Weisen Guo

A System for Ontology-Based Sharing of Expert Knowledge in Sustainability Science

Work towards creation of a knowledge sharing system for sustainability science through the application of semantic data modeling is described. An ontology grounded in description logics was developed based on the ISO 15926 data model to describe three types of sustainability science conceptualizations: situational knowledge, analytic methods, and scenario frameworks. Semantic statements were then created using this ontology to describe expert knowledge expressed in research proposals and papers related to sustainability science and in scenarios for achieving sustainable societies. Semantic matching based on logic and rule-based inference was used to quantify the conceptual overlap of semantic statements, which shows the semantic similarity of topics studied by different researchers in sustainability science, similarities that might be unknown to the researchers themselves.

A Random Network Generator with Finely Tunable Clustering Coefficient for Small-World Social Networks

Many social networks share two generic distinct features: power law distributions of degrees and a high clustering. In some cases, it is difficult to obtain the structure information of real networks. Network generators provide a way to generate test networks for simulation. We present a random network generator to generate test networks with prescribed power law distributions of degrees and a finely tunable average clustering coefficient. The generator is composed of three steps. First, the degree sequences are generated following the given degree power law exponents. Second, the generator constructs a test network with these degree sequences. Third, the test network is modified to meet the prescribed average clustering coefficient as closely as possible. Experiments show the impact of the clustering coefficient on network connectivity using this generator. The comparison with existing random network generators is presented.

Explicit scientific knowledge comparison based on semantic description matching

Researchers begin new research by acquiring pre-existing explicit scientific knowledge that is potentially relevant to the research subject. In order to find some potentially relevant explicit scientific knowledge items, such as knowledge whose content is similar to the targeted research, a researcher must examine the semantics of each item. In this paper, after reviewing related work, an automated semantic description matching-based approach is presented for comparing items of explicit scientific knowledge. This approach obtains a matching score between semantic descriptions of two items of explicit scientific knowledge that indicates their similarity. Three dimensions are considered in this approach: matching granularity, similarity scale for instance classes, and logic similarity scale. In order to match two semantic descriptions, a six-step method is presented: creation of atomic queries, generalization of query classes, generalization of query properties, addition of rules, creation of instances implied by complex class definition, and semi-automatic pruning of matching results. Finally, some conclusions regarding the approach are presented together with plans for future work.

Inferring Trust from Recommendations in Web-Based Knowledge Sharing Systems

Conventional web-based systems for knowledge sharing cannot help users determine the reliability of an unknown knowledge source on the web. This paper introduces an approach for using the concepts of trust and recommendation from social networks in web-based knowledge sharing systems. A simulation study of three algorithms for calculating trust inferred from recommendations on a social network is presented. The results show that our proposed algorithms can calculate inferred trust values for over 99% of the entities in the network with a high degree of accuracy. Finally, a prototype MAS system that uses trust and recommendation in knowledge sharing is described.

Extracting Relationship Associations from Semantic Graphs in Life Sciences

The rate of literature publication in life sciences is growing fast, and researchers in the bioinformatics and knowledge discovery fields have been studying how to use the existing literature to discover novel knowledge or generate novel hypothesis. Existing literature-based discovery methods and tools use text-mining techniques to extract non-specified relationships between two concepts. This paper presents a new approach to literature-based discovery, which adopts semantic web techniques to measure the relevance between two relationships with specified types that involve a particular entity. We extract pairs of highly relevant relationships, which we call relationship associations, from semantic graphs representing scientific papers. These relationship associations can be used to help researchers generate scientific hypotheses or create their own semantic graphs for their papers. We present the results of experiments for extracting relationship associations from 392 semantic graphs representing MEDLINE papers.

Using human authored description logics ABoxes as concept models for natural language generation

Papers written by researchers in medical and life sciences are a valuable source of information even for non-experts looking for knowledge related to rare diseases, but only if those non-experts can read English. If researchers create descriptors of their papers in the form of description logics (DL) ABoxes (assertion components) according to a DL ontology, then by using currently available software, computers can reason over the ABoxes to infer semantic consequences of the assertions in the descriptor. One open issue is how best to render information contained in the ABox for a particular user based on that users knowledge requirements and background knowledge, including language preference. Natural language generation (NLG) is a method for rendering computer-interpretable statements, content models, in human-readable form, natural language text. ABoxes could be used as content models for NLG with particularly rich semantics. In particular, ABoxes could be used to generate expressions of expert knowledge in languages different from the original language that are more accurate and more tailor-fit to the users cognitive state than existing methods for translating scientific papers. A method for generating natural language expressions from ABoxes in English and Japanese is presented and compared with a state-of-the-art expert translation software package.

