zhai 2007; Petkova & Croft 2006) build a textual (usually term-based) representation of candidate experts, and rank them based on a query/topic, using traditional ad-hoc retrieval models. Previous studies on classifying names into some particular categories from text, such as the names of people, places, and organizations focus on combining abundant rules or trigger words to enhance the system performance. Using SVM, high accuracy can be obtained (Xu-Dong Lin et al 2006). The advantage of rule based concept annotators is that the rules are human comprehensible and can be tweaked to get the desired results (Prasad

8 101 M Deshpande et al 2009). Participants of the Text Retrieval Conference (TREC 2007) have investigated numerous methods, including probabilistic and language modeling techniques for expert finding (Bailey et al 2007). There have been various studies presented for evidence collection to represent among people. As given in the problem description, the first key requirement of expert locator is the ability to identify experts. Several approaches have been proposed to, (Campbell et al 2003; Balog & de Rijke 2006) used network, (Jing Zhang et al 2010; Fu et al 2007) used social network, (Maryam Karimzadehgan et al 2009) used organizational hierarchy, (Breslin et al 2007) proposed an approach to identify expertise using discussion forums, user communities and social networks, (Wei Zhang et al 2009) used in corporation of similar people to collect the evidence, (Tang et al 2007) identify experts from their publications from DBLP, (David et al 2010) use experts database to recommend experts. Martha et al (2009) performed a study on the usability of tags for concept extraction and determining equivalence relations between concepts based on the tag sets associated with these concepts. There are different approaches to the problem of assigning each word of a text with a Parts-of- Speech Tag (POSTag). Fahim et al (2007) made a comparative study on different POSTagging techniques and suggested that the rule based tagger as the best one. Cunningham et al (2002) presented GATE tagger framework which is rule based. All the existing approaches use one or two parameters to identify to using one or two events. In this chapter a method for expert finding using concept linking which is based on candidate model is proposed. Multiple taught,

9 102 department, research interest, context of their activities like publications as author and co-author, associations, patents and awards received, member in any board / panel, involvement in discussion forums, etc. are used to identify the expert. These information are collected and/or organization s web page, publication repository, social network sites, blogs, research forums, etc. 5.4 SYSTEM ARCHITECTURE The proposed method uses concept linking approach to identify the expertise of people. The system consists of the following three main layers and the overall architecture is given in Figure 5.2. Data Layer Control (Processing) Layer Presentation Layer Figure 5.2 Architecture of Expert Locator

10 103 The Data Layer holds all the necessary data required for the system. The input to this layer is from the Web Crawler. The Processing / Control layer does the major text processing and extracting and linking concepts. There are three components in this layer. Preprocessor component helps in finding and validating the input comes from the data store. It also converts the format to a common machine usable one. Concept Extractor involves tokenizing, POSTagging of the preprocessed text. It then recognizes the named entities, and normalizes. The normalized text is then parsed to semantically extract categorized details. Concept Linker finds out the relationship between the concepts extracted by the concept extractor. The Presentation layer helps the user to view the linked concepts. The user can choose or filter out concepts and view only the necessary ones. This system visualizes the concepts as facets. 5.5 SYSTEM WORKFLOW Expert Locator starts by crawling of live data from various websites and converts the data to a common format, mining the converted data and visualizing the mined data. Web crawlers and converting to common format form the preprocessing step of the system, while faceted browser gives the visualization. The overall system workflow is given in Figure Crawling Data This system is not an organization specific and requires data from various educational websites, research forums, news sites, blogs etc. The system needs both the historic and live data. Input data has to flow into the system from enormous digital resources that are readily available from the sources such as WWW. Since Expert Locator requires information about all

11 104 academicians and researchers, such kind of data has to be crawled into the system using web crawlers. Live Data from web, blog, news wires Crawl data and Collect information Conversion to common text format Concept Extraction Tokenizer Tagger Entity Recognizer Linking Ontology Document Faceted Browsing Facets Figure 5.3 Expert Locator Workflow Conversion to Common Format In order to identify the expertise of experts, data from many heterogeneous data sources need to be collected. This phase converts all the crawled data to text format. The generated new file is added to the data store Concept Extraction This phase identifies and extracts the concepts like name, address, skill, experience, publications, associations, colleagues, patents, awards,

12 105 panels / boards and other details. Figure 5.4 shows the processes involved in the extraction phase. The tokenization is performed using the OpenNLP parser. In this process tokens are extracted using regular expressions to recognize whitespaces, punctuation marks, hyphens, and numbers, among others. Tokenization sometimes faces challenges in finding the starting and end point of a token. This is because not all tokens are made of one word. For example, unked to be a single word and not two be a single word and not four tokens. GATE tool tagger which is rule based to predict the POS (part-ofspeech) of each word is employed. Some of the transformation rules used for tagging are: Converting a noun to a number (CD) if "." appears in the word Converting a noun to a past participle if ((string)words[i]) ends Converting any type to adverb if it ends in "ly"; The role of named entity recognition is to classify names into some particular categories from text by machine learning or statistical method. Support vector machine as the power tool in machine learning was widely used in the text categorization for named entity recognition (Xu-Dong Lin et al 2006). Named entity recognition is now firmly established as a key technology for understanding low level semantics of texts. Its main role is to identify expressions such as date and time as well as names of people, places, and organizations.

