Inductive Logic Programming in Databases: from Datalog to DL+log

In this paper we address an issue that has been brought to the attention of the database community with the advent of the Semantic Web, i.e. the issue of how ontologies (and semantics conveyed by them) can help solving typical database problems, through a better understanding of KR aspects related to databases. In particular, we investigate this issue from the ILP perspective by considering two database problems, (i) the definition of views and (ii) the definition of constraints, for a database whose schema is represented also by means of an ontology. Both can be reformulated as ILP problems and can benefit from the expressive and deductive power of the KR framework DL+log. We illustrate the application scenarios by means of examples. Keywords: Inductive Logic Programming, Relational Databases, Ontologies, Description Logics, Hybrid Knowledge Representation and Reasoning Systems. Note: To appear in Theory and Practice of Logic Programming (TPLP).Comment: 30 pages, 3 figures, 2 tables

Building Rules on Top of Ontologies for the Semantic Web with Inductive Logic Programming

Building rules on top of ontologies is the ultimate goal of the logical layer of the Semantic Web. To this aim an ad-hoc mark-up language for this layer is currently under discussion. It is intended to follow the tradition of hybrid knowledge representation and reasoning systems such as $\mathcal{AL}$-log that integrates the description logic $\mathcal{ALC}$ and the function-free Horn clausal language \textsc{Datalog}. In this paper we consider the problem of automating the acquisition of these rules for the Semantic Web. We propose a general framework for rule induction that adopts the methodological apparatus of Inductive Logic Programming and relies on the expressive and deductive power of $\mathcal{AL}$-log. The framework is valid whatever the scope of induction (description vs. prediction) is. Yet, for illustrative purposes, we also discuss an instantiation of the framework which aims at description and turns out to be useful in Ontology Refinement. Keywords: Inductive Logic Programming, Hybrid Knowledge Representation and Reasoning Systems, Ontologies, Semantic Web. Note: To appear in Theory and Practice of Logic Programming (TPLP)Comment: 30 pages, 6 figure

Semantic networks

AbstractA semantic network is a graph of the structure of meaning. This article introduces semantic network systems and their importance in Artificial Intelligence, followed by I. the early background; II. a summary of the basic ideas and issues including link types, frame systems, case relations, link valence, abstraction, inheritance hierarchies and logic extensions; and III. a survey of ‘world-structuring’ systems including ontologies, causal link models, continuous models, relevance, formal dictionaries, semantic primitives and intersecting inference hierarchies. Speed and practical implementation are briefly discussed. The conclusion argues for a synthesis of relational graph theory, graph-grammar theory and order theory based on semantic primitives and multiple intersecting inference hierarchies

