The Market Blended Insight project1 has the objective of improving the UK business to business marketing performance using the semantic web technologies. In this project, we are implementing an ontology driven web extraction and translation framework to supplement our backend triple store of UK companies, people and geographical information. It deals with both the semi-structured data and the unstructured text on the web, to annotate and then translate the extracted data according to the backend schema

Darlington, John

Imtiaz, Hazzaz

Zuo, Landong

English

Southampton (e-Prints Soton)

1 The Market Blended Insight project (DTI Project No: TP/5/DAT/6/I/H0410D) is a three year applied research project funded under the UK Governments Technology Programme. 2 Web Ontology Language (OWL) http://www.w3.org/2004/OWL/ Ontology Driven Web Extraction from Semi-structured and Unstructured Data for B2B Market Analysis S.M. Hazzaz Imtiaz School of Electronics & Computer Science University of Southampton, UK. +44 (0)23 8059 5415 hsmi@ecs.soton.ac.uk John Darlington School of Electronics & Computer Science University of Southampton, UK. +44 (0)23 8059 9045 jd@@ecs.soton.ac.uk Landong Zuo School of Electronics & Computer Science University of Southampton, UK. +44 (0)23 8059 5415  lz@ecs.soton.ac.uk  ABSTRACT The  Market  Blended  Insight  project1  has  the  objective  of improving  the  UK  business  to  business  marketing  performance using  the  semantic  web  technologies.  In  this  project,  we  are implementing an ontology driven web extraction and translation framework  to  supplement  our  backend  triple  store  of  UK companies,  people  and  geographical  information.  It  deals  with both  the  semi-structured  data  and  the  unstructured  text  on  the web, to annotate and then translate the extracted data according to the backend schema. Categories and Subject Descriptors I.2.4  [Artificial  Intelligence]:  Knowledge  Representation Formalisms and Methods - Semantic networks. I.2.6 [Artificial Intelligence]: Learning - Knowledge acquisition. I.2.7  [Artificial  Intelligence]:  Natural  Language  Processing  - Language parsing and understanding, Text analysis. General Terms Algorithms, Languages. Keywords Semantic web, Ontology Driven Web Extraction, Market analysis. 1.  BACKGROUND AND RELATED WORK In today‟s competitive economic environment, it is important for a business to understand emerging trends, segment its market based on the functions, products and services of its prospective clients, scan beyond typical structured business data sources and interpret a  market  opportunity.  There  is  a  huge  amount  of  semi  and unstructured  text  relating  to  organizations  available  in  public domain  on  the  web.  The  organizations  typically  describe themselves in their websites using various attributes, behaviors, relationships  with  clients,  suppliers  etc.  The  opinions  of consumers and between organizations or the products that it wants  to market are stated in discussion forums, reviews, media or news  sites,  people  blogs  etc.  Understanding  these  information  about prospective  clients  as well as competitors, effectively and on a timely  basis is important for an organization to formulate their marketing strategy. The project has concentrated on developing an ontology  driven  web  information  extraction  system  which generates  semantic  content  for  the  topic  of  interest  to  the marketing user. It consists of domain specific wrappers generated by  hand,  named  entity  recognition  with  relevant grammars and gazetteers,  relation  extraction  from  both  semi  and  unstructured data  etc.  The  ontology  driven  web  extraction  provides obvious advantages such as exploiting the ontological data in class labels, synonyms for wider search, restrictions for verifying instances etc. The extraction is focused as we are only looking for interesting classes  and  relations  existing  in  that  ontology  and  not  other content. Finally the system interface to the search engines opens it to a much wider range of documents.  Several  approaches  have  been  proposed  for  extracting semantically  annotated  data  from  the  web.  McDowell  [1] proposed    an  ontology  driven  ,  domain  independent,  web extraction system named „OntoSyphon‟. It  takes any ontology as input,  uses  that  to  specify  web  searches  that  identify  possible semantic instances, relations, and taxonomic information. Yildiz [2] used ontology contents and predefined OWL2 semantics for the automatic extraction rule generation process. It uses words in the concept names and properties and populates their values by extracting  closely  preceding  or  following  values  matching  that particular  datatype.  Schutz  [3]  proposed  an  ontology  extension mechanism by relation extraction from text in the Football domain using  the  RelExt  tool.  