The role of market knowledge in recognizing and exploiting entrepreneurial opportunities in technology intensive firms

Entrepreneurial opportunity recognition is an increasingly prevalent phenomenon. Of particular interest is the ability of promising technology based ventures to recognize and exploit opportunities. Recent research drawing on the Austrian economic theory emphasizes the importance of knowledge, particularly market knowledge, behind opportunity recognition. While insightful, this research has tended to overlook those interrelationships that exist between different types of knowledge (technology and market knowledge) as well as between a firm’s knowledge base and its entrepreneurial orientation. Additional shortfalls of prior research include the ambiguous definitions provided for entrepreneurial opportunities, oversight of opportunity exploitation with an extensive focus on opportunity recognition only, and the lack of quantitative, empirical evidence on entrepreneurial opportunity recognition. In this dissertation, these research gaps are addressed by integrating Schumpeterian opportunity development view with a Kirznerian opportunity discovery theory as well as insights from literature on entrepreneurial orientation. A sample of 85 new biotechnology ventures from the United States, Finland, and Sweden was analyzed. While leaders in all 85 companies were interviewed for the research in 2003-2004, 42 firms provided data in 2007. Data was analyzed using regression analysis. The results show the value and importance of early market knowledge and technology knowledge as well as an entrepreneurial company posture for subsequent opportunity recognition. The highest numbers of new opportunities are recognized in firms where high levels of market knowledge are combined with high levels of technology knowledge (measured with a number of patents). A firm’s entrepreneurial orientation also enhances its opportunity recognition. Furthermore, the results show that new ventures with more market knowledge are able to gather more equity investments, license out more technologies, and achieve higher sales than new ventures with lower levels of market knowledge. Overall, the findings of this dissertation help further our understanding of the sources of entrepreneurial opportunities, and should encourage further research in this area.

Probabilistic Models for Scalable Knowledge Graph Construction

In the past decade, systems that extract information from millions of Internet documents have become commonplace. Knowledge graphs -- structured knowledge bases that describe entities, their attributes and the relationships between them -- are a powerful tool for understanding and organizing this vast amount of information. However, a significant obstacle to knowledge graph construction is the unreliability of the extracted information, due to noise and ambiguity in the underlying data or errors made by the extraction system and the complexity of reasoning about the dependencies between these noisy extractions. My dissertation addresses these challenges by exploiting the interdependencies between facts to improve the quality of the knowledge graph in a scalable framework. I introduce a new approach called knowledge graph identification (KGI), which resolves the entities, attributes and relationships in the knowledge graph by incorporating uncertain extractions from multiple sources, entity co-references, and ontological constraints. I define a probability distribution over possible knowledge graphs and infer the most probable knowledge graph using a combination of probabilistic and logical reasoning. Such probabilistic models are frequently dismissed due to scalability concerns, but my implementation of KGI maintains tractable performance on large problems through the use of hinge-loss Markov random fields, which have a convex inference objective. This allows the inference of large knowledge graphs using 4M facts and 20M ground constraints in 2 hours. To further scale the solution, I develop a distributed approach to the KGI problem which runs in parallel across multiple machines, reducing inference time by 90%. Finally, I extend my model to the streaming setting, where a knowledge graph is continuously updated by incorporating newly extracted facts. I devise a general approach for approximately updating inference in convex probabilistic models, and quantify the approximation error by defining and bounding inference regret for online models. Together, my work retains the attractive features of probabilistic models while providing the scalability necessary for large-scale knowledge graph construction. These models have been applied on a number of real-world knowledge graph projects, including the NELL project at Carnegie Mellon and the Google Knowledge Graph.

