Semantic Web Asking the Right Questions Wodzisaw Duch

Semantic Web: Asking the Right Questions Włodzisław Duch Department of Informatics, Nicolaus Copernicus University, Poland Google: W Duch Julian Szymański Dept. of Electronic, Telecommunication & Informatics, Gdańsk University of Technology, Poland

Plan Goal: Reaching human-level competence in all aspects of NLP. • • • Semantic Web – what and how? Semantic memories. The human way, or neurocognitive inspirations. Word games and dialogues for knowledge acquisition Search guided by semantic knowledge. Example: psychiatric diagnoses. • • • Words in the brain. Priming, brains and creativity. Taking heads and words games. Enhancing document representations. Medical applications.

Semantic Web: idea Words are ambiguous => Some information is difficult to find. Duch – a name? A nikname? A spirit? A ghost? Software agents can’t understand. I have a dream for the Web [in which computers] become capable of analyzing all the data on the Web – the content, links, and transactions between people and computers. A ‘Semantic Web’, which should make this possible, has yet to emerge, but when it does, the day-to-day mechanisms of trade, bureaucracy and our daily lives will be handled by machines talking to machines. The ‘intelligent agents’ people have touted for ages will finally materialize. Tim Berners-Lee, 1999 “The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. ” Tim Berners-Lee, James Hendler, Ora Lassila, The Semantic Web, Scientific American, May 2001

Semantic Web – how? Several roads leading to the Semantic Web: • Use Natural Language Processing to understand the meaning. • Use automatic clusterization techniques. • Use NLP to annotate the web. • Annotate the web in semi-automatic way. • Help to ask the right questions. First, create standards for metadata, define how to express knowledge. The Semantic Web is a major research initiative of the World Wide Web Consortium (W 3 C) to create a metadata-rich Web of resources that can describe themselves not only by how they should be displayed (HTML) or syntactically (XML), but also by the meaning of the metadata. W 3 C Semantic Web Activity Page

Semantic Web: standards Extend HTML => XML; B = Ontology languages built on top of RDF and RDF Schema B A • A = Ontology languages based on XML syntax • B = Ontology languages built on top of RDF and RDF Schema • Semantic Web: not much more than standards

SM Techno XML: customizable e. Xtensible Markup Language, additional tags XHTML: Extensible HTML XML Schema: is a language for providing and restricting the structure and content of elements contained within XML documents. RDF: Resource Description Framework, language for expressing data models, which refer to objects ("resources") and their relationships. An RDF-based model can be represented in XML syntax. RDF Schema: a vocabulary for describing properties and classes of RDF-based resources, with semantics for generalized-hierarchies of such properties and classes. DAML: DARPA Agent Markup Language OIL: Ontology Inference Layer OWL: more sophisticated vocabulary for describing properties and classes: relations between classes (e. g. disjointness), cardinality (e. g. "exactly one"), equality, richer typing of properties, characteristics of properties (e. g. symmetry), and enumerated classes. SPARQL: is a protocol and query language for semantic web data sources.

Linking-Open-Data Collect info from distributed sources, linking RDFs.

Knowledge: how? CYC, Douglas Lenat, started in 1984. Developed by Cy. Corp, with 2. 5 millions of assertions linking over 300. 000 concepts and using thousands of micro-theories (2007). Cyc-NL is still a “potential application”, knowledge representation in frames is quite complicated and thus difficult to use. Open Mind Common Sense Project (MIT): a WWW collaboration with over 14, 000 authors, who contributed 710, 000 sentences; used to generate Concept. Net, very large semantic network. Some interesting projects are being developed now around this network but no systematic knowledge has been collected. Other such projects: How. Net (Chinese Academy of Science), Frame. Net (Berkley), various large-scale ontologies. DBPedia - 1. 6 million concepts, 91 million RDF triples from Wikipedia

Semantic Memory Models Endel Tulving „Episodic and Semantic Memory” 1972. Semantic memory refers to the memory of meanings and understandings. It stores concept-based, generic, context-free knowledge. Permanent container for general knowledge (facts, ideas, words etc). Hierarchical Model Collins & Quillian, 1969 Semantic network Collins & Loftus, 1975

Semantic memory Hierarchical model of semantic memory (Collins and Quillian, 1969), followed by most ontologies. Connectionist spreading activation model (Collins and Loftus, 1975), with mostly lateral connections. Our implementation is based on connectionist model, uses relational database and object access layer API. The database stores three types of data: • concepts, or objects being described; • keywords (features of concepts extracted from data sources); • relations between them. IS-A relation us used to build ontology tree, serving for activation spreading, i. e. features inheritance down the ontology tree. Types of relations (like “x IS y”, or “x CAN DO y” etc. ) may be defined when input data is read from dictionaries and ontologies.