It  identifies  triples  which  are  pairs  of concepts connected through a relation or verb and measure their relevance in terms of a highly ranked subject and a highly ranked direct or indirect object, which is then integrated into an existing ontology. Our approach is similar to that of McDowell in that we are ontology driven, automatic and only extract the instances of the  ontology  concepts  the  user  is  interested  in  and  similar  to Schutz in that we also extract relation triples. However we deal with both semi and un-structured text for knowledge extraction with some site specific wrappers and crawler configurations. We also  concentrate  on  a  set  of  organization  websites  to  mine  for relevant  facts  and  have  the  option  to  use  a  crawler,  count relevance and focus crawl the user‟s pages of interest. 2.  FOCUSED WEB CRAWLING We have used WebSphinx [4]web crawler and re-implemented the crawler to run in an ontology focused crawling mode similar to that of Ehrig [5]. The use of topical or focused crawling mode can Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not  made  or  distributed  for  profit  or  commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise,  or  republish,  to  post  on  servers  or  to  redistribute  to  lists, requires prior specific permission and/or a fee. Conference’04, Month 1–2, 2004, City, State, Country. Copyright 2004 ACM 1-58113-000-0/00/0004…$5.00.   1 GATE, A General Architecture For Text Engineering http://gate.ac.uk/   2 JAPE: Regular Expressions Over Annotations, http://gate.ac.uk/sale/tao/index.html#x1-1790007     3 WordNet, http://wordnet.princeton.edu/    4 Architect Journal Specification, http://www.ajspecification.com/    5 Barbour ABI, http://www.barbour-abi.com/xml_feed/rss_project_news_clickthrough_barbour-abi.htm 2 keep it limited to relevant topics of marketing user‟s interest. It takes in the basic crawling parameters being specified as a root webpage  or  several  root  web  pages,  depth  level,  regular expression link visit pattern, maximum number of pages, breadth or depth first crawl etc. Additionally for running in an ontology focused mode and computing the relevance based on it, it also takes  in  the  background  ontology  and  the  entities  we  are interested in that ontology. The documents are preprocessed with a GATE1 pipeline containing a morphological analyzer to get to the  word  roots,  relevant  gazetteers  and  JAPE2  grammars  to annotate  semantic  entities  contained  in  the  ontology.  The  page relevance is scored by counting the number of entities of interest, entities which are linked to these by the taxonomy or by relations in the ontology graph multiplied by different weight measures as described in [5].By limiting the crawl only to a specific website we can discover all the pages of interest in that website. From the relevant pages, for text mining, we can extend the ontology with instances as described in section 4. 3.  SEMI STRUCTURED DATA EXTRACTION WITH WRAPPERS The JAPE2 grammar is written as a set of phases where each phase may consist of one or more pattern/action rules with a priority ordering.  Having  a  sequential  phasing  means,  a  progressively complex annotation can be built at later stages, where annotations generated by the rules of previous stages are used in the rules at the later stages. For example: before we annotate a whole address, we  annotate  the  postcode,  county,  city,  street  name  and  house number,  using  a  combination  of  gazetteers and  jape  grammars. Similarly, extraction rule for a contact person may consist of his name and optionally his address, email, phone number etc. The CETF keeps a set of jape scripts as wrappers which covers standard HTML page structures such as table or list wrappers. The table header cells are mapped to that concept‟s attributes in the backbone ontology using a string distance measure such as the Levenshtein  Distance  algorithm  [6].If  its  below  a  threshold,  a WordNet3  based  semantic  similarity  metric  of  Lin  [7]  is employed. In case of nested tables, a concept may be linked to many other concepts through object properties and a column may contain concepts with attributes instead of just attributes of the main concept. For example: a table may describe the instances of the person concept with the columns: first name, surname, age, email, phone number etc. Therefore if the person concept in the backbone ontology has similar attributes, they will be mapped to it.  