25 years of knowledge creation processes in pharmaceutical contemporary trends

The pharmaceutical industry is knowledge and research-intensive. Due to technological, socio-political and organisational changes there has been a continuous evolution in the knowledge base utilized to achieve and maintain competitive advantages in this global industry. There is a gap in analysing the linkages and effects of those changes on knowledge creation processes associated with pharmaceutical R&D activities. Our paper looks to fill this gap. We built on an idiosyncratic research approach – the systematic literature review – and looked to unearth current trends affecting knowledge creation in international/global pharmaceutical R&D. We reviewed scientific papers published between 1980 and 2005. Key findings include promising trends in pharmaceutical innovation and human resource management, and their potential implications on current R&D practices within the pharmaceutical industry, from managerial and policy-making perspectives.

Mathematical pedagogical content knowledge in early childhood education: A study in initial teacher education in Portugal

The study aims to explore the specificity of mathematics Pedagogical Content Knowledge in Early Childhood Education Pedagogy. The pedagogy of ECE (Siraj-Blatchford, 2010) and the didactics of ECE (Pramling & Pramling-Samuelsson, 2011) suggest dimensions of knowledge that require strong content and PC knowledge of teachers. Recent studies about PCK of ECE teachers highlight similar specific dimensions: organization of educational environment and interactions with children (Lee, 2010, McCray, 2008, Rojas, 2008). The current framework for ECE Teacher Education in Portugal (since 2007) focuses both content knowledge and subject didactics. PCK has been labelled the 'great unknown' in ECE (Rojas, 2008) in traditions where the child's development is considered as the main knowledge base for ECE (Chen & McNamee, 2006, Cullen, 2005, Hedges & Cullen, 2005). We studied the perspectives of 27 initial teacher education students about knowledge for teaching and about ECE Pedagogy. We used one open-ended questionnaire and students' analysis of episodes focusing children's answers or discourse relevant for mathematics (about high numbers and square root). The questionnaire was anonymous and students’ permission to use the answers was obtained. In the questionnaire, interactions with children (62%) and organization of the educational environment (38%) are highlighted as the most important focus for the teacher. Students suggested tasks that were adult planned and oriented to further the situations presented in the episodes. Very few references to children's exploratory actions (Bonawitz et al., 2011) were made. The specificity of ECE (child initiated activities, e.g.) needs to be further developed in initial teacher education.

Delivery Of A Urology Online Course Using Moodle Versus Didactic Lectures Methods.

To subjectively and objectively compare an accessible interactive electronic library using Moodle with lectures for urology teaching of medical students. Forty consecutive fourth-year medical students and one urology teacher were exposed to two teaching methods (4 weeks each) in the form of problem-based learning: - lectures and - student-centered group discussion based on Moodle (modular object-oriented dynamic learning environment) full time online delivered (24/7) with video surgeries, electronic urology cases and additional basic principles of the disease process. All 40 students completed the study. While 30% were moderately dissatisfied with their current knowledge base, online learning course delivery using Moodle was considered superior to the lectures by 86% of the students. The study found the following observations: (1) the increment in learning grades ranged from 7.0 to 9.7 for students in the online Moodle course compared to 4.0-9.6 to didactic lectures; (2) the self-reported student involvement in the online course was characterized as large by over 60%; (3) the teacher-student interaction was described as very frequent (50%) and moderately frequent (50%); and (4) more inquiries and requisitions by students as well as peer assisting were observed from the students using the Moodle platform. The Moodle platform is feasible and effective, enthusing medical students to learn, improving immersion in the urology clinical rotation and encouraging the spontaneous peer assisted learning. Future studies should expand objective evaluations of knowledge acquisition and retention.

