Interlinking through Lemmas. The Lexical Collection of the LiLa Knowledge Base of Linguistic Resources for Latin

The paper presents conception of utilization of Petri nets to visualization the structure of rule knowledge bases and the functional and implementation issues of system using this conception for visualization both the structure of knowledge bases and the inference process in such bases. The basic terms of Petri nets as well as the idea of using Petri nets as the modeling rule knowledge base have been presented in this paper. The methods of using Petri nets for modeling of the inference process has been also discussed in this paper together with description of realization features of proper software.

Different lexical resources may pursue different views on lexical meaning. However, all of them deal with lexical items as common basic components, which are described according to criteria that may vary from one resource to another. In this paper, we present a method for measuring the degree of similarity between a valency-based lexical resource and a WordNet. This is motivated by both theoretical and practical reasons. As for the former, we wonder if there are lexical classes that "impose" themselves regardless of the fact that they are explicitly recorded as such in source lexical resources. As for the latter, our work wants to contribute to the research task dealing with merging lexical resources. In order to apply and evaluate our method, we propose a normalized coefficient of overlapping that measures the overlapping rate between a valency lexicon and a WordNet. In particular, in the context of the exploitation of the linguistic resources for ancient languages built over the last decade, we compute and evaluate the overlapping between a selection of homogeneous lexical subsets extracted from two lexical resources for Latin.

Treebanks are essential resources for both data-driven approaches to natural language processing (NLP) and empirical linguistic researches. Developing these resources is time- and cost-consuming and requires specialized expertise. Therefore, they should be designed to be reused for different purposes. Currently, there are several dependency treebanks for some languages which are annotated in CoNLL format. For some languages, such as Persian, they are the few available linguistic resources. These treebanks are more suitable for the input of data-driven parsers, and querying linguistic data in them is not easy. In recent years, XML has been widely used for formatting treebanks, and there are various tools available for querying and annotating a linguistic croups in this format. In this paper, we present a tool for converting a dependency treebank in CoNLL format to an appropriate XML format. We designed the XML scheme to be particularly suitable for writing linguistic queries in XQuery syntax.

The problem of easy knowledge arises for theories that have what I call a “basic knowledge structure”. S has basic knowledge of P just in case S knows P prior to knowing that the cognitive source of S's knowing P is reliable.1 Our knowledge has a basic knowledge structure (BKS) just in case we have basic knowledge and we come to know our faculties are reliable on the basis of our basic knowledge. The problem I raised in “Basic Knowledge and the Problem of Easy Knowledge”2 (BKEK) is that once we allow for basic knowledge, we can come to know our faculties are reliable in ways that intuitively are too easy. This raises a serious doubt about whether we had the basic knowledge in the first place. In “Easy Knowledge”, Peter Markie argues that BKS theories do not face any problem concerning easy knowledge.3 I argued that the problem arises in two forms, and Markie takes issue with both. I will argue that Markie's defense of BKS theories fails.

For the data-driven approach in Natural Language Processing (NLP) applications, good quality linguistic resources considered as a main factor to obtain good results. Although Arabic language is one of the main languages in the world, it is considered as low-resourced language in term of good quality and free linguistic resources. This work presents the first stage of building a new open source dependency treebank for Arabic language. It describes the prototype of the new dependency treebank that are inspired by Lexical Functional Grammar (LFG). This paper shows a main approach of developing a newly treebank and put lines the future work needed to complete this novel linguistic resource.

The use of lexical resources in linguistic analysis has expanded rapidly in recent years. However, most lexical resources, such as WordNet or online dictionaries, at this point do not usually indicate figurative meanings, such as conceptual metaphors, as part of a lexical entry. Studies that attempt to establish the relationships between literal and figurative language by detecting the connectivity between WordNet relations usually do not deal with linguistic data directly. However, the present study demonstrates that SUMO definitions can be used to identify the source domains used in conceptual metaphors. This is achieved by identifying the relationships between metaphorical expressions and their corresponding ontological nodes. Such links are important because they show which lexical items are mapped under which concepts. This, in turn, helps specify which lexical items in electronic resources involve conceptual mappings. Looking specifically at the concept of PERSON, this work also establishes connectivity between lexical items which are related to “Organism.” Therefore, the methodology reported herein not only aids the categorizing of lexical items according to their conceptual domains but also can establish links between these items. Such bottom-up and top-down analyses of lexical items may provide a means of representing metaphorical entries in lexical resources.

