Knowledge acquisition using automated techniques
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The document discusses methods of automated knowledge extraction from web documents. It describes how knowledge extraction systems work by defining output structures, preprocessing input text, applying extraction methods, and generating output. The key steps involve identifying named entities, their relationships, and addressing issues like ambiguity and accuracy challenges. While automated extraction helps analyze vast online information, limitations regarding accuracy, efficiency and dependency on external resources exist. Combining automated techniques with human oversight may help improve knowledge extraction.
![References
[1] I. Tuomi. Data is more than knowledge:
implications of the reversed knowledge hierarchy
for knowledge management and organizational
memory. J. Manage. Inf. Syst. , 16(3):103–117, Dec.
1999.
[2] S. Sekine. Named Entity: History and Future. 2004.
[3] S. Sarawagi. Information extraction. Found. Trends
databases , 1(3):261–377, Mar. 2008.
[4] S. C. Weller. Cultural consensus theory:
Applications and frequently asked questions. Field
Methods,19(4):339–368, 2007.](https://image.slidesharecdn.com/knowledgeacquisition-ir-121029225733-phpapp02/85/Knowledge-acquisition-using-automated-techniques-42-320.jpg)
![References (contd.)
[5] Z. Syed, E. Viegas, and S. Parastatidis. Automatic
discovery of semantic relations using mindnet.
LREC,2010.
[6] G. A. Miller, R. Beckwith, C. Fellbaum, D. Gross, and
K. Miller. Wordnet: An on-line lexical database.
International Journal of Lexicography , 3:235–244,
1990
[7] T. S. Jayram, R. Krishnamurthy, S. Raghavan, S.
Vaithyanathan, and H. Zhu. Avatar information
extraction system. IEEE Data Eng. Bull. , pages 40–48,
2006.
[8] E. Greengrass. Information retrieval: A survey, 2000.](https://image.slidesharecdn.com/knowledgeacquisition-ir-121029225733-phpapp02/85/Knowledge-acquisition-using-automated-techniques-43-320.jpg)
Knowledge acquisition using automated techniques
- 1. Methods of Knowledge Extraction Deepti Aggarwal SIEL|SERL, IIIT-Hyderabad, India
- 2. Agenda Introduction to Web as a knowledge repository Automated extraction techniques (Input sources, extracted structures, input pre- processing, extraction methods, output generation) Issues with automated extraction
- 3. What is knowledge? A familiarity with someone or something with experience Includes facts, information, descriptions, skills
- 4. Types of Knowledge Explicit Knowledge Implicit Knowledge Always present Not present explicitly explicitly in records for analysis Objective facts having Cultural beliefs with a definite answer subjective judgments E.g., Hyderabad is the capital of A.P. E.g., Hyderabad is the best city to live in India.
- 5. How knowledge is represented over a period of time? From Public library to global library
- 6. How knowledge is represented over the web? Millions of documents, blogs, forums, social networks scattered on web Diverse topic, different formats, from diverse people in diverse language, different point of views
- 7. Benefits of knowledge extraction over the Web Question Answering systems Search engines Explicit Validating knowledge knowledge Tracking a particular information Predicting market, polls etc. Implicit Community advertisements knowledge
- 8. Problems with knowledge acquisition over web Abundance of data Relevance of information Personalized retrieval
- 9. Possible approaches Manual filtering Automated techniques Combination of both
- 11. Working of automated extraction systems Defining Input output pre- Extraction Output structures processing methods processing Input sources Database of all facts, Extraction system relations
- 12. Input sources Types
- 13. Input sources web documents news articles blogs social networks activities (user profiles, posts, comments) Sentence level parsing required.
- 14. Defining the structures of output Named Entities and their relations
- 15. Output structures Named Entities Named entities relations
- 16. 1. Named Entity: Definition It is an atomic element in a body of text. Types: person, organization, location etc. Different named entities when linked together, form a relation.
- 17. 1. Named Entity: An example Sachin Tendulkarwas born in Bombay. NE of type „Person‟ NE of type „Location‟
- 18. 2. Named Entity Relationship: Structure Subject – Relation - Object NE of any type NE of any type Verb, Adjective, Adverb
- 19. 2. Named Entity Relationship: An Example Sachin Tendulkar was born inBombay Subject Relation Object
- 20. Co-referencing Sachin was born in Bombay. He is a ... Sachin Tendulkar…. Mr. Tendulkar … Master Blaster...
- 21. Input pre-processing Libraries
- 22. NLP libraries: Splitting each sentence into tokens, words, digits using Sentence Tokenizer Recognizing language constructs, nouns, verbs, pronouns using Part-of-speech Tagger Example: Sachin/NNPTendulkar/NNP was/VBD born/VBN in/IN Bombay/NNP
- 23. NLP libraries (contd.): Linking individual constituents of a sentence with Parser to form parse tree Identify types of named entity using Named Entity Recognizer Example: Sachin Tendulkar/PERSON was born inBombay/LOCATION
- 24. NLP libraries (contd.): Identify all co-references and replace with actual entity using Co - reference Resolution tool Identify specific meaning of a word Word Sense Disambiguation External vocabularies: MindNet, DBpedia, WordNet E.g., contextual meaning of „crane‟: noun-bird, verb-lift/move
- 26. Extracting relationships among NEs: Standard process named entities within a 1. Identify sentence. verbor adjective that 2. Find the connects the identified named entities. 3. Connect them together to form relation.