Lahktarindid suulises eesti keeles: uurimus infostruktuuri raamistikus

Käesolev doktoriväitekiri „Initial and final detachments in spoken Estonian: a study in the framework of Information Structuring“ käsitleb lahktarindeid eesti suulises keeles infostruktuuri raamistikus. Lahktarinditena (inglise keeles detachments, dislocations) vaadeldakse siin konstruktsioone, milles leksikaalne element esineb kas enne pealauset või pärast seda, kusjuures pealauses esineb leksikaalse elemendiga samaviiteline asesõna. Neid konstruktsioone peetakse spontaanses keelekasutuses universaalseteks, kuid nende esinemus ja funktsioonid on keeleti mõnevõrra erinevad. Lahktarindeid ei ole eesti keeles varem uuritud; üldiselt on neid keeleuurimustes seotud infostruktuuri mõistetega nagu teema ja postreema, st nad tõstavad esile infostruktuuri tasandil leksikaalses üksuses ühelt poolt teema, mille kohta pealauses (reema) midagi öeldakse ning teiselt poolt esinevad nad struktuurides, kus pealause ehk reema on lausungis esimesel kohal, millele järgneb leksikaalse üksusena postreema. Et infostruktuuri tasandist lähtuvaid uurimusi eesti keele kohta tehtud pole, antakse väitekirja esimestes peatükkides ülevaade valdkonna problemaatikast ning käesolevas töös olulistest lähenemistest. Väitekiri sisaldab ka ülevaadet enamikust (süntaksit käsitlevatest) uurimustest eesti keele kohta, milles kasutatakse infostruktuuri mõisteid. Korpuseanalüüsis vaadeldakse lähemalt ülalmainitud kaht tüüpi lahktarindeid, keskendudes peamiselt referendi informatsioonilisele staatusele, tarindite erinevatele funktsioonidele ja toimimisele diskursuse tasandil. Väitekirja tulemusena võib lahktarindite kohta väita järgmist: tegemist on suulises kõnes levinud struktuuridega, mis toetavad suulisele kõnele iseloomulikku info edastamist lühemate üksuste kaupa. Tarindites viidatavad referendid on enamasti kas vestluses juba esinenud või on tuletatavad vestluse üldisest raamistikust. Postreemat sisaldavates tarindites, kus leksikaalne referent tuleb alles lausungi lõpus, esineb rohkem ka eelneva sisuga seostamata referente. Lisaks vormilistele erinevustele ning nendest tulenevalt on nende kahe struktuuri puhul selgelt näha erinevaid strateegiaid referendi sissetoomise puhul ning selle käsitlemisel edaspidises diskursuses.The present thesis “Initial and final detachments in spoken Estonian: a study in the framework of Information Structuring” analyses detachment constructions (initial and final detachments) in spoken Estonian, in the framework of Information Structuring. Initial detachments are defined as structures where a detached lexical element precedes the main clause, which contains a coreferential pronoun; in final detachments, the referent is at first referred to by a pronoun in the main clause, followed by the lexical element in a detached element at the end of the utterance. These structures have been considered universals in spontaneous oral language, but their functions and degree of grammaticalization differ in various languages. Detached constructions have not been investigated before in Estonian. They have been associated to the notions of Theme and Post-rheme at the level of Information Structuring, i.e. initial detachment introduces an element that is considered as Theme at the informational level and final detachment serves to introduce the Post-rheme. Given that there are no studies dedicated to the level of Information Structuring as independent object of investigation, an overview about the main problems of this domain is provided in first chapters of the thesis, followed by a summary of studies (in syntax) about Estonian where the notions of Information Structuring framework have been used. The analysis of the corpus concentrates mainly on the informational status of the referent, different functions of these constructions and their functioning at the discourse level. The analysis revealed that detached constructions allow the information to be staged during the discourse building work; as concerns the informational status of the referents, it could be shown that they have mostly been mentioned before during the conversation or are present in the thematic frame of the discourse. Final detachments are also used in order to introduce unmentioned and new referents. Besides the formal differences, and stemming from them, the two constructions are clearly different as concerns the mechanisms of introduction of the referent and its treatment in the discourse

Basic Communication Course Annual Vol. 8

(207 Pages, 7.696 MB

Cognition and Content

Os tópicos e problemas filosóficos discutidos no volume são de natureza bastante variada: a natureza da complexidade computacional no processamento de uma língua natural; a relação entre o significado linguístico e o sentido Fregeano; as conexões entre a a agência e o poder; o conteúdo semântico da ficção; a explicação dos impasses éticos; a natureza dos argumentos cépticos; as conexões entre as dissociações cognitivas e o carácter modular da mente; a relação entre a referência e o significado. Estes tópicos deixam-se subsumir num tema mais geral, o tema das ligações múltiplas entre a cognição e o conteúdo, mental ou linguístico. O tópico do conteúdo, a questão de determinar como é que muitas das nossas elocuções e muitos dos nossos estados mentais são dotados de conteúdo, representam algo (correcta ou incorrectamente), e o tópico da cognição, a investigação da natureza e dos mecanismos envolvidos na cognição humana, no processamento de informação tipicamente proveniente do exterior, são inegavelmente tópicos centrais da investigação filosófica, presente ou passada.Dado o carácter inclusivo e pluridisciplinar dos tópicos cobertos, não é surpreendente que os ramos da Filosofia representados no presente volume sejam igualmente diversificados: a Filosofia Moral, a Filosofia da Linguagem, a Teoria do Conhecimento, a Filosofia da Mente, os Fundamentos da Ciência Cognitiva. E também não é surpreendente que nele também estejam representados outros ramos do conhecimento cuja relevância para o estudo do conteúdo e da cognição é conspícua: a Psicologia Cognitiva; a Linguística Computacional e a Inteligência Artificial. Os autores dos ensaios são de proveniências diversas. De um lado, há um conjunto de especialistas nacionais a trabalhar na tradição analítica em Filosofia, como João Branquinho, ou a trabalhar em algumas áreas filosoficamente importantes da Ciência Cognitiva, como António Branco (Processamento da Língua Natural), José Frederico Marques (Psicologia Cognitiva) e Helder Coelho (Inteligência Artificial). Do outro lado, há um conjunto de especialistas estrangeiros de elevada reputação internacional: Allan Gibbard, da Universidade de Michigan, autor de livros influentes na área da Ética e Filosofia Moral; Stephen Schiffer, da New York University, uma das principais figuras actuais da Filosofia da Linguagem e da Semântica Geral; Paolo Leonardi, da Universidade de Bolonha, um especialista nas áreas da Filosofia da Linguagem e Filosofia da Comunicação; e Manuel García-Carpintero, da Universidade de Barcelona, um dos grandes vultos da Filosofia Analítica praticada em Espanha e na Europa Continental

