Knowledge Systems and Project Halo*

Knowledge Systems and Project Halo* University of Texas at Austin Ken Barker, Shaw Yi Chaw, James Fan, Bruce Porter, Dan Tecuci, Peter Yeh SRI International Vinay Chaudhri, David Israel, Sunil Mishra Boeing Phantom Works Peter Clark * Funding for various parts of this research was provided by DARPA, Vulcan, Inc, Boeing, and the US Army.

Knowledge Systems • Knowledge Systems use formal representations of knowledge to answer unanticipated questions with coherent explanations • Knowledge System = KB + Q/A + Explanation Generator + Knowledge Acq. tools

Advances over Expert Systems • Coverage of domain, not domain task • Various modes of reasoning, well integrated • Domain level explanation • Rapid construction U Just how advanced are they?

Project Halo* • Long term: build a Knowledge System encompassing much of the world’s scientific knowledge • Short term: assess current technologies • Use a portion of the Advance Placement (AP) chemistry exam as a metric * Full support for Project Halo was provided by Vulcan Inc, Seattle, WA

Challenges Systems must be robust in the face of widely varying, unanticipated questions. Explanations are as important as correctness. Hard-ball evaluation, aimed to expose weaknesses. New domain and short development time require using off-the-shelf KR&R methods and systems. It was not clear at the outset that these challenges could be met.

Example Questions • The spectator ions in the reaction of barium nitrate with sodium sulfate are what? (choices) • Although nitric acid and phosphoric acid have very different properties as pure substances, their aqueous solutions possess many common properties. List some general properties of these solutions and explain their common behavior in terms of the species present. • Explain why a solution of HClO4 and NaClO4 cannot act as a buffer solution. • Sodium azide is used in air bags to rapidly produce gas to inflate the bag. The products of the decomposition reaction are what? (choices) Questions were manually encoded in our formal language

Because Questions Vary Widely… … we can not anticipate the questions, or even the type of questions, so a retrieval method won’t do. A custom inference method won’t do. The system must be capable of using its knowledge in unanticipated ways.

An Example Explanation • What are the products of the given decomposition reaction? • By definition, oxidation-reduction reactions occur when electrons are transferred from the atom that is oxidized to the atom that is reduced. We need to look for changes in the oxidation states of the elements in the reaction. • In the reactants, the oxidation state(s) of the element Na is/are (1). In the product, the oxidation state(s) is/are (0) • Therefore, the reaction causes a change in oxidation state. • Therefore, this is an oxidation reduction reaction. • By definition, a Binary Ionic-Compound Decomposition Reaction occurs when a binary ionic compound is heated. • Therefore, this reaction is a Binary-Ionic Compound Decomposition reaction. • In general, a Binary Ionic-Compound Decomposition Reaction converts a binary ionic-compound into basic elements. • In this reaction, NaN3 reacts to produce Na and N2. • The products of the decomposition reaction are: (d) Sodium and nitrogen-g

Our KR&R System * • KM: KRL-like frame system with FOL semantics. • …able to represent: • classes, instances, prototypes • defaults, fluents, constraints • (hypothetical) situations • actions (pre-, post-, and during- conditions) • …and reason about: • inheritance with exceptions • constraints • automatic classification (given a partial description of an instance, determine the classes to which it belongs) • temporal projection (“my car is where I left it”) • effects of actions • KM answers questions by interleaving two types of inference: • Automatic classification • Backward chaining * Details: AAAI’97