A multifunctional polyketide-peptide synthetase essential for albicidin biosynthesis in Xanthomonas albilineans

Albicidins, a family of potent antibiotics and phytotoxins produced by the sugarcane leaf scald pathogen Xanthomonas albilineans, inhibit DNA replication in bacteria and plastids. A gene located by Tn5-tagging was confirmed by complementation to participate in albicidin biosynthesis. The gene (xabB) encodes a large protein (predicted Mr 525695), with a modular architecture indicative of a multifunctional polyketide synthase (PKS) linked to a non-ribosomal peptide synthetase (NRPS). At 4801 amino acids in length, XabB is the largest reported PKS–NRPS. Twelve catalytic domains in this multifunctional enzyme are arranged in the order N terminus–acyl-CoA ligase (AL)–acyl carrier protein (ACP)–ß-ketoacyl synthase (KS)–ß-ketoacyl reductase (KR)–ACP–ACP–KS–peptidyl carrier protein (PCP)–condensation (C)–adenylation–PCP–C. The modular architecture of XabB indicates likely steps in albicidin biosynthesis and approaches to enhance antibiotic yield. The novel pattern of domains, in comparison with known PKS–NRPS enzymes for antibiotic production, also contributes to the knowledge base for rational design of enzymes producing novel antibiotics.

Agent Inferencing Meets the Semantic Web

We provide all agent; the capability to infer the relations (assertions) entailed by the rules that, describe the formal semantics of art RDFS knowledge-base. The proposed inferencing process formulates each semantic restriction as a rule implemented within a, SPARQL query statement. The process expands the original RDF graph into a fuller graph that. explicitly captures the rule's described semantics. The approach is currently being explored in order to support descriptions that follow the generic Semantic Web Rule Language. An experiment, using the Fire-Brigade domain, a small-scale knowledge-base, is adopted to illustrate the agent modeling method and the inferencing process.

An integrated system to support electricity tariff contract definition

This paper presents an integrated system that helps both retail companies and electricity consumers on the definition of the best retail contracts and tariffs. This integrated system is composed by a Decision Support System (DSS) based on a Consumer Characterization Framework (CCF). The CCF is based on data mining techniques, applied to obtain useful knowledge about electricity consumers from large amounts of consumption data. This knowledge is acquired following an innovative and systematic approach able to identify different consumers’ classes, represented by a load profile, and its characterization using decision trees. The framework generates inputs to use in the knowledge base and in the database of the DSS. The rule sets derived from the decision trees are integrated in the knowledge base of the DSS. The load profiles together with the information about contracts and electricity prices form the database of the DSS. This DSS is able to perform the classification of different consumers, present its load profile and test different electricity tariffs and contracts. The final outputs of the DSS are a comparative economic analysis between different contracts and advice about the most economic contract to each consumer class. The presentation of the DSS is completed with an application example using a real data base of consumers from the Portuguese distribution company.

WEXSYS - Web Expert System

Desde o início que os sistemas periciais não foram vistos apenas como sistemas que poderiam substituir os peritos. Os peritos por sua vez, independentemente da área em que operam, eram tidos como indivíduos que atingiram a excelência através da experiência, estudo e total dedicação, por vezes durante anos. Hoje, mais do que assumir o papel de um perito na excelência de uma área de actuação, os sistemas periciais sobressaem pela disponibilidade contínua, acessos facilitados, custos reduzidos, estabilidade de funcionamento e coerência de raciocínio. Empurrados pelos avanços actuais das redes em termos de velocidade e propagação global, estes sistemas são hoje disponibilizados de forma mais simples e acessível. Este trabalho é realizado com o propósito de evoluir o actual sistema para uma versão mais apelativa, funcional e fiável. Um dos objectivos passa pela evolução do actual modo de funcionamento, utilização local e apenas um utilizador, para um modo de funcionamento que permita uma disponibilização num ambiente de acesso global, acessível a qualquer hora e em qualquer local.