Creating SM The API serves as a data access layer providing logical operations between raw data and higher application layers. Data stored in the database is mapped into application objects and the API allows for retrieving specific concepts/keywords. Two major types of data sources for semantic memory: 1. machine-readable structured dictionaries directly convertible into semantic memory data structures; 2. blocks of text, definitions of concepts from dictionaries/encyclopedias. 3 machine-readable data sources are used: • • • The Suggested Upper Merged Ontology (SUMO) and the MId-Level Ontology (MILO), over 20, 000 terms and 60, 000 axioms. Word. Net lexicon, more than 200, 000 words-sense pairs. Concept. Net, concise knowledgebase with 200, 000 assertions.

Creating SM – free text Word. Net hypernymic (a kind of … ) IS-A relation + Hyponym and meronym relations between synsets (converted into concept/concept relations), combined with Concept. Net relation such as: Capable. Of, Property. Of, Part. Of, Made. Of. . . Relations added only if in both Wordnet and Conceptnet. Free-text data: Merriam-Webster, Word. Net and Tiscali. Whole word definitions are stored in SM linked to concepts. A set of most characteristic words from definitions of a given concept. For each concept definition, one set of words for each source dictionary is used, replaced with synset words, subset common to all 3 mapped back to synsets – these are most likely related to the initial concept. They were stored as a separate relation type. Articles and prepositions: removed using manually created stop-word list. Phrases were extracted using Apple. Pie. Parser + concept-phrase relations compared with concept-keyword, only phrases that matched keywords were used.

Semantic => vector reps Word w in the context: (w, Cont), distribution of brain activations. States (w, Cont) lexicographical meanings: clusterize (w, Cont) for all contexts, define prototypes (wk, Cont) for different meanings wk. Simplification: use spreading activation in semantic networks to define . How does the activation flow? Try this algorithm on collection of texts: • Perform text pre-processing steps: stemming, stop-list, spell-checking. . . • Use Meta. Map with a very restrictive settings to discover concepts, avoiding highly ambiguous results when mapping text to UMLS ontology. • Use UMLS relations to create first-order cosets (terms + all new terms from included relations); add only those types of relations that lead to improvement of classification results. • Reduce dimensionality of the first-order coset space, leave all original features; use feature ranking method for this reduction. • Repeat last two steps iteratively to create second- and higher-order enhanced spaces, first expanding, then shrinking the space. Create X vectors representing concepts.

RDF and semantic knowledge vw. CRK: certainty – truth – Concept Relation Keyword Similar to RDF in semantic web. Simplest rep. for massive evaluation/association: CDV – Concept Description Vectors, forming Semantic Matrix Cobra is_a is_a is_a has has has animal beast being brute creature fauna organism reptile serpent snake vertebrate belly body part cell chest costa

Visual representation Visualization of Wordnet, Wikipedia, ULMS and Cite. Seer, help to edit knowledge; available at http: //wordventure. eti. pg. gda. pl/

Visual representation Visualization of relations between Wordnet and Wikipedia concepts: http: //wordventure. eti. pg. gda. pl/

Relations • IS_A: specific features from more general objects. Inherited features with w from superior relations; v decreased by 10% + corrected during interaction with user. • Similar: defines objects which share features with each other; acquire new knowledge from similar objects through swapping of unknown features with given certainty factors. • Excludes: exchange some unknown features, but reverse the sign of w weights. • Entail: analogical to the logical implication, one feature automatically entails a few more features (connected via the entail relation). Atom of knowledge contains strength and the direction of relations between concepts and keywords coming from 3 components: • • • directly entered into the knowledge base; deduced using predefined relation types from stored information; obtained during system's interaction with the human user.

20 Q The goal of the 20 question game is to guess a concept that the opponent has in mind by asking appropriate questions. www. 20 q. net has a version that is now implemented in some toys! Based on concepts x question table T(C, Q) = usefulness of Q for C. Learns T(C, Q) values, increasing after successful games, decreasing after lost games. Guess: distance-based. SM does not assume fixed questions. Use of CDV admits only simplest form “Is it related to X? ”, or “Can it be associated with X? ”, where X = concept stored in the SM. Needs only to select a concept, not to build the whole question. Once the keyword has been selected it is possible to use the full power of semantic memory to analyze the type of relations and ask more sophisticated questions. How is the concept selected?

20 q for semantic data acquisition Play 20 questions with Avatar! http: //diodor. eti. pg. gda. pl Think about animal – system tries to guess it, asking no more than 20 questions that should be answered only with Yes or No. Given answers narrows the subspace of the most probable objects. System learns from the games – obtains new knowledge from interaction with the human users. Is it vertebrate? Y Is it mammal? Y Does it have hoof? Y Is it equine? N Is it bovine? N Does it have horn? N Does it have long neck? Y I guess it is giraffe.