What are the products of the given decomposition reaction? • By definition, oxidation-reduction reactions occur when electrons are transferred from the atom that is oxidized to the atom that is reduced. We need to look for changes in the oxidation states of the elements in the reaction. • In the reactants, the oxidation state(s) of the element Na is/are (1). In the product, the oxidation state(s) is/are (0) • Therefore, the reaction causes a change in oxidation state. • Therefore, this is an oxidation reduction reaction. • By definition, a Binary Ionic-Compound Decomposition Reaction occurs when a binary ionic compound is heated. • Therefore, this reaction is a Binary-Ionic Compound Decomposition reaction. • In general, a Binary Ionic-Compound Decomposition Reaction converts a binary ionic-compound into basic elements. • In this reaction, NaN3 reacts to produce Na and N2. • The products of the decomposition reaction are: (d) Sodium and nitrogen-g Oxidation Reduction of a Binary Ionic Compound (Sodium Azide)  the reaction classified as Binary-Ionic Compound Decomposition  Backward chaining to compute the products of such reactions

Structure of the Knowledge Base* Two principal types of chemistry knowledge: • terms, e.g. “binary ionic compound” • laws, e.g. problem-solving method for computing products of reactions of binary-ionic compounds Terms are encoded as definitions to enable automatic classification. Laws are encoded as rules to enable backward chaining. * Details: KR’04 (Barker, et.al.)

The conditions under which the law applies The subset of variables that must be bound The subset of variables that will be bound The axioms used to compute values for output variables The Content of a Chemistry Law Concentration of Solute Law Context: a mixture M such that: volume(M) = V liters has-part(M) includes Chemical C such that: quantity(C) = Q moles concentration(C) = Conc molar Input: V, Q Output: Conc Method: Conc ← Q/V

Knowledge Engineering Methodology Knowledge base built in 4 months: • Ontological engineering (4 person-months): designed representations, including structure of terms, laws, reactions, solutions, etc. • Knowledge capture (6 person-months): consolidated 70 textbook pages into 35 pages of terms and laws • Knowledge encoding (15 person-months): coded in KM 500 types and relations, 150 chemistry laws and 65 terms. Compiled a large test suite which was run daily • Explanation engineering (3 person-months): augmented the representations of terms and laws with templates

Results of Project Halo* • After 4 month development effort, the knowledge systems were sequestered and given a test: • 165 novel questions: 50 multiple choice; 115 free form response • Questions translated from English to formal language by each team, then assessed for fidelity by Vulcan and team representatives • * Details: AI Magazine (Winter 2004) • www.projecthalo.com : systems, Q/A, and analyses

Correctness • Our system’s correctness score corresponds to an AP score of 3 – high enough for credit at UCSD, UIUC, and many other universities. • We’ve predicted scoring 85% after a 3 month follow-on project.

Explanation Quality

Error Analysis * We analyzed every point lost. Most deductions were due to errors in domain modeling — mistakes that domain experts would not make. (More later) Some errors were caused by technology problems. * Details: KR’04 (Friedland, et.al.)

Problems Due to KR&R Technology • Explanations too verbose: e.g. passages repeated multiple times with only small variations – graders expected a general statement that covered them all. Requires explanation planning • Questions that require reasoning about our representations: • Calculate the pH of a particular substance. Explain why the result is unreasonable. • Explain the difference between the subscript “3” and the coefficient “3” in 3HNO3. • Explain when and why it’s OK for a particular chemistry method to use an equation that only approximates the true answer.

Reasoning about Relevance Hydrofluoric acid is a weak acid, Ka = 6.8 x 10-4, and yet it is considered to be a very reactive compound. For example, HF dissolves glass. The major reason it is considered highly reactive is: (a) It is an acid. (b) It forms H3O+. (c) It dissociates. (d) It readily forms very stable fluoride compounds. (e) It is a weak electrolyte. All five statements are true. The question requires that the system reason about which of the multiple true statements is most relevant to the claim.

Reasoning about Relevance An instructor handed you a bottle and indicated that the contents were one of the following: silver acetate, calcium acetate, and lead nitrate. The instructor asked you to identify the contents of the bottle. Using only solubility as a guide, indicate what chemicals you would use to test the unknown, and what the expected results would be for each of the possibilities. The question requires the system to choose some chemical to use in testing the unknown chemical. In order to avoid trial and error involving all known chemicals (which is actually what our system tried to do), you would need to reduce the search space to only those chemicals that are likely to be useful in the test.