Sistema pericial para o estudo de composição de betões

O betão é o material de construção feito pelo Homem mais utilizado no mundo. A sua composição é um processo complexo que exige um conhecimento teórico sólido e muita experiência prática, pelo que poucas pessoas estão habilitadas para o fazer e são muito requisitadas. No entanto não existe muita oferta actual de software que contemple alguns dos aspectos importantes da composição do betão, nomeadamente para o contexto europeu. Nesse sentido, foi desenvolvido um sistema de apoio à decisão chamado Betacomp, baseado num sistema pericial, para realizar estudos de composição de betão. Este contempla as normas legais portuguesas e europeias, e a partir da especificação do betão apresenta toda a informação necessária para se produzir um ensaio de betão. A aquisição do conhecimento necessário ao sistema contou com a colaboração de um especialista com longa e comprovada experiência na área da formulação e produção do betão, tendo sido construída uma base de conhecimento baseada em regras de produção no formato drl (Drools Rule Language). O desenvolvimento foi realizado na plataforma Drools.net, em C# e VB.net. O Betacomp suporta os tipos de betão mais comuns, assim como adições e adjuvantes, sendo aplicável numa grande parte dos cenários de obra. Tem a funcionalidade de fornecer explicações sobre as suas decisões ao utilizador, auxiliando a perceber as conclusões atingidas e simultaneamente pode funcionar como uma ferramenta pedagógica. A sua abordagem é bastante pragmática e de certo modo inovadora, tendo em conta parâmetros novos, que habitualmente não são considerados neste tipo de software. Um deles é o nível do controlo de qualidade do produtor de betão, sendo feito um ajuste de compensação à resistência do betão a cumprir, proporcional à qualidade do produtor. No caso dos produtores de betão, permite que indiquem os constituintes que já possuem para os poderem aproveitar (caso não haja impedimentos técnicos) , uma prática muito comum e que permitirá eventualmente uma aceitação maior da aplicação, dado que reflecte a forma habitual de agir nos produtores.

e-Recruitment: descrição e inferência sobre oferta e procura

Trabalho de Projeto para obtenção do grau de Mestre em Engenharia Informática e de Computadores

Modelling a network of heterogeneous e-learning systems

In recent years emerged several initiatives promoted by educational organizations to adapt Service Oriented Architectures (SOA) to e-learning. These initiatives commonly named eLearning Frameworks share a common goal: to create flexible learning environments by integrating heterogeneous systems already available in many educational institutions. However, these frameworks were designed for integration of systems participating in business like processes rather than on complex pedagogical processes as those related to automatic evaluation. Consequently, their knowledge bases lack some fundamental components that are needed to model pedagogical processes. The objective of the research described in this paper is to study the applicability of eLearning frameworks for modelling a network of heterogeneous eLearning systems, using the automatic evaluation of programming exercises as a case study. The paper surveys the existing eLearning frameworks to justify the selection of the e-Framework. This framework is described in detail and identified the necessary components missing from its knowledge base, more precisely, a service genre, expression and usage model for an evaluation service. The extensibility of the framework is tested with the definition of this service. A concrete model for evaluation of programming exercises is presented as a validation of the proposed approach.

Computing semantic relatedness using DBPedia

Extracting the semantic relatedness of terms is an important topic in several areas, including data mining, information retrieval and web recommendation. This paper presents an approach for computing the semantic relatedness of terms using the knowledge base of DBpedia — a community effort to extract structured information from Wikipedia. Several approaches to extract semantic relatedness from Wikipedia using bag-of-words vector models are already available in the literature. The research presented in this paper explores a novel approach using paths on an ontological graph extracted from DBpedia. It is based on an algorithm for finding and weighting a collection of paths connecting concept nodes. This algorithm was implemented on a tool called Shakti that extract relevant ontological data for a given domain from DBpedia using its SPARQL endpoint. To validate the proposed approach Shakti was used to recommend web pages on a Portuguese social site related to alternative music and the results of that experiment are reported in this paper.