CROSS-LANGUAGE KNOWLEDGE SHARING MODEL BASED ON ONTOLOGIES AND LOGICAL INFERENCE

AbstractVast amounts of new knowledge are created on the Internet in many different languages every day. How to share and search this knowledge across different languages efficiently is a critical problem for information science and knowledge management. Conventional cross-language knowledge sharing models are based on natural language processing (NLP) technologies. However, natural language ambiguity, which is a problem even for single language NLP. is exacerbated when dealing with multiple languages. Semantic web technologies can circumvent the problem of natural language ambiguity by enabling human authors to specify meaning in a computer-interpretable form. In particular, description logics ontologies provide a way for authors to describe specific relationships between conceptual entities in a way that computers can process to infer implied meaning. This paper presents a new cross-language knowledge sharing model, SEMCL, which uses semantic web technologies to provide a potential solution to the problem of ambiguity. We first describe the methods used to support searches at the semantic predicate level in our model. Next, we describe how our model realizes a cross-language approach. We present an implementation of the model for the general engineering domain and give a scenario describing how the model implementation handles semantic cross-language knowledge sharing. We conclude with a discussion of related work.

Mining relationship associations from knowledge about failures using ontology and inference

Mining general knowledge about relationships between concepts described in the analyses of failure cases could help people to avoid repeating previous failures. Furthermore, by representing knowledge using ontologies that support inference, we can identify relationships between concepts more effectively than text-mining techniques. A relationship association is a form of knowledge generalization that is based on binary relationships between entities in semantic graphs. Specifically, relationship associations involve two binary relationships that share a connecting entity and that co-occur frequently in a set of semantic graphs. Such connected relationships can be considered as generalized knowledge mined from a set of knowledge resources, such as failure case descriptions, that are formally represented by the semantic graphs. This paper presents the application of a technique to mine relationship associations from formalized semantic descriptions of failure cases. Results of mining relationship associations in a knowledge base containing 291 semantic graphs representing failure cases are presented.

Enriching city entities in the EKOSS failure cases knowledge base with Linked Open Data

Repositories of semantic statements that are created manually can contain detailed domain-specific knowledge with “heavy-weight” semantics that support intelligent computer processing services. However, such repositories often lack common knowledge since the authors of semantic statements often leave out details that human readers would already know. Some of this common knowledge can be encoded in ontologies that provide computers with background facts for a particular domain. However, the scalability of this approach is limited. We describe a stepwise procedure to enrich a repository of user contributed semantic statements with a set of standard entity statements that provide information about commonly known entities in the domain. We apply the procedure to extract common knowledge about city entities from Linked Open Data sources into standard entity statements that we use to enrich the semantic statements in the EKOSS failure cases knowledge base, which contains knowledge about failures in engineering. We then present a qualitative evaluation through a scenario, which demonstrates that using this simple procedure to enrich domain-specific semantic statements with common knowledge makes it possible to answer interesting search queries that would otherwise not get any results.

SEMCL: A Cross-Language Semantic Model for Knowledge Sharing

To promote global knowledge sharing, one should solve the problem that knowledge representation in diverse natural languages restricts knowledge sharing effectively. Traditional knowledge sharing models are based on natural language processing NLP technologies. The ambiguity of natural language is a problem for NLP; however, semantic web technologies can circumvent the problem by enabling human authors to specify meaning in a computer-interpretable form. In this paper, the authors propose a cross-language semantic model SEMCL for knowledge sharing, which uses semantic web technologies to provide a potential solution to the problem of ambiguity. Also, this model can match knowledge descriptions in diverse languages. First, the methods used to support searches at the semantic predicate level are given, and the authors present a cross-language approach. Finally, an implementation of the model for the general engineering domain is discussed, and a scenario describing how the model implementation handles semantic cross-language knowledge sharing is given.