Topcat Aircraft Database Process Full Text Resulted

I doubt X is a key player Sheer volume of patterns Need ability to focus on user needs Consequence for results: False associations Vague, dull associations.

May incorporate technologies such as concept extraction that ease integration with a Knowledge Discovery in Text system.

Data stored is usually semi-structured Traditional information retrieval techniques become inadequate for the increasingly vast amounts of text data Information retrieval A field developed in parallel with database systems Information is organized into (a large number of) documents Information retrieval problem: locating relevant documents based on user input, such as keywords or example documents.

This effect is captured through the assignment of numerical weights to each index term of a document.

DBMS Analogy Index Terms Attributes Weights Attribute Values.

Based Retrieval A document is represented by a string, which can be identified by a set of keywords Queries may use expressions of keywords E.

DBMS but not Oracle Queries and retrieval should consider synonyms, e.

Major difficulties of the model Synonymy: A keyword T does not appear anywhere in the document, even though the document is closely related to T, e.

Polysemy: The same keyword may mean different things in different contexts, e.

Basic techniques Stop list Set of words that are deemed irrelevant, even though they may appear frequently E.

The degree of similarity of the document d with regard to the query q is calculated as the correlation between the vectors that represent them, using measures such as the Euclidian distance or the cosine of the angle between these two vectors.

Project q into the term-document space: Dq q Uk Sk-1 Calculate similarities: cos Dq.

Evoke association mining algorithms Consider each document as a transaction View a set of keywords in the document as a set of items in the transaction Term level association mining No need for human effort in tagging documents The number of meaningless results and the execution time is greatly reduced.

Model-Based clustering (Neural Network Approach): clusters are represented by exemplars.

Cat identified separate topics for U.

S. embassy bombing and counterstrike.

METIS graph partitioning algorithm (based on (Han et.

Groups entities that may not appear together in a single broadcast, but are still closely related.

Topcat Aircraft Database Process Full Text Resulted

Full text resulted in almost 5 million unique worddocument pairs vs.

Prototype was unable to generate frequent itemsets at support thresholds lower than 2 for full text.

At 2 support, one week of full text data took 30 times longer to process than the named entities at 0.

For one week: 91 topics were generated with the full text, most of which arent readily identifiable.

Groups entities that may not appear together in a single broadcast, but are still closely related Authority Ramallah U.

N. West Bank Albright Arafat Gaza Netanyahu Iraq Israel Jerusalem State.

Topic clusters harder Topic may contain partial itemsets Solution: Information Retrieval Treat items as keys to search for Use Term FrequencyInter Document Frequency as distance metric between document and topic Multiple ways to interpret ranking Cutoff: Document matches a topic if distance within threshold Best match: Document only matches closest topic.

NODE working with NIMA as test site Incorporation in DARPA-sponsored TIDES Portal for Strong AngelRIMPAC exercise this summer.

Topcat Aircraft Database Update Process