Reason Maintenance Systems: Tools for Foundations-based Belief Revision

Dynamic and distributed environments are hard to model since they suffer from unexpected changes, incomplete knowledge, and conflicting perspectives and, thus, call for appropriate knowledge representation and reasoning (KRR) systems. Such KRR systems must handle sets of dynamic beliefs, be sensitive to communicated and perceived changes in the environment and, consequently, may have to drop current beliefs in face of new findings or disregard any new data that conflicts with stronger convictions held by the system. Not only do they need to represent and reason with beliefs, but also they must perform belief revision to maintain the overall consistency of the knowledge base. One way of developing such systems is to use reason maintenance systems (RMS). In this paper we provide an overview of the most representative types of RMS, which are also known as truth maintenance systems (TMS), which are computational instances of the foundations-based theory of belief revision. An RMS module works together with a problem solver. The latter feeds the RMS with assumptions (core beliefs) and conclusions (derived beliefs), which are accompanied by their respective foundations. The role of the RMS module is to store the beliefs, associate with each belief (core or derived belief) the corresponding set of supporting foundations and maintain the consistency of the overall reasoning by keeping, for each represented belief, the current supporting justifications. Two major approaches are used to reason maintenance: single-and multiple-context reasoning systems. Although in the single-context systems, each belief is associated to the beliefs that directly generated it—the justification-based TMS (JTMS) or the logic-based TMS (LTMS), in the multiple context counterparts, each belief is associated with the minimal set of assumptions from which it can be inferred—the assumption-based TMS (ATMS) or the multiple belief reasoner (MBR).

Vertex component analysis: a geometric-based approach to unmix hyperspectral data