Bottom Line • Halo I was a rigorous evaluation of current Knowledge System technology. • In general, the systems were more capable than Vulcan expected. • The major hurdles to building a Knowledge System for science are errors (in domain modeling) and cost ($10K/page).

Halo – Phase II • Knowledge Systems built by domain experts, not knowledge engineers • 3 test domains: chemistry, physics, biology • Questions posed by (a different set of) users (non-experts), in English • 2 teams, expanded with HCI experts It’s not clear that these challenges can be met.

Domain experts pose questions • in English • without knowing the ontology* scientific codes Controlled Language (Boeing) * Details: AAAI’04 Requirements Domain experts build knowledge systems • with no help from knowledge engineers • working with familiar concepts and without writing axioms • with little more effort than writing technical papers

(One part of) Our Approach • Observation: Even domain-specific concepts are defined in terms of common abstractions. • Approach: We build a library of reusable, common abstractions (“components”) and a set of relations for composing them together. Then, KB’s are built by instantiating and assembling them.

Component Library • Common events, entities, and roles • Currently about 800 components; projected to be about 3000 • Relatively general and atomic. Include enter, but probably not burgle, and definitely not endocytosis. • Every component richly axiomatized

Library Construction • draw from related work • ontology design/knowledge engineering • linguistics • semantic primitives • case theory, discourse analysis, NP semantics • draw from English lexical resources • dictionaries, thesauri, word lists • WordNet, Roget, LDOCE, COBUILD, etc.

Contents • actions — things that happen, change states • Enter, Copy, Replace, Transfer, etc. • states — relatively temporally stable events • Be-Closed, Be-Attached-To, Be-Confined, etc. • entities — things that are • Substance, Place, Object, etc. • roles — things that are, but only in the context of things that happen • Container, Catalyst, Barrier, Vehicle, etc.

Library Contents relations between events, entities, roles • agent, donor, object, recipient, result, etc. • content, part, material, possession, etc. • causes, defeats, enables, prevents, etc. • purpose, plays, etc. properties between events/entities and values • rate, frequency, intensity, direction, etc. • size, color, integrity, shape, etc.

Richly Axiomatized Components Knowledge about Enter • before the Enter, the object is outside some container • instances of Enter inherit axioms from Move, such as: the action changes the location of the object of the Move • during the Enter, the object passes through a portal of the container • if the portal has a covering, it must be open; and unless it is known to be closed, assume that it’s open • after the Enter, the object is inside that container and contained by it • the resulting Be-Contained state persists until the object Exits the container • etc.

Searching the Library(and expanding its coverage) components are linked to WordNet synsets • given a word, find similar components • climb the WordNet hypernym tree with search terms; rank the results by distance and specificity • assemble: Attach,Come-Togethermend: Repairinfiltrate: Enter,Traverse,Penetrate,Move-Intogum up: Block, Obstructbusted: Be-Broken,Be-Ruined

Using the Component Library for Knowledge Engineering* • First used for DARPA’s Rapid Knowledge Formation project • Enable biologists to encode college-level textbook knowledge about cells • Graphical editor (“SHAKEN”) of Concept Maps (“C-Maps”) • A small example: mRNA-Transport • “mRNA is transported out of the cell nucleus into the cytoplasm” • Transport: Move-Out-Of * Details: KCap’01, IAAI’02

unify

location

Using the Component Library for NL Understanding • “Understanding” evaluated by ability to answer questions and solve problems. • There’s a significant gap between the content of NL utterances and the knowledge required for reasoning. • Can the Component Library fill the gap?

My Dream Future • Although the Component Library was built for knowledge engineering, it will increasingly be used for other tasks, such as NL understanding and machine learning. • Knowledge Systems will be built by the distributed community of active scientists, as part of the computational infrastructure, to answer questions and organize/search digital libraries. Far more profound will be their contribution to scientific research and discovery.

Knowledge Systems and Project Halo*