When  it  contains  address  of  the  person  in  a  column,  the extractor needs to call the appropriate wrapper to annotate address and get different address attributes such as postcode, city, street etc.  and  map  it  to  the  corresponding  object  property  in  the generated dataset. Figure 1 shows the inferred company roles and relationships, extracted from Architect Journal Specification4  and The Barbour ABI5 websites. There are plenty of free web directory services for organizations. Some list companies of a particular business such as Applegate directory for manufacturers, some are trade associations such as glass  and  glazing  federation,  some  are  local  council  company directories,  some  list  companies  by  their  roles  such  as  UK wholesalers etc. They usually list companies by some hierarchy   Figure 1. Company roles and relationships extracted from Ajspec2 and Barbour ABI3 website based  on  business  activity,  alphabetical  list  and  often  contain company pages reflecting that business details. Thus a crawler can exploit  these  rules  to  get  to  a  company  page  to  extract  the company  details  such  as  address,  important  contact  persons,  a textual description and also its business activity hierarchy. We can map  this  hierarchy  to  ours  and  create  a  finer  level  micro-segmentation of companies. For example a company listed just as a  restaurant  in  our  backbone  can  be  discovered  as  an  Italian restaurant  in  a  directory  website  hierarchy  or  in  the  textual description. Currently the CETF stores the crawler configuration for several directory websites and wrappers in the form of jape scripts  to  extract  company  information  such  as  contact  data, business activity and contact person details. The user can select the key concepts i.e. company, address, person etc. to extract from any of them. 4.  UNSTRUCTURED FREE TEXT EXTRACTION  To extract relational triples, the system takes as input the relations that we want to extract, their argument types and some of its seed instances  to  boot  strap  the  process.  We  consider  the  binary relationships of two entities. The well known „hyponymy‟ or is-a and  the  „meronymy‟  or  part-whole  binary  relations  can  be expressed using a small number of lexico-syntactic Hearst patterns [8].  Patterns like “NP0 such as {NP1, NP2 . . . . (and | or)} NPn“, where NP stands for noun phrase i.e. “Software vendors such as Microsoft  or  Oracle”,  indicate  hyponymy  relation.  We  have exploited these natural language patterns to build up our gazetteer of instances of desired types using the Google search API. The figure  2  shows  some  of  the  instances  extracted  for  the  search engine query “high value electronic items|goods|objects such as” which may be useful for a postal service provider building up an ontology of high value goods with instances, to extract from its prospective customers websites. Banko [9] has shown that by using a few syntactic patterns, most of the other binary relation patterns can be grouped into categories such as E1 verb E2, E1 verb preposition E2, E1 NP preposition E2 1The Stanford Parser: A statistical parser, http://nlp.stanford.edu/software/lex-parser.shtml   etc.  where  E1,  E2  indicate  semantic  categories  i.e.  PERSON established  COMPANY.  Important  business  facts  such  as company A acquiring company B, or company A merging with company B or Person X becoming CEO of company A etc. can be extracted mostly using the above patterns. For annotating these relations and be able to run inference on them, argument phrases   High Value Electronic GoodsInstances : ipods, Mobile Phones, Cameras, Laptops,  Computer Chips, Multimedia Devices, Flat Screen TVHigh Value GoodsHigh Value Cosmetic GoodsInstances: Cosmetics, Perfumes,  Clothing, Jewelry........... Figure 2. Instances of classes generated using Hearst patterns with Google search API are normalized which involves mapping them as objects to some known  concepts.  If  the  term  in  question  doesn‟t  belong  to  a semantic category i.e. hasn‟t matched with the gazetteer instances or with the grammar pattern, another mapping is attempted using a string distance measure Levenshtein [6] with a threshold value. If  it  is  a  multi-word  expression  such  as  a  noun  phrase,  the syntactic  head  of  the  noun  phrase  is  taken  into  account  and previous  steps  repeated.  Next,  relation  phrases  are  normalized, ignoring differences in verb root forms and trying to match it with the verbal expression of any existing relation between the classes of instances under consideration. If the relation doesn‟t match, the string distance matching as in the previous step is attempted. If it fails,  its  synonyms  are  checked  against  all  possible  relations between  the  classes.  If  it  is  a  multi-word  expression,  the  most representative word or the verb of that expression is taken into account  and  previous  steps  repeated.  To  identify  new  relations between entities as well as new entities, the process starts with a set of seed patterns [10] and each candidate pattern in the corpus is scored using some function and against these seed patterns. The high scoring patterns, above a threshold, are included in the set of seed  patterns.  However,  Stevenson  [10]  considered  pattern elements to belong to either a semantic category or a lexical item. But,  the  class  of  entities  the  project  has  considered  so  far  i.e. organization,  person  etc.,  it  would  not  be  accurate  to  judge similarity for lexical items. Thus only relations between classes of entities  are  considered  here.  