- 27. Extracting relationships among NEs: Required process 1. Identifypart-of-speech constructs: noun, verb, adjective etc. Co-references, 2. Determine Acronyms and abbreviations. 3. Connect them together to form a relationship.
- 28. Extraction Methods Natural Language Processing: rule based. Based on sentence structure E.g., for English language, a rule can be “noun-verb-noun” Machine Learning: supervised and unsupervised learning. Features are detected from the training data E.g., to extract instances of some medical diseases, system is trained over all the symptoms of each given disease.
- 29. Extraction Methods (contd.) Other methods:Vocabulary based systems, context based clustering. Maintaining a mapping file of all countries and their nationalities helps to determine nationality of a person when his birth place is known. Hybrid: NLP based libraries to pre-process the input data, applying machine learning approach to extract the relations by using some external vocabulary as WordNet.
- 31. Types of output systems 1. Identifies all mentionsof named entities and their relations. E.g., from a given corpus, extract all named entity relations. 2. Identify missing relations of a database E.g., Given a database, extract the missing attributes of given entities from the corpus. 3. Linking various entities within a database. E.g., Given a database, link two entities together with some relation extracted from the corpus.
- 32. Working of automated extraction systems Defining Input output pre- Extraction Output structures processing methods processing Input sources Database of all facts, Extraction system relations
- 33. Issues with automated extraction Accuracy, running time, dependency
- 34. Issue 1: Challenges of language structure Co-reference resolution Ambiguous, complex sentences Abbreviations Acronyms
- 35. See an example… “Tomcalled his father last night. They talked for an hour. Hesaid hewould be home the next day." What is „He'referring to? Tomorhis father?
- 36. “You see sir, I can talk English, I can walk English, I can laugh English, I can run English, because English is such a funny language.” Amitabh in NamakHalal
- 37. Issue 2: Accuracy Named entity detection: 90%, relationship 50-70%. Introduction of noise at each step. E.g., disambiguation of acronym „crane‟ with WordNet, introduces contextual errors, which then decreases accuracy of rule based relationship extraction
- 38. Issue 3: Efficiency Feature detection steps are expensive. Require days for computation
- 39. Issue 4: Dependency on external vocabulary sources, like Wikipedia, WordNet, MindNetetc. Maintenance &updationof vocabulary sources is manual: costly and require expertise. Limited size produce context based noise Domain-dependent: medical domain Corpus-dependent: Wikipedia, news corpus Relation specific: Dateand Place-of- event
- 40. Issue 5: Problem with Implicit knowledge extraction Community Knowledge is learned and shared No one can be an expert. cultural competence and perception of workers are fed into a system as variables. Cultural Consensus Theory provides models to include such variables into the system.
- 41. Can we do better? Can we seek human intelligence to improve the accuracy of automated techniques?
- 42. References [1] I. Tuomi. Data is more than knowledge: implications of the reversed knowledge hierarchy for knowledge management and organizational memory. J. Manage. Inf. Syst. , 16(3):103–117, Dec. 1999. [2] S. Sekine. Named Entity: History and Future. 2004. [3] S. Sarawagi. Information extraction. Found. Trends databases , 1(3):261–377, Mar. 2008. [4] S. C. Weller. Cultural consensus theory: Applications and frequently asked questions. Field Methods,19(4):339–368, 2007.
- 43. References (contd.) [5] Z. Syed, E. Viegas, and S. Parastatidis. Automatic discovery of semantic relations using mindnet. LREC,2010. [6] G. A. Miller, R. Beckwith, C. Fellbaum, D. Gross, and K. Miller. Wordnet: An on-line lexical database. International Journal of Lexicography , 3:235–244, 1990 [7] T. S. Jayram, R. Krishnamurthy, S. Raghavan, S. Vaithyanathan, and H. Zhu. Avatar information extraction system. IEEE Data Eng. Bull. , pages 40–48, 2006. [8] E. Greengrass. Information retrieval: A survey, 2000.
- 44. Thank you Questions?
Editor's Notes
- #4: The definition of knowledge is a matter of on-going debate among philosophersbut for our talk I have taken this definition from wikipedia
- #8: Predicting market: to predict whether people likes Lux soap or not.community advertisements. Ex: Advertising Bengalis’ community in Hyderabad for a concert in Bengali.
- #9: Scarcity is not the issue but abundance is!Easy for humans to understand the meaning lying in different documents.Becomes difficult for a user to find a document of his interest.
- #10: Too much of labour, time consuming, biasedness, For huge data, an intelligent way is to formulate an algo which can perform repetitive computation. with systems instead of manual labour. Less time consuming, Which I will talk about in my ppt.I Consider it to be more appropriate. Combines the advantages of both systems and humans. Systems: scalability and accuracy and intelligence with humans. In my thesis, I have particularly opted for this approach. Today I am not talking about this approach. I will cover this topic in some later ppt.
- #11: Systems that are built over some algorithms: the use of methods for controlling industrial processes automatically, esp by electronically controlled systems, often reducing manpower
- #12: Broad overview of how system worksAccording to me these are five main components
- #33: Broad overview of how system worksAccording to me these are five main components
- #42: Type of extraction method depends on the applicationHighly sophisticated system can achieve max. of 70% accuracy. Accuracy of automated techniques can not surpass human intelligence.