20 Q web

Algorithm for 20 questions game p(keyword=vi) is fraction of concepts for which the keyword has value vi Subspace of candidate concepts O(A) is selected: O (A) = {i; d=|CDVi-ANS | is minimal } where CDVi is a vector for i-concept, ANS is a partial vector of retrieved answers we can deal with user mistakes choosing d > minimal

Automatic data acquisition Basic semantic data obtained from aggregation of machine readable dictionaries: Wordnet, Concept. Net, Sumo Ontology – Used relations for semantic category: animal – Semantic space truncated using word popularity rank: • • • IC – information content is an amount of appearances of the particular word in Word. Net descriptions. GR - Google. Rank is an amount of web pages returned by Google search engine for a given word. BNC - are the words statistics taken from British National Corpus. Initial semantic space reduced to 94 objects and 72 features

Human interaction & knowledge acquisition • Data obtained from machine readable dictionaries: – – Not complete Not Common Sense Sometimes specialized concepts Some errors • Knowledge correction in the semantic space: W 0 – initial weight, initial knowledge (from dictionaries) ANS – answer given by user N – number of answers β - parameter indicating importance of initial knowledge

Active Dialogues with the user for obtaining new knowledge/features: While system fails guess the object: I give up. Tell me what did you think of? The concepts used in the game corrects the semantic space While two concepts has the same CDV Tell me what is characteristic for <concept 1/2> ? The new keywords for specified concepts are stored in semantic memory While system needs more knowledge for same concept: I don’t have any particular knowledge about <concept>. Tell me more about <concept>. System obtains new keywords for a given concept.

Experiments in animal domain Word. Net, Concept. Net, Sumo. Milo ontology + Mind. Net project as knowledge sources; added to SM only if it appears in at least 2 sources. Basic space: 172 objects, 475 features, 5031 relations. # features/concept = CDV density. Initial CDV density = 29, adding IS_A relations =41, adding similar, entails, excludes=46. Quality Q = NS/N = #searches with success/# all searches. Error E = 1 -Q = 1 -NS/N. For 10 concepts selected with #features close to the average. Q~0. 8, after 5 repetition E ~ 18%, so some learning is needed.

Quality measures Initial semantic space: average # of games for correct recognition ~2. 8. This depends on the number of semantic neighbors close to this concept. Completeness of concept representation: • is CDV description sufficient to win the game? • how far is it from the golden standard (manually created)? 4 measures of the concept description quality: Sd = Nf(GS)–Nf(O) = #Golden Standard features - #features in O. how many features are still missing compared to the golden standard. SGS=Si [1–d(CDVi(GS), CDVi(O))] similarity based on co-occurrence. SNO = #features in O but not found in GS (reverse of SGS). Difw= Si [|CDVi(O)–CDVi(GS)|/m, average difference of O and GS.

Learning from games Select O randomly with preference for larger # features, p~exp(-N(O)/N)) N(O) = #features in O, and N = total number of features, Learning procedure: CDV(O) representation of the chosen concept O is inspected, and if necessary corrected. CDV(O) is removed from the memory. Try to learn the concept O by playing the 20 questions game. Average results for 5 test objects as a function of # games shown. NOr = SNO + SGS graph showing the average growth of the number of features as a function of the number of games played. Randomization of questions helps to find different features in each game. Average number of games to learn selected concepts Nf=2. 7. After the first successful game when a particular concept has been correctly recognized it was always found properly. After 4 games only a few new features are added.

DSM IV experiment Asks relevant questions, formulate the query precisely. DSM IV: Diagnostic and Statistical Manual of Mental Disorders, used by all psychiatrist as a reference text for diagnosis of mental disorders. Lists different categories of mental disorders and diagnostic criteria. 6 decision trees for diagnosis in specialized psychiatric domains. A search for mental problem is based on symptoms: ask only important questions, depending on what you already know! Compare results with diagnosis based on decision trees in DSM IV. Algorithm formulates questions using our 20 Q approach. Result: questions asked to increase information gain lead nearly 45% faster to the diagnosis then the sequential tests in most decision trees.

DSM IV experiment results Even in mistakes in answers for some classes of problems less questions were asked than with the standard decision tree approach.

Identify objects What is it? How to identify animals, flowers, cars and other natural objects using images? Let the search engine help you!

Few conclusions • • • Complex knowledge in frames is not too useful for large-scale search. Semantic search requires extensive knowledge. We do not have even the simplest common sense knowledge description, in many applications such representations are sufficient. It should be easier to generate this knowledge rather than wait for embodied systems. Semantic memory built from parsing dictionaries, encyclopedias, ontologies, results of collaborative projects. Active search is used to assign features found for concepts that are not far in ontology (for example, have same parents). Large-scale effort to create a numerical version of Wordnet for general applications is necessary, specialized knowledge is also important. Word games may help to create and correct some knowledge. 20 Q is easier than Turing text, good intermediate step. Time for word games Olympics!

