Hypotheses

1. Hypotheses

2. What is a Hypothesis? A hypothesis (from Greek ὑπόθεσις) is a suggested explanation of a phenomenon or reasoned proposal suggesting a possible correlation between multiple phenomena. The term derives from the ancient Greek, hypotithenai meaning "to put under" or "to suppose". The scientific method requires that one can test a scientific hypothesis. Scientists generally base such hypotheses on previous observations or on extensions of scientific theories. - Wikipedia

3. Characteristics of Good Hypotheses • Statement of belief, a claim, a proposition • Clear, precise, unambiguous • Testable • Sufficient as the basis for experimentation • Repeatable by others who may care

4. Ontology-Based Constraint Recognition of Free-Form Requests I want to see a dermatologist between the 5th and the 10th, at 1:00 PM or after. The dermatologist should be within 5 miles from my home and must accept my IHC insurance. Appointment(x0) is with Dermatologist(x1) /\ Appointment(x0) is for Person(x2) /\ Dermatologist(x0) has Name(x3) /\ Person(x2) has Name(x4) /\ Appointment(x0) is on Date(x5) /\ DateBetween(x5, “the 5th”, “the 10th”) /\Appointment(x0) is at Time(x6) /\ TimeAtOrAfter(x6, “1:00 PM) /\ Dermatologist(x1) is at Address(x7) /\Person(x2) is at Address(x8) /\ DistanceLessThanOrEqual(DistanceBetweenAddresses(x7, x8), “5”) /\ Dermatologist(x1) accepts Insurance(x9) /\ InsuranceEqual(x9, “IHC”)

5. Ontology-Based Constraint Recognition of Free-Form Requests • Ontology-based, information-extraction techniques can translate free-form requests to database queries well. • Using ontology-based, information-extraction techniques, we can establish predicates with a recall of 90% or better and arguments with a precision of 95% or better. • Our ontology-based logic form generation system preforms better than other logic form generation systems, which achieve a recall within the interval [78%, 90%] and a precision within [81%, 87%] for predicate generation and achieve a recall within [65%, 77%] and a precision within [72%, 77%] for argument generation.

6. Hypothesis Refinement • Refinement: adding qualifying statements and more precise measures • Your initial hypothesis may be wrong—then what? • Refining may expose that it is not worth pursuing • Restrictions too complex • Necessary to assume current insoluble problems must first be solved • Refined scope and conditions may still be interesting (“Much of scientific progress can be viewed as refinement and development of hypotheses to fit new observations.”) • Occam’s razor • Simplicity in scientific theories—embrace the less complicated hypothesis • A hypothesis should make as few assumptions as possible—remove those that make no difference

7. Table Interpretation bySibling-Page Comparison Values Labels

8. Table Interpretation bySibling-Page Comparison • Sibling-Page Comparison techniques yield good table interpretations. • When sibling pages exist (e.g., hidden-web pages from a single site), sibling-page comparison techniques are 90% accurate. • For sibling tables in the z gold standard of ground truth for web table-interpretation, sibling-table comparison techniques perform significantly better (p  .05) than either the well known x-technique or y-technique for table interpretation.

9. Thesis Statement (from the grad handbook) A clear and concise statement of what is to be demonstrated or developed in your thesis work. A good thesis statement makes a specific claim that your readers care about. Ideally, your introduction will give your readers the background they need to understand your thesis statement and to conclude that it matters. Estimated length is 1 to 2 sentences. Thesis statement vs. hypothesis? Science hypothesis vs. engineering proposal?

10. “Science” Thesis Statements (from the grad handbook) • A dynamic dead variable analysis will reduce the size of the hash table in explicit model checking by marking more variables dead compared to static dead variable analysis. • Model checking with magnetic disk requires less time when using a chained partitioned hash table than when using an open address monolithic hash table because delayed duplicate detection time is reduced. • Given a set of precepts P and an associated training set Tr, a precept-driven learning algorithm (PDLA) can be designed which produces better results in generalization than when Tr alone is used. • When trunk type, network traffic type, and customer characteristics are considered in setting overbooking factors, bandwidth can be used more efficiently and utilization can be predicted more accurately.

11. “Engineering” Thesis Statements • A tool can be built that will take a mini-ontology and a growing ontology as input and make it possible to produce manually, semi-automatically, and automatically an extended growing ontology as output. Characteristics of this tool include: (1) a graphical, interactive user interface with features that will allow users to map and merge ontologies, and (2) a framework supporting pluggable, semi-automatic and automatic mapping and merging algorithms. • To interpret and correctly label machine-readable genealogical data and place it in a fully-searchable format, a specialized ontology can be built specifically for information extraction of primary genealogical records, with expert logic and rules to correctly extract information and to group individuals into families.