Hyperspectral remote sensing exploits the electromagnetic scattering patterns of the different materials at specific wavelengths [2, 3]. Hyperspectral sensors have been developed to sample the scattered portion of the electromagnetic spectrum extending from the visible region through the near-infrared and mid-infrared, in hundreds of narrow contiguous bands [4, 5]. The number and variety of potential civilian and military applications of hyperspectral remote sensing is enormous [6, 7]. Very often, the resolution cell corresponding to a single pixel in an image contains several substances (endmembers) [4]. In this situation, the scattered energy is a mixing of the endmember spectra. A challenging task underlying many hyperspectral imagery applications is then decomposing a mixed pixel into a collection of reflectance spectra, called endmember signatures, and the corresponding abundance fractions [8–10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. Linear mixing model holds approximately when the mixing scale is macroscopic [13] and there is negligible interaction among distinct endmembers [3, 14]. If, however, the mixing scale is microscopic (or intimate mixtures) [15, 16] and the incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [17], the linear model is no longer accurate. Linear spectral unmixing has been intensively researched in the last years [9, 10, 12, 18–21]. It considers that a mixed pixel is a linear combination of endmember signatures weighted by the correspondent abundance fractions. Under this model, and assuming that the number of substances and their reflectance spectra are known, hyperspectral unmixing is a linear problem for which many solutions have been proposed (e.g., maximum likelihood estimation [8], spectral signature matching [22], spectral angle mapper [23], subspace projection methods [24,25], and constrained least squares [26]). In most cases, the number of substances and their reflectances are not known and, then, hyperspectral unmixing falls into the class of blind source separation problems [27]. Independent component analysis (ICA) has recently been proposed as a tool to blindly unmix hyperspectral data [28–31]. ICA is based on the assumption of mutually independent sources (abundance fractions), which is not the case of hyperspectral data, since the sum of abundance fractions is constant, implying statistical dependence among them. This dependence compromises ICA applicability to hyperspectral images as shown in Refs. [21, 32]. In fact, ICA finds the endmember signatures by multiplying the spectral vectors with an unmixing matrix, which minimizes the mutual information among sources. If sources are independent, ICA provides the correct unmixing, since the minimum of the mutual information is obtained only when sources are independent. This is no longer true for dependent abundance fractions. Nevertheless, some endmembers may be approximately unmixed. These aspects are addressed in Ref. [33]. Under the linear mixing model, the observations from a scene are in a simplex whose vertices correspond to the endmembers. Several approaches [34–36] have exploited this geometric feature of hyperspectral mixtures [35]. Minimum volume transform (MVT) algorithm [36] determines the simplex of minimum volume containing the data. The method presented in Ref. [37] is also of MVT type but, by introducing the notion of bundles, it takes into account the endmember variability usually present in hyperspectral mixtures. The MVT type approaches are complex from the computational point of view. Usually, these algorithms find in the first place the convex hull defined by the observed data and then fit a minimum volume simplex to it. For example, the gift wrapping algorithm [38] computes the convex hull of n data points in a d-dimensional space with a computational complexity of O(nbd=2cþ1), where bxc is the highest integer lower or equal than x and n is the number of samples. The complexity of the method presented in Ref. [37] is even higher, since the temperature of the simulated annealing algorithm used shall follow a log( ) law [39] to assure convergence (in probability) to the desired solution. Aiming at a lower computational complexity, some algorithms such as the pixel purity index (PPI) [35] and the N-FINDR [40] still find the minimum volume simplex containing the data cloud, but they assume the presence of at least one pure pixel of each endmember in the data. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. PPI algorithm uses the minimum noise fraction (MNF) [41] as a preprocessing step to reduce dimensionality and to improve the signal-to-noise ratio (SNR). The algorithm then projects every spectral vector onto skewers (large number of random vectors) [35, 42,43]. The points corresponding to extremes, for each skewer direction, are stored. A cumulative account records the number of times each pixel (i.e., a given spectral vector) is found to be an extreme. The pixels with the highest scores are the purest ones. N-FINDR algorithm [40] is based on the fact that in p spectral dimensions, the p-volume defined by a simplex formed by the purest pixels is larger than any other volume defined by any other combination of pixels. This algorithm finds the set of pixels defining the largest volume by inflating a simplex inside the data. ORA SIS [44, 45] is a hyperspectral framework developed by the U.S. Naval Research Laboratory consisting of several algorithms organized in six modules: exemplar selector, adaptative learner, demixer, knowledge base or spectral library, and spatial postrocessor. The first step consists in flat-fielding the spectra. Next, the exemplar selection module is used to select spectral vectors that best represent the smaller convex cone containing the data. The other pixels are rejected when the spectral angle distance (SAD) is less than a given thresh old. The procedure finds the basis for a subspace of a lower dimension using a modified Gram–Schmidt orthogonalizati on. The selected vectors are then projected onto this subspace and a simplex is found by an MV T pro cess. ORA SIS is oriented to real-time target detection from uncrewed air vehicles using hyperspectral data [46]. In this chapter we develop a new algorithm to unmix linear mixtures of endmember spectra. First, the algorithm determines the number of endmembers and the signal subspace using a newly developed concept [47, 48]. Second, the algorithm extracts the most pure pixels present in the data. Unlike other methods, this algorithm is completely automatic and unsupervised. To estimate the number of endmembers and the signal subspace in hyperspectral linear mixtures, the proposed scheme begins by estimating sign al and noise correlation matrices. The latter is based on multiple regression theory. The signal subspace is then identified by selectin g the set of signal eigenvalue s that best represents the data, in the least-square sense [48,49 ], we note, however, that VCA works with projected and with unprojected data. The extraction of the end members exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. As PPI and N-FIND R algorithms, VCA also assumes the presence of pure pixels in the data. The algorithm iteratively projects data on to a direction orthogonal to the subspace spanned by the endmembers already determined. The new end member signature corresponds to the extreme of the projection. The algorithm iterates until all end members are exhausted. VCA performs much better than PPI and better than or comparable to N-FI NDR; yet it has a computational complexity between on e and two orders of magnitude lower than N-FINDR. The chapter is structure d as follows. Section 19.2 describes the fundamentals of the proposed method. Section 19.3 and Section 19.4 evaluate the proposed algorithm using simulated and real data, respectively. Section 19.5 presents some concluding remarks.