Memory-based parsing with parallel marker-passing

The semantic network array processor (SNAP) is a specialized, highly parallel architecture for knowledge representation and reasoning. The instruction set has been carefully designed to reflect the requirements of semantic network processing. SNAP is a marker propagation architecture, where the passing of markers between cells plays a fundamental role. The movement of markers between cells is controlled by a set of propagation rules. Various reasoning mechanisms were implemented using these propagation rules. A simulator was developed, and knowledge processing examples, such as inheritance, recognition, and classification, were tested. By comparing the simulation results with the same examples run on the Connection Machine, it was found that SNAP outperforms the Connection Machine over a broad range of knowledge processing examples by a factor of 1000 or more.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The advent of Industry 4.0 brings the Internet of Things (IoT) and Cyber-Physical Systems (CPSs) inside the factory premise and made the boundaries between the information and operational technologies (IT & OT) obsolete. This, in turn, introduces the problem of interoperability due to the integration of multiple industrial protocols and technologies from various automation networks. The aim of this paper is to investigate this interoperability problem of heterogeneous network management and propose a semantic network knowledge base as a solution to it. It also describes a set of generic interface functions for data acquisition.

End-to-end (E2E) semantic communication (SemCom) powered by semantic knowledge base (SKB) is an efficient SemCom framework. However, in practical scenarios, SKB discrepancy among multiple SemCom pairs arises due to dynamic environmental changes (e.g., varying source data or tasks) or system-level alterations (e.g., integration of new SemCom pairs with divergent SKBs). Such discrepancy leads to performance disparity in semantic transmission, where underperforming pairs fail to maintain efficient task execution. To address this challenge, this paper introduces a cooperative SKB update policy, which enables collaborative evolution of SKBs to mitigate SKB discrepancy and improve performance of underperforming pairs. For each SemCom pair endowed with SKB-enabled SemCom, partial local SKB is selected out and uploaded to a mobile edge computing (MEC) server for establishing a global SKB. The global SKB aggregates the advantages of each local SKB, and is broadcasted to all SemCom pairs. Then, each SemCom pair updates their local SKB with the assistance of the global SKB. This process makes the local SKBs more sensible and less ambiguous, thereby enhancing the semantic transmission performance. Furthermore, in order to maximize the cooperative gains under limited uplink budgets of SemCom pairs, a knowledge selection optimization problem is formulated for the selection of the uploaded knowledge. Numerical results show that the proposed cooperative SKB update policy obtains significant performance gains, especially for the initially poor-performing pairs, and provide comprehensive performance comparison of the knowledge selection scheme.

In recent years, several large-scale knowledge bases (KBs) have been constructed, such as YAGO, DBpedia, and Google Knowledge Graph. Although automatic extractio techniques that extract facts and rules from the Web is necessary for constructing such large-scale KBs, incorporation of noisy, unreliable knowledge cannot be unavoidable. Thus, Google Knowledge Vault assigns extracted knowledge with confidence scores based on consistency with the existing KBs. In this paper, we propose a new approach for associating confidence scores with knowledge based on a large amount of raw data for domains, where there is no existing KB. We first construct knowledge in a specific domain as a semantic network, and then design a probabilistic network, that corresponds to the semantic network. To associate the confidence scores with the semantic network, we train the probabilistic network with a large amount of open data, provided by the Osaka central wholesale market in Japan. We also confirm the validity of the confidence scores with the accuracy of reasoning on the probabilistic network. A semantic network associated with confidence scores, that is, a weighted labeled graph is advantageous not only for reducing the noisy, unreliable knowledge with low confidence, but also for making retrieval results ranking on the KB. In the future, probabilistic reasoning on semantic networks may also be possible.

Semantic impairment in patients with Alzheimer's disease (AD) is revealed by tasks of verbal naming, verbal fluency, and semantic knowledge. Causes of the deficit remain unclear in spite of many studies to investigate whether AD patients suffer from the inability to have voluntary access to an almost intact semantic store or from its break down. Word-stem completion (WSC) tasks have been utilized in healthy subjects in order to study semantic memory and network by exploiting the possibility of the involuntary access to them. Available conflicting data refer to the presence of semantic prime in AD patients. To explore the semantic network in AD, patients were requested to complete with the first word that sprang to their mind a stem submitted immediately after presentation of the word prime, as a WSC task. Stems consisted of the three beginning letters of words that were semantically related to primes. We compared data obtained with this task from patients with mild to moderate AD with those from normal controls (NC). AD patients completed less stems (P<0.001) with the expected words than NC, suggesting a break down of the semantic network rather than a deficit in the access to the semantic store.