言語教育研究においては、学習者と教師、および学習者間の口頭コミュニケーション活動についての重要性がこれまでも指摘されてきたが、日本の英語教育においても、授業に口頭コミュニケーションを取り入れる必要性が徐々に認識されつつある。本研究では、真正性の高い有意味な言語活動を促進するために作られたタスクの基準（e.g., Ellis, 2003; Ellis & Shintani, 2014）を用いて、中学校教科書に含まれる口頭コミュニケーションを志向する活動がどのような基準に合致しているかを分析した。そしてその結果をもとに、中学校教科書に含まれている活動をそのまま用いることによって、学習者の言語スキル向上に対してどのような結果が期待できるか、またはできないかについて、第二言語習得研究の研究結果を参照しながら考察した。そして教科書に掲載されている活動の多くは、そのまま用いると自発的に発話内容を言語化するプロセスを学習者が経験したり、言語習得上有意義な意味交渉が起こったりすることが期待できないことを示唆した。 In the field of language teaching research, the importance of meaningful interactions and oral communication activities has been pointed out repeatedly. In English language teaching in Japan, this importance has also been recognized by some teachers, although gradually. In this study we analyzed 3 textbooks used in Japanese junior high schools, referring to task criteria (e.g., Ellis, 2003; Ellis & Shintani, 2014) that were developed for the purpose of promoting authentic meaningful communication. There were 4 task criteria: (a) the focus is on meaning, (b) there is a gap, (c) the learners rely on their own linguistic or nonlinguistic resources, and (d) learners’ language use is not used to assess achievement. We examined whether or not the oral-communication-oriented activities in the textbooks met these criteria. The textbook analysis indicated that the majority of the activities presented did not meet the task criteria. Among the four criteria, (c)—the learners rely on their own resources—was met the least. In most of the cases, linguistic resources such as conversation examples and lexical items were provided for the students, and the only thing the students needed to do was to use those resources. On the other hand, almost half of the activities met (b)—there is a gap—and this was the most easily satisfied criterion. We gave careful consideration to what kind of learner language proficiency development can be expected if classroom teachers use these communication-oriented activities as they appear in the textbook. In doing so, we considered the results obtained from previous SLA research. The fact that most of the activities in the textbooks did not meet the task criteria means that, if they are not modified appropriately, they would prevent language learners from engaging in voluntary grammatical encoding and negotiation of meaning. For example, as most of the activities did not meet criteria (c), the students can hardly experience grammatical encoding because they do not need to think about what linguistic form they should use to convey the meaning. Also, the fact that the focus of the task was not on meaning would result in a serious lack of meaningful negotiation, and therefore the students would miss precious opportunities to get comprehensible input through negotiation of meaning. In sum, the activities presented in the textbooks we analyzed were not enough to guarantee that the students would participate in negotiation of meaning and experience necessary cognitive processing during speaking, both of which are the essence of SLA. We do not propose that the activities should not be used or that they are useless. Rather, we believe that it is worthwhile to think of the communication-oriented activities with task criteria in mind in order to ensure the development of learners’ language proficiency. In addition, teachers should modify the activities to enable the students to focus on meaning and to communicate using their own resources. The results of this study provide useful insights for teachers who want to make their classes more communicative and to have the students engage in meaningful conversation.