13 106 Text Tokenization POSTagging Named entity recognition (NER) Number Normalization Shallow parsing Semantic parsing Co-reference Concepts Figure 5.4 Processes involved in concept extraction Among the extracted tokens, relevant features are extracted. The frequency of the words is taken into account. All nouns and verbs are retained as concepts. Stopwords are eliminated. Thus this phase gives the concepts. These concepts have to be linked to the category they belong to, that is annotation of the extracted concepts have to be done Concept Linking The extracted concepts are then filtered and annotated. Only the necessary concepts are retained and each concept is linked to its category. In order to link to the correct category, GATE annotator is used.

14 107 To annotate the extracted entity, GATE is used. GATE requires Gazetteers and JAPE rules to be written. Gazetteers serve as database of known named entities. They are a kind of lists maintained for named entity recognition; 140 gazetteers, namely city, country, etc. are maintained and in addition rules to identify named entities which have patterns like address for instance the extraction module extracts the addresses available in the text by looking at the pattern in the text and then validates the pattern of the address are written. For recognizing entities that have no unique pattern, JAPE rules are written. JAPE rules make use of the gazetteers available. Figure 5.5 Sample main JAPE file Figure 5.5 shows a sample main JAPE file, which lists the gazetteers, and JAPE rule files available as a resource to annotate named entities in documents. Figure 5.6 Sample JAPE rule

15 108 Figure 5.6 shows a sample JAPE rule that helps annotating cities available in a document. The annotated concepts from all sources are stored in an ontology document, which is the input to the faceted browser. Figure 5.7 shows part of concepts and relations in the Expert Ontology document. In addition the faceted browser needs the list of facets and attributes to be displayed which are automatically generated from the ontology document. string int string int String String String paper Patent publication book int string int Journal projects Activity forum type String String String String string honors People Ph.D Domain String string Awards int Boards/ Panel researc h String Course String int string int int String int Property Sub class of Figure 5.7 Part of concepts and relations in ontology Faceted Browsing Faceted browsing is a technique for accessing a collection of information represented using a faceted classification, allowing users to explore by filtering available information.

16 109 A faceted classification system allows the assignment of multiple classifications to an object, enabling the classifications to be ordered in multiple ways, rather than in a single, pre-determined, taxonomic order. 5.6 RANKING EXPERTS It is important to rank all the experts, meeting the searching criteria in order to provide the users with relevant experts. It is necessary to differentiate experts based on relevance. To differentiate experts, relative ranking method is used. After storing all the annotated concepts from all sources in an ontology document, each parameter is given some weightage. The total weight (EW) is computed for each expert and a frequency table of weight for all experts meeting the search criteria, n is prepared. The statistical weights are arrived as given below. (5.1) (5.2) The rank to each expert meeting the search criteria is awarded distribution of weight as detailed in Table 5.1. In this ranking scheme, the ranks are awarded to the experts based on their total weightage relative to the others, with 1 as the highest rank and 6 as the lowest rank. Rank 0 is considered, as the expert is not qualified.

17 110 Table 5.1 Rank calculation using total weight Total Weight, EW secured by the expert Rank µ - 4 µ µ EW < µ EXPERIMENTAL RESULTS AND DISCUSSION Earlier studies on expert finding concentrate to mine the information from structured resume documents that matches the given job requirement. The most common method used for searching in a large set of document is keyword search, which will return all documents that contains the keyword (Sanjay Agrawal et al 2002). There are lots of keyword based search engines available that can rank the matched documents (Gjergji Kasneci et al 2008). Another approach is semantic based search engines. This approach can understand the document and will return all documents that are semantically related to the given query (Christoph Mangold 2007). This approach gives a more relevant result than the keyword search engines, still rs query, returns the candidate with word sales on their resume. This is incorrect. (Dan Crow & DeSanto 2005).