In the past two decades, there has been increasing use of semantic networks in engineering design for supporting various activities, such as knowledge extraction, prior art search, idea generation and evaluation. Leveraging large-scale pre-trained graph knowledge databases to support engineering design-related natural language processing (NLP) tasks has attracted a growing interest in the engineering design research community. Therefore, this paper aims to provide a survey of the state-of-the-art semantic networks for engineering design and propositions of future research to build and utilize large-scale semantic networks as knowledge bases to support engineering design research and practice. The survey shows that WordNet, ConceptNet and other semantic networks, which contain common-sense knowledge or are trained on non-engineering data sources, are primarily used by engineering design researchers to develop methods and tools. Meanwhile, there are emerging efforts in constructing engineering and technical-contextualized semantic network databases, such as B-Link and TechNet, through retrieving data from technical data sources and employing unsupervised machine learning approaches. On this basis, we recommend six strategic future research directions to advance the development and uses of large-scale semantic networks for artificial intelligence applications in engineering design.

414 LANGUAGE, VOLUME 67, NUMBER 2 (1991) Brixhe & Lejeune's 1984 Corpus des inscriptions paléo-phrygiennes. Since Phrygian is both closely related to Greek and preserved in part through Greek testimony, N has chosen to present his account as a history of PhrygianGreek linguistic contact. This procedure may explain his vagueness in discussing shared features of the two languages: although he calls them 'common innovations' (10), he seems elsewhere to regard them as the result of borrowing during a hypothesized period of close contact among Greek, Phrygian, and several other IE languages in the south Balkans (5-6). Some of these shared features are quite striking—e.g. factitives in *-oyô like Gk. orthöö 'straighten'. Phr. kakoioi (11, 13), and laryngeal vocalization followed by umlaut in Gk. ónoma, Phr. onoman = [b] not [bh], this development is trivial and proves only that Phrygian did not share the Greek change to [ph] etc.). Pp. 6-10: Common retentions of Greek and Phrygian (e.g. the augment and the thirdperson present middle ending -toi). Pp. 10-13: Common innovations of Greek and Phrygian (e.g. agent nouns in -tä, Gk. autos 'self = Phr. autos 'particular'). Pp. 13-16: Inherited lexical material of Phrygian which is lost or only marginally preserved in Greek. Pp. 16-18: Sometime after the hypothesized period of south Balkan close contact with Greek, the Phrygians migrated to Anatolia, where they are attested in historical times. Contact with speakers ofAnatolian languages led to borrowings into Phrygian (mainly names, e.g. Midas). Pp. 18-19: Greek expansion in the first millennium led to contact and hence Greek loans into Phrygian. Pp. 19-22: Direct attestation of Phrygian lost from the third century B.C.E. through the first century CE. Pp. 22-23: Neo-Phrygian attested in grave inscriptions, still in part uninterpretable . Although Phrygian is unlikely ever to play a significant role in IE historical linguistics, it does, as N remarks in conclusion (23), repay time spent applying to it our knowledge of IE. N's essay is a valuable contribution to the project of understanding Phrygian. [Andrew Garrett, University of Texas, Austin.] Natural language processing technologies in Artificial Intelligence: The science and industry perspective. By Klaus K. Obermeier. Chichester : Ellis Horwood, 1989. Pp. 263. Natural language processing (NLP) is the area of research concerned with the many aspects of 'natural' language communication with computers . In this book Obermeier provides an introduction to NLP from both commercial and academic perspectives, primarily aimed at businessmen and data-processing professionals. His presentation is nontechnical, though very comprehensive . Linguists will find the book an accessible introduction to the practical issues in NLP, which may help to clarify the direction and emphasis in this area of language research. The book's organization is excellent, making it very easy to read, although the typographic and editorial quality are not the best. The twelve chapters are divided into three sections: 'NLP in perspective' (17-92). which provides an introduction to the structure of NLP systems and the approaches and formalisms used to implement them; 'Applications' (93-185), in which six major applications of NLP are described: and 'Bottom line' (187-232), where O presents his own evaluation of the state of NLP research and current systems. A good list of references (239-59) is also included. Much of the material in the first section will be familiar to linguists, since the design of NLP systems is largely based on formal linguistic analyses in the areas ofphonology, morphology, syntax, and semantics. In addition, methods for knowledge representation and reasoning drawn from Artificial Intelligence are explained in relation to their relevance to NLP development. This nontechnical overview of the techniques used in NLP is up-to-date. It includes a discussion of parsing methods ranging from contextfree parsing, unification-based parsing, and ATNs to such knowledge-representation schemes from AI as production rules, semantic networks, and frames, to the new connectionist models. BOOK NOTICES 415 The second section comprises the largest part of the book, with one chapter devoted to each of the applications addressed: natural-language interfaces, machine translation, text processing/ understanding, text generation, speech processing , and writing aids. For each application there is a brief...