In light of recent work on ‘scale structure’, this paper provides a new perspective on event framing in Japanese and Chinese. It has a focus on motion as well as result as sub-domains of event representation. Lexical resources, such as verb compounds, serial verb constructions (SVC) and open / closed-scale adjectival predicates (APs) are revisited. It is postulated that different lexical resources present distinct event framing patterns. Japanese change-of-state (COS) events show sensitivity to the scalar structure of APs, i.e. closed-scale APs give rise to satellite framing and open-scale APs invite verb framing. The distribution of Chinese motion events denoted by SVCs is not arbitrary but restricted i.e. the order of motion morphemes must be [open / closed - scale change morpheme + closed-scale change morpheme], meaning the second constituent has to be closed-scale morpheme. This study argues that the distinct event framing patterns intralinguistic and crosslinguistic are based on the diversity of lexical resources of motion/resultative event framing and preferences for event-encoding options by selecting different linguistic resources.

Introduction. The ability to automate processes is a key aspect of modern information technology. The construction and use of the conceptual structure of the knowledge base is becoming an urgent need in the modern world, where the amount of information is growing exponentially. The ability to automate processes, including the construction of ontologies, which requires the extraction of knowledge from full-text sources and their automatic structuring, is important. Knowledge bases are used to manage complex dynamic systems by ensuring the storage, organization, and access to a large amount of information that allows for effective analysis and prediction of the behavior of such systems. The purpose of the paper. The purpose of the paper is to demonstrate the effectiveness of using deep learning methods to automate the formation of the conceptual structure of the knowledge base. The study also aims to show how the integration of knowledge bases with deep learning methods can improve the quality of forecasts and increase the efficiency of rehabilitation trajectory management. Results. The algorithm successfully extracted and processed symptom information from the medical cases, effectively handling duplicates and synonyms. The utilization of cosine similarity enabled the identification of synonymous symptoms within the established knowledge base, facilitating the seamless integration of new information while preventing redundancy. The system demonstrated its capability to discern which symptoms should be incorporated into the knowledge base and which should be omitted based on their similarity to existing entries. The outcomes underscore the potential of this automated approach to enhance the knowledge base and contribute to the refinement of predictive models within the healthcare domain. Conclusions. The study demonstrated the effectiveness of deep learning in automating the formation of the conceptual structure of a medical knowledge base. The approach enhances the filling and comprehensiveness of the knowledge base, which is crucial for building predictive models for patient trajectories and improving healthcare decision support. Keywords: Knowledge-Oriented Management Systems, knowledge base, Support Vector Machine, Word2Vec, Skip-Gram, BioBERT.

We aim to automatically generate natural language descriptions about an input structured knowledge base (KB). We build our generation framework based on a pointer network which can copy facts from the input KB, and add two attention mechanisms: (i) slot-aware attention to capture the association between a slot type and its corresponding slot value; and (ii) a new \emph{table position self-attention} to capture the inter-dependencies among related slots. For evaluation, besides standard metrics including BLEU, METEOR, and ROUGE, we propose a KB reconstruction based metric by extracting a KB from the generation output and comparing it with the input KB. We also create a new data set which includes 106,216 pairs of structured KBs and their corresponding natural language descriptions for two distinct entity types. Experiments show that our approach significantly outperforms state-of-the-art methods. The reconstructed KB achieves 68.8% - 72.6% F-score.

This paper addresses a novel task of semantically analyzing the comparative constructions inherent in attributive superlative expressions against structured knowledge bases (KBs). The task can be defined in two-fold: first, selecting the comparison dimension against a KB, on which the involved items are compared; and second, determining the ranking order, in which the items are ranked (ascending or descending). We exploit Wikipedia and Freebase to collect training data in an unsupervised manner, where a neural network model is then learnt to select, from Freebase predicates, the most appropriate comparison dimension for a given superlative expression, and further determine its ranking order heuristically. Experimental results show that it is possible to learn from coarsely obtained training data to semantically characterize the comparative constructions involved in attributive superlative expressions.