Neurocognitive approach Why is NLP so hard? Only human brains are adapted to it. Ambitious approach: make an artificial brain! Computational cognitive neuroscience: aims at rather detailed neural models of cognitive functions, first annual CNN conf. Nov. 2005. Brain simulation with ~1010 neurons and ~1015 synapses (NSI San Diego), 1 sec = 50 days on a 27 processor Beowulf cluster. Neurocognitive informatics: focus on simplified models of higher cognitive functions: in case of NLP various types of associative memory: recognition, semantic and episodic. Many speculations, because we do not know the underlying brain processes, but models explaining most neuropsychological syndromes exist; computational psychiatry is rapidly developing since ~ 1995. “Roadmap to human level intelligence” – workshops ICANN’ 05, WCCI’ 06

Words in the brain Psycholinguistic experiments show that most likely categorical, phonological representations are used, not the acoustic input. Acoustic signal => phoneme => words => semantic concepts. Phonological processing precedes semantic by 90 ms (from N 200 ERPs). F. Pulvermuller (2003) The Neuroscience of Language. On Brain Circuits of Words and Serial Order. Cambridge University Press. Action-perception networks inferred from ERP and f. MRI Phonological neighborhood density = the number of words that are similar in sound to a target word. Similar = similar pattern of brain activations. Semantic neighborhood density = the number of words that are similar in meaning to a target word.

Brain areas involved Organization of the word recognition circuits in the left temporal lobe has been elucidated using f. MRI experiments (Cohen et al. 2004). How do words that we hear, see and thinking of activate the brain? Seeing words: orthography, phonology, articulation, semantics. Visual word form area (VWFA) in the left occipitotemporal sulcus is strictly unimodal visual area. Adjacent lateral inferotemporal multimodal area (LIMA) reacts to both auditory & visual stimulation, has cross-modal phonemic and lexical links. Likely: homolog of the VWFA in the auditory stream, the auditory word form area, located in the left anterior superior temporal sulcus; this area shows reduced activity in developmental dyslexics. Large variability in location of these regions in individual brains.

Insights and brains Activity of the brain while solving problems that required insight and that could be solved in schematic, sequential way has been investigated. E. M. Bowden, M. Jung-Beeman, J. Fleck, J. Kounios, „New approaches to demystifying insight”. Trends in Cognitive Science 2005. After solving a problem presented in a verbal way subjects indicated themselves whether they had an insight or not. An increased activity of the right hemisphere anterior superior temporal gyrus (RH-a. STG) was observed during initial solving efforts and insights. About 300 ms before insight a burst of gamma activity was observed, interpreted by the authors as „making connections across distantly related information during comprehension. . . that allow them to see connections that previously eluded them”.

Insight interpreted What really happens? My interpretation: • • LH-STG represents concepts, S=Start, F=final understanding, solving = transition, step by step, from S to F if no connection (transition) is found this leads to an impasse; RH-STG ‘sees’ LH activity on meta-level, clustering concepts into abstract categories (cosets, or constrained sets); connection between S to F is found in RH, leading to a feeling of vague understanding; gamma burst increases the activity of LH representations for S, F and intermediate configurations; stepwise transition between S and F is found; finding solution is rewarded by emotions during Aha! experience; they are necessary to increase plasticity and create permanent links.

Creativity What features of our brain/minds are most mysterious? Consciousness? Imagination? Intuition? Emotions, feelings? Higher mental functions? Masao Ito (director of RIKEN, neuroscientist) answered: creativity. Still domain of philosophers, educators and a few psychologists, for ex. Eysenck, Weisberg, or Sternberg (1999), who defined creativity as: “the capacity to create a solution that is both novel and appropriate”. MIT Encyclopedia of Cognitive Sciences has 1100 pages, 6 chapters about logics & over 100 references to logics in the index. Creativity: 1 page (+1 page about „creative person”). Intuition: 0, not even mentioned in the index. In everyday life we use intuition more often than logics. Unrestricted fantasy? Creativity may arise from higher-order schemes! Use templates for analytical thinking, J. Goldenberg & D. Mazursky, Creativity in Product Innovation, CUP 2002

Memory & creativity Creative brains accept more incoming stimuli from the surrounding environment (Carson 2003), with low levels of latent inhibition responsible for filtering stimuli that were irrelevant in the past. “Zen mind, beginners mind” (S. Suzuki) – learn to avoid habituation! Creative mind maintains complex representation of objects and situations. Pair-wise word association technique may be used to probe if a connection between different configurations representing concepts in the brain exists. A. Gruszka, E. Nęcka, Creativity Research Journal, 2002. Word 1 Priming 0, 2 s Word 2 Words may be close (easy) or distant (difficult) to connect; priming words may be helpful or neutral; helpful words are related semantically or phonologically (hogse for horse); neutral words may be nonsensical or just not related to the presented pair. Results for groups of people of low/high creativity are surprising …

Creativity & associations Hypothesis: creativity depends on the associative memory, ability to connect distant concepts together. Results: creativity is correlated with greater ability to associate words & susceptibility to priming, distal associations show longer latencies before decision is made. Neutral priming is strange! • for close words and nonsensical priming words creative people do worse than less creative; in all other cases they do better. • for distant words priming always increases the ability to find association, the effect is strongest for creative people. Latency times follow this strange patterns. Conclusions of the authors: More synaptic connections => better associations => higher creativity. But results for neutral priming are puzzling!