The  WordNet  based  semantic similarity metric of Lin [7] is used for our purpose.  The  project  is  currently  working  to  use  dependency  analysis, where a sentence can be represented using a set of directed binary links between a head and its modifiers where the links are the relations such as subject, object etc. Using the dependency tree, one can use different information extraction models. Greenwood [11]  has provided a comparison of these models i.e. predicate-argument  structure,  chain,  linked  chain,  subtree  and  showed linked chains to be the most suitable for information extraction tasks  with  relatively  high  precision  and  recall.  We  can  use Stanford1  or  Minipar  [12]  parsers  to  obtain  the  dependency  or parse  trees  more  readily.    However,  it  would  require  more expensive  pipeline  processing  and  annotating  these  different patterns correctly would require additional work. Thus, this can be attempted only when we rank the document highly. 5.  CONCLUSION The project has implemented a wide range of B2B market search scenarios. To cover the client bases from insurance companies, banks, construction material supplier to postal delivery services, it has looked at heterogeneous data sources of semi or unstructured data,  covering  different  domains.  With  a  relatively  simple user interface, it has exposed high level parameters such as concepts to be  extracted,  extraction  schedule,  start  URL,  search  engine queries,  while  abstracting  the  lower  level  parameter  such  as crawler  configuration  for  directory  websites.  The  project  has brought  the  latest  advances  in  web  extraction  and  text  mining technology  to  timely  supplement  the  semantic  content  already held in backend triple stores and enabled the user to query and inference this data for his marketing campaign. It enables him to identify  and  target  the  desired  market  segment,  consisting  of organizations who have the required need and behavior for the propensity to buy his products. 6.  REFERENCES [1]  McDowell,  L.  K.  and  Cafarella,  M.  2006  Ontology-driven Information Extraction with OntoSyphon. [2] Burcu, M. S. Y. 2007. ontoX - A Method for Ontology-Driven Information  Extraction.  In  Computational  Science  and  Its Applications (ICCSA 2007), LNCS 4707, Springer-Verlag, 2007, S. 660 - 673. [3] Schutz, A. and Buitelaar, P. 2005. RelExt: A Tool for Relation Extraction from Text in Ontology Extension. [4] Miller, R. C. and Bharat, K. 1998. Sphinx: A framework for creating  personal,site-specic web crawlers. Computer Networks, 30(1-7):119-130. [5]  Ehrig,  M.  and  Maedche,  A.  Ontology-focused  Crawling  of Web Documents. [6] Levenshtein, V. 1965. Binary Codes Capable of Correcting Deletions,  Insertions,  and  Reversals.  Soviet  Physics  Doklady, Translated from Doklady Akademii Nauk SSSR. [7]  Lin,  D.  1998.  An  Information-Theoretic  Definition  of Similarity. In Proceedings of the 15th International Conference on Machine Learning. [8]  Hearst,  M.  1992.  Automatic  acquisition  of  hyponyms from large text corpora. In Procs. of the 14th International Conference on Computational Linguistics, pages 539–545. [9] Banko, M.  and Etzioni, O. The Tradeoffs Between Open and Traditional Relation Extraction.  [10]  Stevenson,  M.  and  Greenwood,  M.  A.  2006.  Learning Information Extraction Patterns using WordNet. In Proceedings of the  5th  Intl.  Conf.  on  Language  Resources  and  Evaluations, LREC 2006 22 - 28 May 2006, Vol. 2006 (2006), pp. 95-102.  [11]  Stevenson,  M.  and  Greenwood,  M.  A.  2007.  Comparing Information  Extraction  Pattern  Models.  In  Proceedings  of  the Information  Extraction  Beyond  The  DocumentWorkshop (COLING/ACL 2006), pages 12-19, Sydney, Australia. [12] Latat, D. L. 2001. Language and text analysis tools. 

An Information-Theoretic Definition of Similarity.

Automatic acquisition of hyponyms from large text corpora.

Binary Codes Capable of Correcting Deletions, Insertions, and Reversals. Soviet Physics Doklady, Translated from Doklady Akademii Nauk SSSR.

Comparing Information Extraction Pattern Models.

Language and text analysis tools.

Learning Information Extraction Patterns using WordNet.

Ontology-driven Information Extraction with OntoSyphon.

Ontology-focused Crawling of Web Documents.

ontoX - A Method for Ontology-Driven Information Extraction.

RelExt: A Tool for Relation Extraction from Text in Ontology Extension.

Sphinx: A framework for creating personal,site-specic web crawlers.

The Tradeoffs Between Open and Traditional Relation Extraction.

Ontology Driven Web Extraction from Semi-structured and Unstructured Data for B2B Market Analysis

Ontology Driven Web Extraction from Semi-structured and Unstructured Data for B2B Market Analysis

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