Multi-goal multi-agent learning for task-oriented dialogue with bidirectional teacher–student learning

In today's world, it is no longer enough for a company to have a better product or service than its competitors to survive and grow. A customer-obsessed attitude is required for businesses to survive and grow. As we all know, competitors can typically quickly duplicate any new market position and even do it better than the organization that started the idea. The more business knowledge your team members have about the products and services your consumers use, the more successful your company will be. You, as an organization, should be swift in responding to your customers' demands to react to the competitive changes in the market. One of the key tools needed for a quick response to this effect is the Knowledge Base System (KBS). This tool can be an internal tool for your employees or an external tool for your customers. This will support the decision-making process, information sharing, products, services, etc. Most organizations have this tool but are not well structured. There is no single correct way to build a knowledge base, but there are multiple methods, each with its own set of advantages and quirks. However, if you follow some basic guidelines, you can be sure that your customers or employees will not get lost in the process. The most basic content format in the knowledge base is an article with text. However, it can include screenshots, photos, videos, audio, and infographics. We can further implement a knowledge-based system with artificial intelligence (AI), which gives room for more productivity in an organization. One thing that constitutes or destroys your knowledge base is its structure. Just like a dictionary won't serve its purpose unless it’s organized alphabetically, a cluttered or disorganized knowledge base will confuse your customers and your employees rather than lead them to a solution. You can convert knowledge base articles into FAQs, product manuals, troubleshooting guides, etc. A knowledge-based system might be a game changer for your organization if you want to make your clients happy. In this article, I will walk through the objectives, scope, strategy, and all you should keep in mind when you're building a knowledge base system for your organization. Keywords: Artificial intelligence (AI), knowledge, information, decision-making, and organizational DOI: 10.7176/IKM/13-1-03 Publication date: January 31 st 2023

DBpedia has been proved to be a successful structured knowledge base, and large scale Semantic Web data has been built by using DBpedia as the central interlinking-hubs of the Web of Data in English. But in Chinese, due to the heavily imbalance in size (no more than one tenth) between English and Chinese in Wikipedia, there are few Chinese linked data are published and linked to DBpedia, which hinders the structured knowledge sharing both within Chinese resources and cross-lingual resources. This paper aims at building large scale Chinese structured knowledge base from Hudong, which is one of the largest Chinese Wiki Encyclopedia websites. In this paper, an upper-level ontology schema in Chinese is first learned based on the category system and Infobox information in Hudong. Totally, there are 19542 concepts are inferred, which are organized in hierarchy with maximally 20 levels. 2381 properties with domain and range information are learned according to the attributes in the Hudong Infoboxes. Then, 802593 instances are extracted and described using the concepts and properties in the learned ontology. These extracted instances cover a wide range of things, including persons, organizations, places and so on. Among all the instances, 62679 of them are linked to identical instances in DBpedia. Moreover, the paper provides RDF dump or SPARQL to access the established Chinese knowledge base. The general upper-level ontology and wide coverage makes the knowledge base a valuable Chinese semantic resource. It not only can be used in Chinese linked data building, the fundamental work for building multi lingual knowledge base across heterogeneous resources of different languages, but also can largely facilitate many useful applications of large-scale knowledge base such as knowledge question-answering and semantic search.

Whereas research on recombination indicates that the structure of the firm’s knowledge base has an impact on the usefulness of its innovation, it emphasizes that the usefulness of innovation increases when firms’ search spans organizational boundaries. Current literature is limited however in providing insight in how a firm should organize its boundary-spanning search for recombination, given the specific characteristics of its knowledge base. Delineating a continuum of knowledge structures from highly decomposable to non-decomposable, we contribute to extant literature by demonstrating that the knowledge base and in particular its structure also impacts the organizational governance choice for collaborative recombination. In particular we demonstrate on a sample of 108 Biotech firms engaged in 968 contractual agreements and 152 joint ventures with 928 partners that firms with a decomposable knowledge base are more likely to engage in equity joint ventures for collaboration. Furthermore, we demonstrate that when the partner has a decomposable knowledge base, a joint venture is also more likely to be the chosen mode of collaboration. Results imply that the structure of the knowledge base is not only important for the innovation potential of the firm, but also impacts the governance choices firms make for collaborative recombination.