Paired associations So why neutral priming for close associations and nonsensical priming words degrades results of creative people? High creativity = many connections between microcircuits; nonsensical words add noise, increasing activity between many circuits; in a densely connected network adding noise creates confusion, the time need for decision is increased because the system has to settle in specific attractor. If creativity is low and associations distant, noise does not help, because there are no connections, and priming words contribute only to chaos. Nonsensical words increase overall activity in the intermediate configura-tions. For creative people resonance between distant microcircuits is possible: this is called stochastic resonance, observed in perception. For priming words with similar spelling and close words the activity of the second word representation is higher, always increasing the chance of connections and decreasing latency. For distant words it will not help, as intermediate configurations are not activated.

Words: simple model Goals: • make the simplest testable model of creativity; • create interesting novel words that capture some features of products; • understand new words that cannot be found in the dictionary. Model inspired by the putative brain processes when new words are being invented starting from some keywords priming auditory cortex. Phonemes (allophones) are resonances, ordered activation of phonemes will activate both known words as well as their combinations; context + inhibition in the winner-takes-most leaves only a few candidate words. Creativity = network+imagination (fluctuations)+filtering (competition) Imagination: chains of phonemes activate both word and non-word representations, depending on the strength of the synaptic connections. Filtering: based on associations, emotions, phonological/semantic density.

Generating novel words Approximations: associative neural networks, self-organizing networks, or statistical models capturing phono/morphology. Preliminary: • create probability models for linking phonemes and syllables; • create semantic and phonological distance measures for words. Statistical algorithm to find novel words: • Read initial pool of keywords. • Find phonological and semantic associations to increase the pool. • Break all words into chains of phonemes and chains of morphemes. • Find all combinations of fragments forming longer chunks ranked according to their phonological probability (estimating ngram plausibility). • For final ranking use estimation of semantic density around morphemes in the newly created words.

Words: experiments A real letter from a friend: I am looking for a word that would capture the following qualities: portal to new worlds of imagination and creativity, a place where visitors embark on a journey discovering their inner selves, awakening the Peter Pan within. A place where we can travel through time and space (from the origin to the future and back), so, its about time, about space, infinite possibilities. FAST!!! I need it soooooooooooon. creativital, creatival (creativity, portal), used in creatival. com creativery (creativity, discovery), creativery. com (strategy+creativity) discoverity = {disc, discover, verity} (discovery, creativity, verity) digventure ={dig, digital, venture, adventure, venue, nature} still new! imativity (imagination, creativity); infinitime (infinitive, time) infinition (infinitive, imagination), already a company name learnativity (taken, see http: //www. learnativity. com) portravel (portal, travel); sportal (space, sport, portal), taken quelion – lion of query systems! Web site timagination (time, imagination); timativity (time, creativity) tivery (time, discovery); trime (travel, time)

Word games were popular before computer games. They are essential to the development of analytical thinking. Until recently computers could not play such games. The 20 question game may be the next great challenge for AI, because it is more realistic than the unrestricted Turing test; a World Championship could involve human and software players. Finding most informative questions requires knowledge and creativity. Performance of various models of semantic memory and episodic memory may be tested in this game in a realistic, difficult application. Asking questions to understand precisely what the user has in mind is critical for search engines and many other applications. Creating large-scale semantic memory is a great challenge: ontologies, dictionaries (Wordnet), encyclopedias, Mind. Net (Microsoft), collaborative projects like Concept Net (MIT) …

Realistic goals? Different applications may require different knowledge representation. Start from the simplest knowledge representation for semantic memory. Find where such representation is sufficient, understand limitations. Drawing on such semantic memory an avatar may formulate and may answer many questions that would require exponentially large number of templates in AIML or other such language. Adding intelligence to avatars involves two major tasks: • building semantic memory model; • provide interface for natural communication. Goal: create 3 D human head model, with speech synthesis & recognition, use it to interact with Web pages & local programs: a Humanized In. Terface (HIT). Control HIT actions using the knowledge from its semantic memory.

Types of memory Neurocognitive approach to NLP: at least 4 types of memories. Long term (LTM): recognition, semantic, episodic + working memory. Input (text, speech) pre-processed using recognition memory model to correct spelling errors, expand acronyms etc. For dialogue/text understanding episodic memory models are needed. Working memory: an active subset of semantic/episodic memory. All 3 LTM are coupled mutually providing context for recogniton. Semantic memory is a permanent storage of conceptual data. • “Permanent”: data is collected throughout the whole lifetime of the system, old information is overridden/corrected by newer input. • “Conceptual”: contains semantic relations between words and uses them to create concept definitions.

SM & neural distances Activations of groups of neurons presented in activation space define similarity relations in geometrical model.

Similarity between concepts Left: MDS on vectors from neural network. Right: MDS on data from psychological experiments with perceived similarity between animals. Vector and probabilistic models are approximations to this process.

Concept Description Vectors Drastic simplification: for some applications SM is used in a more efficient way using vector-based knowledge representation. Merging all types of relations => the most general one: “x IS RELATED TO y”, defining vector (semantic) space. {Concept, relations} => Concept Description Vector, CDV. Binary vector, shows which properties are related or have sense for a given concept (not the same as context vector). Semantic memory => CDV matrix, very sparse, easy storage of large amounts of semantic data. Search engines: {keywords} => concept descriptions (Web pages). CDV enable efficient implementation of reversed queries: find a unique subsets of properties for a given concept or a class of concepts = concept higher in ontology. What are the unique features of a sparrow? Proteoglycan? Neutrino?