18 111 All the existing approaches use one or two parameters to identify to using one or two parameters. Expert Locator System using concept linking approach which allows users to gain an understanding of local expertise is built. M teaching subjects, department, research interest, context of their activities like publications as author and co-author, associations, patents and awards received, member in any board / panel, involvement in discussion forums, etc. are used to identify the expert. Since multiple parameters are used to identify expertise of experts, the accuracy of the proposed method will be high. The concept linking approach used in this chapter gives more relevant and accurate results than other approaches; because this approach extracts all concepts from historic and live data and linked all related concepts to the relevant category and stored as ontology document. For example, From the ontology, all facets (category) are generated and loaded for faceted browsing from which user can search for any category. Figure 5.8 shows the loaded facets. Figure 5.8 Screen shot showing the loaded facets

19 112 The proposed method is compared with some of the existing methods that does concept linking. POOLPARTY, SAS Text Miner, MALLET, LitLinker, AeroText are some of the products that do concept linking. All these existing systems are not domain specific and hence the accuracy is low. That is, the rule for text in a domain will not apply to another domain. That is, aiming to create a generic solution results in less accurate systems. Moreover, customizing the systems for a specific purpose is not available in many systems. Thus, it does not quench the thirst of text analytics solutions for any specific vertical. None of the available systems provide a database or gazetteer independent solution for Named Entity Recognition. Entity extraction accuracy is quite low, and does not work for unstructured text as they do not contain detailed description or a well formed text. The collected information is stored as text file. They are basically semi-structured and may not have full sentences to analyze the context. Since the expertise of experts are represented in ontology document, it is very easy 1) to find the detailed information about one expert by giving their name 2) to find the list of experts in the given field 3) to find persons related with one expert 4) to find the projects undertaken by one expert 5) to find the teaching experience and research experience in the field 6) to find the number of PhD scholars supervised by one expert. These types of simple queries can be answered very easily and quickly. SPARQL is used to query the ontology document. It is advantageous to represent expertise of expert in ontology. Since the major objective of this system is to retrieve experts, the ranking ability is also very important. In order to evaluate the system performance in the ranking accuracy, the relative ranking method is compared with the voting techniques (Macdonald & Ounis 2008) (a score aggregation technique the score of a

20 113 document is the sum of the normalized scores received by the document in each individual ranking) and with the baseline (where integrated information of each expert is considered as a single document). Table 5.2 gives the comparison of relative ranking technique with voting. It is found that the system performance is enhanced significantly by using relative ranking technique, both MAP and p@5 improved. This is because the rank is computed based on their weightage relative to the others and the weight of all parameters are all taken into account in the relative ranking approach. Table 5.2 Comparison of Relative Ranking Technique with Voting Evaluation Criteria MAP P@2 P@5 Voting Relative Ranking COMPARISON OF THE PROPOSED MODEL WITH EXISTING MODELS Commercially available solutions to expert finding have appeared in the marketplace. Mark T. Maybury (2006) has given an overview of some expert finding models. The proposed model is compared with the already existing expert finding tools in terms of sources used to collect information, technique to identify expertise and support given for searching. Table 5.3 shows the comparison of the proposed model with the existing models. All the existing systems use self declaration by the expert and web page content as the source to collect information. This information may be a biased one. Existing systems use ranking and voting techniques to identify the expertise of the experts. Many existing systems provide only keyword based searching facility for the users. The proposed model overcomes all these limitations. The proposed model uses publication repository and forum discussions apart

21 114 from the self declaration and web page content. Relative ranking is used to identify the expertise of the expert and to rank the experts. Apart from keyword search, faceted browsing and natural language support is also provided for searching. From Table 5.2, it is clear that relative ranking technique outperforms the voting technique. It can be inferred from Table 5.2 that the proposed model gives better result than the other models that use voting technique. Table 5.3 Comparison of the proposed model with existing models Models Sources used to collect information Techniques to Identify Expertise Support of Searching Self declaration Documents Web Pages Ranking Social Network Analysis Entity Extraction Keyword Natural Language Taxonomy Supports Foreign Language Self declaration Documents Web pages Ranking Keyword Self declaration Documents Web pages Entity Extraction Keyword Natural Language Taxonomy Supports Foreign Language Documents Web pages Ranking Keyword Supports Foreign Language Documents Ranking Social Net Analysis Ranking Social Network Analysis Keyword Natural Language

22 115 Table 5.3 (Continued) Models Sources used to collect information Technique to Identify Expertise Support of Searching Self declaration Documents Web pages Ranking Keyword Natural Language Taxonomy Proposed Model Self declaration Documents Web pages Publication Repository Personal Home Pages Discussion forums / news wires Behavior Concept Linking Entity Extraction Ranking Author Identification Keyword Natural Language Taxonomy Faceted Browsing 5.9 SUMMARY Expert finding is an important and challenging task. In this chapter an Expert Locator using concept linking which facilitates the finding of people with relevant expertise and/or experience for a given subject is proposed. Expert Locator extracts concepts from Web documents. Whenever anyone wants to find out the people who are all working in the same field for collaboration and/or for problem consulting, then this proposed method will be helpful for them to find them easily. Experiment results show that the proposed method gives a useful result with accuracies between 84% and 92%.