Acquisition and validation of software requirements

Even though the advent of the Web coupled with powerful search engines has empowered the knowledge workers to quickly find the needed information, it still is a time-consuming operation. Presently there are no readily available tools that can create and maintain an up-to-date personal knowledge base that can be readily consulted when needed. While organizing the entire Web as a semantic network is a long-term goal, creation of a semantic network of personal knowledge sources that are continuously updated by crawlers and other devices is an attainable task. We created an app titled ExperTwin, that collects personally relevant knowledge units (known as JANs) from the Web, Email correspondence, and locally stored files, organize them as a semantic network that can be easily queried and visualized in many formats - just in time - when performing a knowledge-based task. The architecture of ExperTwin is based on the model of a “Society of Intelligent Agents”, where each agent is responsible for a specific task. Collection of JANs from multiple sources, establishing the relevancy, and creation of the personal semantic network are some of the many tasks performed by the individual agents. Tensorflow and Natural Language Processing (NLP) tools have been implemented to let ExperTwin learn from users. Document the design and deployment of ExperTwin as a “Knowledge Advantage Machine” able to search for relevant information while performing a knowledge-based task, is the main goal of the research presented in this paper.

With the advent of the big-data era, massive textual information stored in electronic and digital documents have become valuable resources for knowledge discovery in the fields of design and engineering. Ontology technologies and semantic networks have been widely applied with text mining techniques including Natural Language Processing (NLP) to extract structured knowledge associations from the large-scale unstructured textual data. However, most existing works mainly focus on how to construct the semantic networks by developing various text mining methods such as statistical approaches and semantic approaches, while few studies are found to focus on how to subsequently analyze and fully utilize the already well-established semantic networks. In this paper, a specific network analysis method is proposed to discover the implicit knowledge associations from the existing semantic network for improving knowledge discovery and design innovation. Pythagorean means are applied with Dijkstra’s shortest path algorithm to discover the implicit knowledge associations either around a single knowledge concept or between two concepts. Six criteria are established to evaluate and rank the correlation degree of the implicit associations. Two engineering case studies were conducted to illustrate the proposed knowledge discovery process, and the results showed the effectiveness of the retrieved implicit knowledge associations on helping providing relevant knowledge from various aspects, and provoking creative ideas for engineering innovation.

Introduction. Donald Walker: a Remembrance. Section 1: The Task of Natural Language Processing. Natural Language Processing: an Historical Review K. Sparck Jones. On Getting a Computer to Listen J. Robinson. Utterance and Objective: Issues in Natural Language Communication B. Grosz. On the Proper Place of Semantics in Machine Translation M. King. Developing a Natural Language Interface to Complex Data G.G. Hendrix, E.D. Sacerdoti. User-Needs Analysis and Design Methodology for an Automated Document Generator K. Kukich, K. McKeown, J. Shaw, J. Robin, J. Lim, N. Morgan, J. Philips. Section 2: Building Computational Lexicons. Machine-Readable Dictionaries and Computational Linguistics Research B. Boguraev. Research Toward the Development of a Lexical Knowledge Base for Natural Language Processing R.A. Amsler. Discovering Relationships Among Word Senses R.J. Byrd. Machine Readable Dictionary as a Source of Grammatical Information E. Hajicova, A. Rosen. The ITT Lexical Database: Dream and Reality S. Pin-Ngern Conlon. Visions of the Digital Library: Views on Using Computational Linguistics and Semantic Nets in Information Retrieval J.L. Klavans. Anatomy of a Verb Entry: from Linguistic Theory to Lexicographic Practice B.T. Atkins, J. Kegl, B. Levin. Issues for Lexicon Building N. Calzolari. Outline of a Model for Lexical Databases N. Ide, J. le Maitre, J. Veronts. Construction-Based MT Lexicons L. Levin, S. Nirenburg. Dependency-Based Grammatical Information in the Lexicon P. Sgall. Semantics in the Brain's Lexicon -- Some Preliminary Remarks on its Epistemology H. Schnelle. Section 3: The Acquisition and Use of LargeCorpora. The Ecology of Language D.E. Walker. Representativeness in Corpus Design D. Biber. The Text Encoding Initiative C.M. Sperberg-McQueen. Discrimination Decisions for 100,000 Dimensional W.A. Gale, K.W. Church, D. Yarowsky. Acquisition and Exploitation of Textual Resources for NLP S. Armstrong-Warwick. The Center for Electronic Texts in the Humanities S. Hockey. Design Principles for Electronic Textual Resources: Investigating Users and Uses of Scholarly Information N.J. Belkin. Section 4: Topics, Methods and Formalisms in Syntax, Semantics and Pragmatics. Evaluating English Sentences in a Logical Model J. Friedman, D.B. Moran, D.S. Warren. Recovering Implicit Information M.S. Palmer, D.A. Dahl, R.J. Schiffman, L. Hirschman, M. Linebarger, J. Downing. Flexible Generation: Taking the User into Account C.L. Paris, V.O. Mittal. Two Principles of Parse Preference J.R. Hobbs, J. Bear. UD, yet Another Unification Device R. Johnson, M. Rosner. Varieties of Heuristics in Sentence Parsing M. Nagao. Some Recent Trends in Natural Language Processing A.K. Joshi. Stone Soup and the French Room Y. Wilks.