HIT the Web Haptek avatar as a plug-in in WWW browser. Connect to web pages, read their contents, send queries and read answers from specific fields in web forms. Access Q/A pages, like MIT Start, or Brainboost that answer reasonably to many questions. “The HAL Nursery”, “the world's first Child-Machine Nursery”, Ai Research www. a-i. com, is hosting a collection of “Virtual Children”, or HAL personalities developed by many users through conversation. HAL is using reinforcement learning techniques to acquire language, through trial and error process similar to that infants are using. A child head with child voice makes it much more interesting to play with. Haptek heads may work with many chatterbots, we focus on use of SM. Several word games with our head are here: http: //diodor. eti. pg. gda. pl/

Talking Head SM is the brain, HIT needs a talking head and voice interface. Haptek’s People. Putty tools have been used (inexpensive) to create a 3 -D talking head; only the simplest version is used. Haptek player is a plugin for Windows browsers, or embedded component in custom programs; both versions were used. High-fidelity natural voice synthesis with lips synchronization may be added to Haptek characters. Free MS Speech Engine, i. e. MS Speech API (SAPI 5) has been used to add text to speech synthesis and speech to text voice recognition. OGG prerecorded audio files may be played. Haptek movements, gestures, face expressions and animation sequences may be programmed and coordinated with speech using Java. Script, Visual Basic, Active-X Controls, C++, or Tool. Book. Result: HIT that can interact with web pages, listen and talk, sending information both ways, hiding the text pages from the user. Interaction with Web pages is based on Microsoft. NET framework.

HIT – larger view … T-T-S synthesis Behavioral models Affective computing Brain models Speech recognition HIT projects Talking heads AI Graphics VR avatars Learning Lingu-bots Info-retrieval Cognitive Architectures Robotics Cognitive science A-Minds Knowledge modeling Semantic memory Episodic Memory Working Memory

DREAM architecture Web/text/ databases interface NLP functions Natural input modules Cognitive functions Text to speech Behavior control Talking head Control of devices Affective functions Specialized agents DREAM is concentrated on the cognitive functions + real time control, we plan to adopt software from the HIT project for perception, NLP, and other functions.

Distance calculation Euclidean distance used for binary Yes/No answer, otherwise the distance ||K–A|| is: where |Ki–Ai| depends on the type of relation Ki and answer Ai: - if either Ki or Ai is Unknown then |Ki–Ai|=0. 5 - if either Ki or Ai is Not Applicable then |Ki–Ai|=1 -otherwise Ki and Ai are assigned numerical values: -Yes=1, Sometimes = 2/3, Seldom = 1/3, No = 0 CDV matrix for a single ontology reduced to animal kingdom was initially used to avoid storage size problems. The first few steps find keywords with IG≈1. CDV vectors are too sparse, with 5 -20, average 8, out of ~5000 keywords. In later stages IG is small, very few concepts eliminated. More information is needed in the semantic memory! Active dialogs.

Query Semantic memory Applications, eg. 20 questions game Store Humanized interface Part of speech tagger & phrase extractor verification Manual Parser On line dictionaries Active search and dialogues with users

Puzzle generator Semantic memory may be used to invent automatically a large number of word puzzles that the avatar presents. This application selects a random concept from all concepts in the memory and searches for a minimal set of features necessary to uniquely define it; if many subsets are sufficient for unique definition one of them is selected randomly. It is an Amphibian, it is orange and has black spots. How do you call this animal? A Salamander. It has charm, it has spin, and it has charge. What is it? If you do not know, ask Google! Quark page comes at the top …

Medical applications: goals & questions • Can we capture expert’s intuition evaluating document’s similarity, finding its category? • How to include a priori knowledge in document categorization – important especially for rare disease. • Provide unambiguous annotation of all concepts. • Acronyms/abbreviations expansion and disambiguation. • How to make inferences from the information in the text, assign values to concepts (true, possible, unlikely, false). • How to deal with the negative knowledge (not been found, not consistent with. . . ). • Automatic creation of medical billing codes from text. • Semantic search support, better specification of queries. • Question/answer system. • Integration of text analysis with molecular medicine. Provide support for billing, knowledge discovery, dialog systems.

Example of clinical summary discharges Jane is a 13 yo WF who presented with CF bronchopneumonia. She has noticed increasing cough, greenish sputum production, and fatique since prior to 12/8/03. She had 2 febrile epsiodes, but denied any nausea, vomiting, diarrhea, or change in appetite. Upon admission she had no history of diabetic or liver complications. Her FEV 1 was 73% 12/8 and she was treated with 2 z-paks, and on 12/29 FEV 1 was 72% at which time she was started on Cipro. She noted no clinical improvement and was admitted for a 2 week IV treatment of Tobramycin and Meropenem.

