Combining Information from Multiple Internet Sources

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Table query_data: In fact whole knowledge base is stored here. This table contains a search engine ranking based on previous system inputs. Columns: • id – Primary Key • query – Query that was input by user • search_engine_id – (Foreign key) Specifies which search engine yielded this result • weight – A ranking parameter – higher the better results the search engine provided • algorithm_id – (Foreign key) Specified which algorithm was used to yield this specific result • query_count – Number of updates on the specific configuration (algorithm, SE, query); it is used to average weights that are submitted on the specific configuration Listing 2.2.3 Query_data table description 20
3. Algorithms This part of the thesis provides the detailed description of the three information combination approaches. This chapter also describes any helper routines that are used by those approaches. Each of the main algorithms for obtaining the final answer has its pseudo code included as well as the activity diagrams. 3.1 Game theory method This sub-chapter presents the Game theory method. This algorithm was used before in the NeurAge system [9] and had to be adapted to suit the purpose of combination of the data retrieved from the Internet. In its original form agents were supposed to vote for certain classes of data; here, they are voting for certain URLs. The confidence values from the original algorithm have been replaced by the URL ranks according to the algorithm described in section 3.4.1. Also, in its original form agents were yielding one class as the final answer. In the adapted form agents are returning 10 URLs in sequence, where any next iteration starts the whole process from the beginning, however without processing the URL which was already selected. This does not violate the main assumptions of the algorithm and this was stated by Edyta Szymańska of Emory University, Atlanta by means of personal communication. In general, a game is defined as follows: it consists of set of players, set of moves (strategies) and specifications of payoffs for each combination of moves. In case of algorithm which will be presented in further section game is a normal form game that is defined as follows: There is a finite set P of players, which we label { ,2,..., m} 1 . Each player k has finite number of pure strategies (moves) S k = { 1, 2, ..., n k }. A pure strategy profile is an association of strategies to players, that is m-tuple r σ = ( σ , 1 σ 2 ,..., σ m ) such that σ 1 ∈ S1, σ 2 ∈ S 2 ,..., σ m ∈ S m Let strategy profiles be denoted by Σ A payoff function is a function F : Σ → R whose intended representation is the award given to a single player at the outcome of the game. Accordingly to specify a game the payoff function has to be specified for each player in P = 1,2,..., m . the player set { } Definition. A game in normal form is a structure ( P , S, F ) Where P = { 1,2,..., m} is a set of players, S ( S , 1 S 2 ,..., S m ) sets, one for each player and F ( F F ,..., ) = is a m-tuple of pure strategy = , 1 2 F m is a m-tuple of payoff functions. Listing 3.1.1 Definition of the normal form game 21
Page 1 and 2: WARSAW UNIVERSITY OF TECHNOLOGY FAC
Page 3 and 4: POLITECHNIKA WARSZAWSKA WYDZIAŁ MA
Page 5 and 6: Combining Information from Multiple
Page 7 and 8: Table of contents 1. Introduction..
Page 9 and 10: application presented in this paper
Page 11 and 12: 2. Design of the tool used for test
Page 13 and 14: 2.2.1 Design of the Client module 2
Page 15 and 16: module. At the end of this section
Page 17 and 18: there are set up search engines. Af
Page 19 and 20: UTILITY CLASSES: AgentController -
Page 21 and 22: ) If initial step of the algorithm
Page 23 and 24: 2.3 Design of the database part Thi
Page 25: Table search_engine: This table con
Page 29 and 30: Example: Let us consider the follow
Page 31 and 32: During algorithm processing there i
Page 33 and 34: Fig 3.1.1 Game theory Method Activi
Page 35 and 36: Example: Consider the following ini
Page 37 and 38: Listing 3.2.3 presents the pseudo c
Page 39 and 40: 3.3 Consensus method The Consensus
Page 41 and 42: Depending on the outcome of the con
Page 43 and 44: 3.4 Common algorithms This part of
Page 45 and 46: is multiplied by 0.01. 3 If the alg
Page 47 and 48: Input: Two lists with URLs Output:
Page 49 and 50: 4.1 Tests with simple query Followi
Page 51 and 52: top enough positions in all result
Page 53 and 54: The following section presents resu
Page 55 and 56: This part will present comparison o
Page 57 and 58: As the first URL MySpiders returns
Page 59 and 60: was yield. However most of the retu
Page 61 and 62: The following part presents compari
Page 63 and 64: The following part presents compari
Page 65 and 66: complexity of the query. As the fir
Page 67 and 68: 7 http://www.calresco.org/lucas/pmo
Page 69 and 70: 5 6 http://www.virtualschool.edu/mo
Page 71 and 72: 10 http://www.mises.org/story/2451
Page 73 and 74: Auction Consensus Game theory Aucti
Page 75 and 76: 5. Final remarks In this thesis app
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[15] Kessler R.R., Griss M.L., Maki
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Warszawa, dnia 21.04.2008r. Oświad
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