A Unified Framework for N-ary Property Information Extraction in Materials Science (2025)

Abstract: This paper presents a unified framework for extracting n-ary property information from materials science literature, addressing the critical challenge of capturing complex relationships that often span multiple sentences. We introduce three complementary approaches: RE-Composition, which transforms binary relations into n-ary structures; Direct EAE, which models polymer properties as events with multiple arguments; and LLM-Guided Assembly, which leverages high-confidence entity and relation outputs to guide structured extraction. Our framework is built upon two novel resources: MatSciNERE, a comprehensive corpus for materials science entities and relations, and PolyEE, a specialized corpus for polymer property events. Through strategic synthetic data generation for both NER and EAE tasks, we achieve significant performance improvements (up to 5.34 F1 points). Experiments demonstrate that our combined approaches outperform any single method, with the LLM-guided approach achieving the highest F1 score (71.53%). The framework enables more comprehensive knowledge extraction from scientific literature, supporting materials discovery and database curation applications. We plan to release our resources and trained models to the research community.

## introduction materials science encompasses diverse entities ranging from broad categories like organic and inorganic materials to specialized subcategories such as polymers. the exponential growth of scientific publications in this field creates significant challenges for researchers attempting to efficiently access and organize critical information. particularly crucial is the extraction of property information, defined as structured knowledge about materials and their properties, measured values, experimental conditions, and characterization methods. this information is essential for materials discovery, database curation, and accelerating research. extracting property information from materials science literature presents unique challenges compared to traditional information extraction (ie) tasks. property information typically involves complex n-ary relationships connecting multiple entities (e.g., a polymer with a specific property value under certain conditions, measured by a particular method). these relationships frequently span multiple sentences, requiring cross-sentence reasoning. additionally, specialized terminology, diverse experimental contexts, and varied writing styles further complicate automated extraction. previous materials information extraction approaches have focused primarily on named entity recognition (ner) to identify materials @xcite or binary relation extraction (re) between entity pairs @xcite . while promising for specific subtasks, these methods fail to capture the n-ary property information essential for comprehensive materials knowledge bases. recent event argument extraction (eae) advancements offer pathways for modeling n-ary relationships, but their application to materials science remains limited. large language models (llms) have demonstrated capabilities in extracting structured information, yet their performance on specialized scientific content is inconsistent without appropriate guidance @xcite . in this work, we introduce a unified framework for n-ary property ie in materials science that integrates three complementary approaches: (1) re-composition, which transforms binary relationship predictions into n-ary structures; (2) direct eae, which models property information as events with property names as triggers and other elements as arguments; and (3) llm-guided assembly, a novel hybrid approach that leverages high-confidence entity and relation predictions to guide llms in generating complete n-ary structures. the main contributions of this paper are: our unified framework addresses the critical need for structured knowledge extraction from materials science literature, offering a robust solution that can adapt to different extraction scenarios. we first present matscinere, a high-quality corpus representing a significant advancement for materials science ie. this resource expands the polynere corpus @xcite with a key innovation: a revised annotation assumption that captures all entity mentions in the text, not just those directly relevant to polymers, enhancing both coverage and consistency. following the approach in similar work @xcite and widely used datasets like matscholar @xcite , a single annotator conducted the primary annotation work to ensure consistency across the corpus, building upon the extensive foundation of existing high-quality entity and relation annotations. a quality assessment was performed where a polymer expert independently annotated a sample set from the corpus, yielding a cohen's kappa coefficient of 0.835 and comparative metrics of 95.88% precision, 79.42% recall, and 86.93% f1 score. this validation confirms the high quality of the annotations. matscinere contains 22,296 entity mentions and 11,935 relation pairs, increasing from 18,930 entities and 11,471 relations in polynere corpus. with 14 entity types and 8 relation types, it includes overlapped (15.65%) and discontinuous (1.26%) mentions crucial for capturing complex scientific expressions, enabling practical extraction systems. detailed statistics are in appendix a. in this work, we also introduce polyee, a specialized corpus for event-based polymer property extraction that addresses a critical gap in materials science ie. polyee reformulates property information as events with property names as triggers and other elements as arguments, enabling the extraction of complete property tuples across multiple sentences. we define our task based on concepts from polyinfo @xcite , the largest polymer database. we focus on five key entity types: polymer (polymer names like "polyethylene"), prop_name (property names such as "glass transition temperature"), prop_value (values with units like "25 mpa"), condition (measurement conditions like "at 25°c"), and char_method (characterization techniques such as "dsc"). these five types represent the core elements needed for polymer property information in polyinfo's schema, which will also serve as the primary knowledge source for developing synthetic data later in this work. our event structure follows a single "propertyinfo" type with prop_name as the trigger and other types as arguments. we developed polyee through a semiautomated approach using two advanced llms (gpt-4o and claude 3.7 sonnet thinking) to generate initial event annotations based on existing entity and relation annotations from matscinere. this dual-llm approach allowed us to crossvalidate annotations and identify potential discrepancies. out of 750 abstracts in matscinere, 503 contain at least one relevant event with a prop_name trigger and the required polymer and prop_value arguments. when events contained multiple conditions or char_methods, we split them into separate instances. this process yielded 1,601 distinct events reflecting various property measurements and methods. to ensure high-quality annotations, we implemented a thorough validation process. event tuples generated by the two llms were manually compared, with 118 events (7.37%) across 86 abstracts (17.10%) undergoing detailed verification. additionally, all annotations in development and test sets were manually reviewed. to assess annotation quality, we compared llmgenerated annotations with those produced by the primary corpus annotator on a sample of 10 abstracts containing the highest number of events. this comparison yielded a cohen's kappa of 0.87, suggesting strong consistency in the annotation process. detailed statistics are in appendix b. ## unified framework for property ie our unified framework integrates three complementary approaches for extracting n-ary property information from materials science literature. as shown in figure 1 , the architecture centers on a foundational ner module that processes scientific text to identify relevant entities. the framework then offers multiple approaches to transform these entities into structured n-ary property information: (1) re-composition, (2) direct eae, and (3) llm-guided assembly. each approach has distinct strengths, and they can be deployed individually or in combination depending on specific extraction needs. two synthetic data generation engines enhance both the ner module and eae models, improving overall performance. ## conclusion we presented a unified framework for n-ary property ie in materials science integrating three approaches: re-composition, direct eae, and llm-guided assembly. built upon four distinct corpora, two standard (matscinere and polyee) and two synthetic (ner-focused and eaefocused), our framework shows synthetic data enhances model performance across components, with optimal gains at moderate volumes. experimental results confirm different extraction scenarios benefit from different approaches, with the llm-guided approach achieving highest performance when constrained, while specialized models offer competitive results with deployment advantages. our work addresses a critical need by advancing structured property ie in materials science. future work will expand to additional scientific domains, improve pathway integration, and explore techniques to reduce llm hallucination in scientific contexts.