ABSTRACT. Indigenous peoples and local communities interact with approximately two-thirds of the world's land area through their worldviews and customary tenure regimes and offer significant knowledge contributions and lessons about sustainability. We worked with Tuawhenua Māori to document domains, concepts, and mechanisms within the worldview representation in a way that could guide environmental conservation in New Zealand. We then applied the framework to a cultural keystone species for Tuawhenua, the kererū ([New Zealand pigeon [(Hemiphaga novaeseelandiae]) to elucidate this human-environment relationship. Whakapapa (genealogy), whenua (land), and tangata (people) were interconnected domains that formed the conceptual basis of our framework. Within these domains, the concepts of mauri (life essence), mana (authority), and ihi (vitality) guided the expression of the community's relationship with the environment. Cultural expressions related to the kererū demonstrated the cultural significance of the bird to Tuawhenua that went well beyond the ecological and intrinsic value of the species. The Tuawhenua worldview representation also emphasized the humannature relationship and the role that metaphor plays in expressing this relationship. Indigenous peoples and local community worldviews are important for establishing priorities, reconciling the human relationship with the environment, and facilitating the coproduction of knowledge in response to pressing local and global environmental conservation issues.

With 13,000,000 volumes comprising 4.5 billion pages of text, it is currently very difficult for scholars to locate relevant sets of documents that are useful in their research from the HathiTrust Digital Libary (HTDL) using traditional lexically-based retrieval techniques. Existing document search tools and document clustering approaches use purely lexical analysis, which cannot address the inherent ambiguity of natural language. A semantic search approach offers the potential to overcome the shortcoming of lexical search, but-even if an appropriate network of ontologies could be decided upon-it would require a full semantic markup of each document. In this paper, we present a conceptual design and report on the initial implementation of a new framework that affords the benefits of semantic search while minimizing the problems associated with applying existing semantic analysis at scale. Our approach avoids the need for complete semantic document markup using pre-existing ontologies by developing an automatically generated Concept-in-Context (CiC) network seeded by a priori analysis of Wikipedia texts and identification of semantic metadata. Our Capisco system analyzes documents by the semantics and context of their content. The disambiguation of search queries is done interactively, to fully utilize the domain knowledge of the scholar. Our method achieves a form of semantic-enhanced search that simultaneously exploits the proven scale benefits provided by lexical indexing.

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