Unified Medical Language System (UMLS) semantic types “Virus” causes “Disease or Syndrome” semantic relation Ø Other relations: “interacts with”, “contains”, “consists of” , “result of”, “related to”, … Ø Other types: “Body location or region”, “Injury or Poisoning”, “Diagnostic procedure”, …

UMLS – Example (keyword: “virus”) Ø Metathesaurus: Concept: Virus, CUI: C 0042776, Semantic Type: Virus Definition (1 of 3): Group of minute infectious agents characterized by a lack of independent metabolism and by the ability to replicate only within living host cells; have capsid, may have DNA or RNA (not both). (CRISP Thesaurus) Synonyms: Virus, Vira Viridae Ø Semantic Network: "Virus" causes "Disease or Syndrome"

Summary discharge test data Clinical Data Reference Data Disease name No. of records Average size [bytes] Pneumonia 609 1451 23583 Asthma 865 1282 36720 Epilepsy 638 1598 19418 Anemia 544 2849 14282 UTI 298 1587 13430 JRA 41 1816 27024 Cystic fibrosis 283 1790 7958 Cerebral palsy 177 1597 35348 Otitis media 493 1420 32416 Gastroenteritis 586 1375 9906 JRA - Juvenile Rheumatoid Arthritis UTI - Urinary tract infection

Data processing/preparation MMTx – discovers UMLS concepts in text Reference Texts MMTx ULMS concepts /feature prototypes/ Filtering - focus on 26 semantic types. Features - UMLS concept IDs Clinical Documents MMTx UMLS concepts Final data Filtering using existing space

Semantic types used Values indicate the actual numbers of concepts found in: I – clinical texts II – reference texts

Data statistics General: • 10 classes • 4534 vectors • 807 features (out of 1097 found in reference texts) Baseline: • Majority: 19. 1% (asthma class) • Content based: 34. 6% (frequency of class name in text) Remarks: • Very sparse vectors • Feature values represent term frequency (tf) i. e. the number of occurrences of a particular concept in text

Model of similarity I Try to capture some intuitions combining evidence while scanning the text: 1. Initial distance between document D and the reference vectors Rk should be proportional to d 0 k = ||D – Rk|| 1/p(Ck) – 1 2. If a term i appears in Rk with frequency Rik > 0 but does not appear in D the distance d(D, Rk) should increase by ik = a 1 Rik 3. If a term i does not appear in Rk but it has non-zero frequency Di the distance d(D, Rk) should increase by ik = a 2 Di 4. If a term i appears with frequency Rik > Di > 0 in both vectors the distance d(D, Rk) should decrease by ik = -a 3 Di 5. If a term i appears with frequency 0 < Rik ≤ Di in both vectors the distance d(D, Rk) should decrease by ik = -a 4 Rik

Model of Similarity II Given the document D, a reference vector Rk and probability p(i|Ck) probability that the class of D is Ci should be proportional to: where ik depends on adaptive parameters a 1, …, a 4 which may be specific for each class. Linear programming technique can be used to estimate ai by maximizing similarity between documents and reference vectors: with the constrains: where k indicates the correct class.

Results M 0 M 1 M 2 M 3 M 4 M 5 k. NN 48. 9 50. 2 51. 0 51. 4 49. 5 SSV dec. tree 39. 5 40. 6 31. 0 39. 5 42. 3 MLP (300 neur. ) 66. 0 56. 5 60. 7 63. 2 72. 3 71. 0 SVM (Optimal C) 59. 3 (1. 0) 60. 4 (0. 1) 60. 9 (0. 1) 60. 5 (0. 1) 59. 8 60. 0 (0. 01) 10 Ref. vectors 71. 6 - 71. 4 71. 3 70. 7 10 -fold crossvalidation accuracies in % for different feature weightings. M 0: tf frequencies; M 1: binary data; 70. 1

Enhancing representations A priori knowledge is form of reference prototypes is not sufficient. Experts reading the text activate their semantic memory and add a lot of knowledge that is not explicitly present in the text. Semantic memory is difficult to create: co-occurrence statistics does not capture structural relations of real objects and features. Better approximation (not as good as SM): use ontologies adding parent concepts to those discovered in the text. Ex: IBD => [C 0021390] Inflammatory Bowel Diseases => -> [C 0341268] Disorder of small intestine -> [C 0012242] Digestive System Disorders -> [C 1290888] Inflammatory disorder of digestive tract -> [C 1334233] Intestinal Precancerous Condition -> [C 0851956] Gastrointestinal inflammatory disorders NEC -> [C 1285331] Inflammation of specific body organs -> [C 0021831] Intestinal Diseases -> [C 0178283] [X]Non-infective enteritis and colitis [C 0025677] Methotrexate (Pharmacologic Substance) => -> [C 0003191] Antirheumatic Agents -> [C 1534649] Analgesic/antipyretic/antirheumatic