Impact of Inquiry-Based, Question-and-Answer Instruction in High-Enrollment Classes

Lecturing is a common way to teach large classes, especially in mathematics. Other styles of instruction have been proven to be more effective in small classroom settings, but those styles are not always practical (or even feasible) in classes with 200+ students. The lecturing dialogue primarily exists at the intersection of Spectrum Theory and the Socratic Method, utilizing inquiry-based learning within the realm of active learning, experiential learning, and constructivism to appeal to learners with various learning styles, which helps students to discover new information (specifically, why things occur in the way that they do) and make connections between old and new material. Spectrum Theory outlines different teaching styles, based on how much a specific style is teacher-centered versus student-centered. The lecturing dialogue combines several of the different approaches from the Spectrum of Teaching Styles, specifically lecture, tutoring, instructional conversation, inquiry-based learning, and guided discovery. It utilizes the Socratic Method to turn students into active learners through constructivism and experiential learning. A high-enrollment, college algebra class using a standard lecture significantly outperformed a class using the lecturing dialogue on labs, surveys/questionnaires, and final grade, but not on final grades without the labs. The labs could be worked on outside of the class, so factors beyond the teaching style may have influenced the results. There was not significant difference between the classes in regards to attitude (enjoyment, motivation, value, and self-confidence). Overall, it appears as though the teaching style has no real impact on either student performance or student attitude in large college algebra classes

Massively parallel reasoning in transitive relationship hierarchies

This research focuses on building a parallel knowledge representation and reasoning system for the purpose of making progress in realizing human-like intelligence. To achieve human-like intelligence, it is necessary to model human reasoning processes by programs. Knowledge in the real world is huge in size, complex in structure, and is also constantly changing even in limited domains. Unfortunately, reasoning algorithms are very often intractable, which means that they are too slow for any practical applications. One technique to deal with this problem is to design special-purpose reasoners. Many past Al systems have worked rather nicely for limited problem sizes, but attempts to extend them to realistic subsets of world knowledge have led to difficulties. Even special purpose reasoners are not immune to this impasse. In this work, to overcome this problem, we are combining special purpose reasoners with massive We have developed and implemented a massively parallel transitive closure reasoner, called Hydra, that can dynamically assimilate any transitive, binary relation and efficiently answer queries using the transitive closure of all those relations. Within certain limitations, we achieve constant-time responses for transitive closure queries. Hydra can dynamically insert new concepts or new links into a. knowledge base for realistic problem sizes. To get near human-like reasoning capabilities requires the possibility of dynamic updates of the transitive relation hierarchies. Our incremental, massively parallel, update algorithms can achieve almost constant time updates of large knowledge bases. Hydra expands the boundaries of Knowledge Representation and Reasoning in a number of different directions: (1) Hydra improves the representational power of current systems. We have developed a set-based representation for class hierarchies that makes it easy to represent class hierarchies on arrays of processors. Furthermore, we have developed and implemented two methods for mapping this set-based representation onto the processor space of a Connection Machine. These two representations, the Grid Representation and the Double Strand Representation successively improve transitive closure reasoning in terms of speed and processor utilization. (2) Hydra allows fast rerieval and dynamic update of a large knowledge base. New fast update algorithms are formulated to dynamically insert new concepts or new relations into a knowledge base of thousands of nodes. (3) Hydra provides reasoning based on mixed hierarchical representations. We have designed representational tools and massively parallel reasoning algorithms to model reasoning in combined IS-A, Part-of, and Contained-in hierarchies. (4) Hydra\u27s reasoning facilities have been successfully applied to the Medical Entities Dictionary, a large medical vocabulary of Columbia Presbyterian Medical Center. As a result of (1) - (3), Hydra is more general than many current special-purpose reasoners, faster than currently existing general-purpose reasoners, and its knowledge base can be updated dynamically