Natural language processing (NLP) is an important and challenging field of computer science. With the advent of computers and related technologies, NLP has grown out of the field of linguistics and is getting more attention in the computer field to support a wide variety of intelligent applications. NLP basically refers to automatic processing of natural languages by computers or machines in order to understand human language in written or spoken form. Ontologies are semantic models that provide general description of the types and structures of objects in a particular subject area or domain. Core components of ontology include concepts, relationships, instances, and axioms. Ontology is used to depict the hierarchical organization of concepts and their relationships. Knowledge graphs represent a set of interconnected descriptions of objects or concepts, and are sometimes referred to as semantic networks. In short, an ontology can be treated as a meta-model for data and knowledge graphs as a representation of the actual data or facts. An ontology (a formal description of relationships) plays a vital role in building a knowledge graph. Ontology and knowledge graphs have proven to be powerful tools to manage enterprise or big data. Today, a number of applications rely on ontology and knowledge graphs, such as knowledge management, information discovery, text extraction, semantic search, content-based recommendation, and intelligent chatbots. This chapter focuses on the importance of ontology and knowledge graphs for Developing NLP-based applications is difficult because computers require structured data, and human speech is highly complex, extremely diverse, unstructured in nature, and often ambiguous. In general, manual creation of knowledge graphs is expensive, therefore any automation of the process is highly desirable. Here, ontologies and knowledge graphs play a crucial role by providing a well-structured knowledge base, allowing semi-automatic creation of a lexicon to be used effectively in language translations, spellcheckers, keyword extraction and similar NLP applications.

This bibliography on parallel parsing and recognition of context-free languages covers most of the important publications in this area of computer science and natural language processing. The list of publications is preluded by an introduction which presents an overview of the field.

The historic Encyclopedia of Architecture, written in German between 1880-1943, was one of the largest projects aiming at conserving all architectural knowledge available at that time. Today, its vast amount of content is mostly lost: few complete sets are available, and its complex structure does not lend itself easily to contemporary application. We show how modern semantic technologies can be applied to make these heritage documents accessible again. In particular, we demonstrate how to go beyond classical digitization projects by transforming the historical documents into a semantic knowledge base. Using techniques from natural language processing and the Semantic Web, we show how to automatically populate an ontology that can be used for various application scenarios: Building historians can use it to navigate and query the encyclopedia, while architects can directly integrate it into contemporary construction tools. Additionally, all content is made accessible in a user-friendly Wiki interface that combines original text with NLP-derived metadata and adds annotation capabilities for collaborative use.

The authors have designed a parallel architecture called the semantic network array processor (SNAP) for natural language understanding (NLU) and other artificial intelligence applications. It is capable of executing large marker-passing programs and generating results in real-time. The design features 32 processing clusters with four to five functionally dedicated digital signal processors in each cluster. Processors within clusters share a marker-processing memory while communication between clusters is implemented by a buffered message-passing scheme. Throughout the machine, overlapping groups of multiport memories provide a direct yet visible interconnection network. The result is a low cost, flexible, and observable parallel processor capable of performing NLU operations within subsecond response time.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>