Enhancing representations Experts reading the text activate their semantic memory and add a lot of knowledge that is not explicitly present in the text. Co-occurrence statistics does not capture structural relations of real objects and features, systematic knowledge is needed. An approximation (not as good as SM): use ontologies adding related concepts (use parent & other relations) to those discovered in the text. Ex: IBD => [C 0021390] Inflammatory Bowel Diseases => -> [C 0341268] Disorder of small intestine -> [C 0012242] Digestive System Disorders -> [C 1290888] Inflammatory disorder of digestive tract -> [C 1334233] Intestinal Precancerous Condition -> [C 0851956] Gastrointestinal inflammatory disorders NEC -> [C 1285331] Inflammation of specific body organs -> [C 0021831] Intestinal Diseases [C 0025677] Methotrexate (Pharmacologic Substance) => -> [C 0003191] Antirheumatic Agents -> [C 1534649] Analgesic/antipyretic/antirheumatic

Enhancing representations MDS for original data MDS on medical discharge summaries after two enhancement steps. . MDS on medical discharge summaries after two enhancement steps

Clusterization on enhanced data MDS mapping of 4534 documents divided in 10 classes, using cosine distances. 1. 2. 3. 4. Direct, binarized vectors. Enhanced by all semantic types, one step (parents only). Enhanced by selected semantic types, one step. Enhanced by selected semantic types, two steps.

Clusterization on enhanced data MDS mapping of 4534 documents divided in 10 classes, using cosine distances. 1. Initial representation, 807 features. 2. Enhanced by 26 selected semantic types, two steps, 2237 concepts with CC >0. 02 for at least one class. Two steps create feedback loops A B between concepts. Structure appears. . . is it interesting to experts? Are these specific subtypes (clinotypes)?

Searching for topics Discover topics, subclusters, more focused than general categories. Map text on the 2007 Me. SH (Medical Subject Headings) ontology, more precise than ULMS. Filter rare concepts (appearing in <1% docs) and very common concepts (>99% docs); remove documents with too few concepts (<1% of all) => smaller but better defined clusters. Leave only 26 semantic types. Ward’s clustering used, with silhouette measure of clustering quality. Only 3 classes: two classes that mix most strongly (Pneumonia and Otitis media), add the smallest class JRA. Initial filtering: 570 concepts with 1%<tf<99%, 1002 documents. Semantic (26 types): 224 concepts, 908 docs with >1% concepts. These 224 concepts have about 70. 000 ULMS relations, only 500 belong to the 26 semantic types. Enhancement: very restrictive, only ~25 most correlated added.

Results Start, iterations 2, 3 and 4 shown, 5 clinotypes may be distinguished.

More semantic relations Neurocognitive approach to language understanding: use recognition, semantic and episodic memory models, create graphs of consistent concepts for interpretation, use spreading activation and inhibition to simulate effect of semantic priming, annotate and disambiguate text. For medical texts ULMS has >2 M concepts, 15 M relations … developing a System for Unambiguous Concept Mapping in Medical Domain (with Matykiewicz, Pestian), and ontology for common reason (with Szymanski)

Graphs of consistent concepts General idea: when the text is read analyzed activation of semantic subnetwork is spread; new words automatically assume meanings that increases overall activation, or the consistency of interpretation. Many variants, all depend on quality of semantic network, some include explicit competition among network nodes. 1. Recognition of concepts associated with a given concept: 1. 1 look at collocations, and close co-occurrences, sort using average distance and # occurrences; 1. 2 accept if this is a ULMS concept; manually verify if not; 1. 3 determine fine semantic types, what states/adjectives can be applied. 2. Create semantic network: 2. 1 link all concepts, determine initial connection weights (non-symmetric); 2. 2 add states/possible adjectives to each node (yes/no/confirmed …).

GCC analysis After recognition of concepts and creation of semantic network: 3. Analyze text, create active subnetwork (episodic working memory) to make inferences, disambiguate, and interpret the text. 3. 1 find main unambiguous concepts, activate and spread their activations within semantic network; all linked concepts become partially active, depending on connection weights. 3. 2 Polysemous words, acronyms/abbreviations in expanded form, add to the overall activation; active subnetwork activates appropriate meanings stronger than other meaning, inhibition between competing interpretations decreases alternative meanings. 3. 3 Use grammatical parsing and hierarchical semantic types constraints (Optimality Theory) to infer the state of the concepts. 3. 4 Leave only nodes with activity above some threshold (activity decay). 4. Associate combinations of particular activations with billing codes etc.

More conclusions Neurocognitive NLP leads to interesting inspirations (Sydney Lamb, Rice Univ, quite general book). Creation of novel interesting words is possible at the human competence level, opening a new vista in creativity research and suggesting new experiments. Specific (drastically simplified) representation of semantic knowledge is sufficient in word games and query precisiation applications. Various approximations to knowledge representation in brain networks should be studied: from the use of a priori knowledge based on reference vectors, through ontology-based enhancements, to graphs of consistent concepts in spreading activation networks. More work on semantic memory for common sense and specialized applications is needed. Sessions on Medical Text Analysis and billing annotation challenge, April 1 -5, 2007, IEEE CIDM, Honolulu, Hilton Hawaiian Village Hotel.

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